From ignasi.sols at nyu.edu Thu Nov 1 04:06:54 2018 From: ignasi.sols at nyu.edu (Ignasi Sols) Date: Wed, 31 Oct 2018 23:06:54 -0400 Subject: [FieldTrip] Brain-shift compensation Error In-Reply-To: References: Message-ID: Many thanks, Arjen. I could solve the problem following your suggestion. It was an issue with the format of the channel information, that was not recognized by* ft_channelselection.* Best, Ignasi On Wed, Oct 31, 2018 at 2:26 AM Arjen Stolk wrote: > Hi Ignasi, > > One can only guess based on that error msg alone. You might want to put a > debug marker at line 156, check whether coord0 is truly empty, and then try > to trace back to what's causing it to be empty (e.g., an empty elecpos > field in your elec structure?). > > Arjen > > On Tue, Oct 30, 2018 at 12:04 PM Ignasi Sols wrote: > >> Dear all, >> I'm following the method developed by Stolk et al (2018) to localize the >> electrodes of ECoG data. >> I'm getting this error on step 23 (Project the electrode grids to the >> surface hull of the implanted hemisphere) and I can't solve it. Could >> anyone help me with this? >> >> Thanks, >> Ignasi >> >> >> *using electrodes specified in the configuration* >> *using warp algorithm described in Dykstra et al. 2012 Neuroimage PMID: >> 22155045* >> *creating electrode pairs based on electrode positions* >> *Error using fmincon (line 241)* >> *You must provide a non-empty starting* >> *point.* >> *Error in warp_dykstra2012 (line 156)* >> *coord_snapped = fmincon(efun, coord0,* >> *[], [], [], [], [], [], cfun,* >> *options);* >> *Error in ft_electroderealign (line* >> *406)* >> * norm.elecpos =* >> warp_dykstra2012(cfg, elec, >> headshape); >> >> -- >> Ignasi Sols >> Postdoctoral Fellow >> Department of Psychology >> New York University >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> >> https://doi.org/10.1371/journal.pcbi.1002202 >> >> > _______________________________________________ > fieldtrip mailing list > > https://urldefense.proofpoint.com/v2/url?u=https-3A__mailman.science.ru.nl_mailman_listinfo_fieldtrip&d=DwIGaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=MfcAKxrNJUDchBMH7cGf8A7flR7i0Mg4wa1BIfRAxTM&m=ogxF67dx9cUcJr32WF9DDK-sz4FQ20js2rkptNpxMBA&s=Jx9wYcv0XnbORxGzfpEH0gqB1QyHZMaw-4hs4evzORE&e= > > https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1371_journal.pcbi.1002202&d=DwIGaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=MfcAKxrNJUDchBMH7cGf8A7flR7i0Mg4wa1BIfRAxTM&m=ogxF67dx9cUcJr32WF9DDK-sz4FQ20js2rkptNpxMBA&s=9nWIVPM4Xf2SQkk3fMSCzXvE82mHFH-pnn67Fhygfyk&e= > -- Ignasi Sols Postdoctoral Fellow Department of Psychology New York University -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Thu Nov 1 08:51:05 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 1 Nov 2018 07:51:05 +0000 Subject: [FieldTrip] Phase Information For PCC Beamformer In-Reply-To: <74e003128d5a0219ae6febed2c6ff119@inserm.fr> References: <74e003128d5a0219ae6febed2c6ff119@inserm.fr> Message-ID: Hi Hesham, If memory serves me well, but you may want to verify this in the code of ft_specest_mtmfft, the phase estimate is expressed relative to t=0 (considering the epoch’s time axis that is passed on to ft_specest_mtmfft). Best wishes, Jan-Mathijs > On 30 Oct 2018, at 17:05, Hesham ElShafei wrote: > > Hello Fieldtrippers! > > So I am trying to do some whole brain connectivity analysis according to this tutorial: http://www.fieldtriptoolbox.org/tutorial/networkanalysis > > All is going fine :) I just would like to understand how the phase information is obtained using these options in ft_freqanalysis > > cfg.method = 'mtmfft'; > cfg.output = 'fourier'; > > In other words , how is time incorporated in the computed phase values? > In other other words, these phase values represent the signal at which time point? > > hope I was clear enough > > Cheers! > Hesham > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From jan.schoffelen at donders.ru.nl Thu Nov 1 09:13:34 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 1 Nov 2018 08:13:34 +0000 Subject: [FieldTrip] Problem using ft_sensrorealign with Yokogawa MEG In-Reply-To: References: Message-ID: <684D7E73-1417-44EB-A75B-DE1A83864556@donders.ru.nl> Hi Victor, You don’t mention which fieldtrip version you are currently using, but ft_sensorrealign has been moved to compat/obsolete about a year ago. This means that this particular function is not actively maintained and supported anymore. At the moment, I don’t remember the reason for this, but it would mean that there is functionality in the main fieldtrip functions that got rid of the raison d’etre of ft_sensorrealign. As is mentioned in the README of the compat/obsolete directory, you should move ft_sensorrealign up a few directories. The reason for this is that the function might rely on low-level functions that are located in fieldtrip/private, which are only visible from function that are located in the directory that contains the private folder. ‘fixpos’ might be one of them. Best wishes, Jan-Mathijs J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands On 30 Oct 2018, at 10:43, Victor RG > wrote: HI Fieldtrip experts! I'm trying to align a headmodel (the one from example "Subject1") with my MEG sensors (Yokogawa system). I'm trying to do that interactively, in order to construct a Leadfied matrix as accurate as possible. To do that, I am testing this code: cfg = []; cfg.method = 'interactive'; cfg.headshape = vol.bnd(1); cfg.senstype = 'meggrad'; grad_aligned = ft_sensorrealign(cfg, grad); % Im using ft_sensorrealign cause I think is the MEG version of ft_electroderealign The variables employed consist of: >> grad = struct with fields: * balance: [1×1 struct] * chanori: [160×3 double] * chanpos: [160×3 double] * chantype: {160×1 cell} * chanunit: {160×1 cell} * coilori: [320×3 double] * coilpos: [320×3 double] * label: {160×1 cell} * tra: [160×320 double] * type: 'yokogawa160' * unit: 'cm' * fid: [1×1 struct] >> vol.bnd(1) = struct with fields: * pos: [1000×3 double] * tri: [1996×3 double] * coordsys: 'ctf' I have looked at the tutorial for EEG sensors realignment, and I have copied the procedure, since there is not a specific tutorial for MEG sensors. I thought it would be the same, but when executing it I obtain the following errors: >> Undefined function 'fixpos' for input arguments of type 'struct'. Error in ft_sensorrealign (line 255) headshape = fixpos(cfg.headshape); Error in generating_leadfield (line 63) grad_aligned = ft_sensorrealign(cfg, grad); Does anybody know how to do that, or how to do an interactive realignment with MEG sensors? Thanks in advance. Víctor. Víctor Rodríguez González Grupo de Ingeniería Biomédica, ETSIT. Universidad de Valladolid, España. _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Thu Nov 1 09:23:11 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 1 Nov 2018 08:23:11 +0000 Subject: [FieldTrip] ft_mergealgin: high residual variance? In-Reply-To: References: Message-ID: <863F96C2-45E4-4441-B330-6C0292880D72@donders.ru.nl> Hi Maria, Are you sure about the units in your headmodel (and gradiometers)? Using the value 1.0 as an inwardshift parameter suggests ‘cm’. Not sure what will happen when by accident the hs_file is in ‘m’ (which, as far as I know, is usually the case). Best wishes, Jan-Mathijs J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands On 29 Oct 2018, at 13:33, Maria Hakonen > wrote: Dear FieldTrip experts, I have run ft_mergealign across subjects to align the head positions. However, the residual variance between the original and the realigned data seems to be high: original -> template RV 21232.46 % original -> original RV 36.96 % original -> template -> original RV 9579.95 % Could someone please let me know what would be the largest acceptable change in the residual variance, and what should I do if the residual variance is too high? Does the increase in residual variance mean that there is a large shift in the head position? I have used ft_mergealign as follows: template = list of subjects (i.e. I want to calculate an average head position over the subjects) grad = data.grad; hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); vol = ft_headmodel_localspheres(hs,grad); cfg = []; cfg.template = template; cfg.inwardshift = 1.0; cfg.vol = vol; data_aligned = ft_megrealign(cfg, data); Best, Maria _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From C.Mazzetti at donders.ru.nl Thu Nov 1 12:04:23 2018 From: C.Mazzetti at donders.ru.nl (Mazzetti, C. (Cecilia)) Date: Thu, 1 Nov 2018 11:04:23 +0000 Subject: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage Message-ID: <1541070268890.93169@donders.ru.nl> Dear everyone, I have some stimulus locked data which I have to baseline with respect to the same data but cue locked. I was wondering whether there is a setting in fieldtrip which allows to do that (i.e. set the baseline timw window according to a different dataset, the cue-locked data) and applying it to the stimulus locked data. Thanks in advance! Best, Cecilia Cecilia Mazzetti - Ph.D. Candidate Donders Centre for Cognitive Neuroimaging, room 0.068 Kapittelweg 29 | 6525 EN Nijmegen -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Thu Nov 1 21:07:08 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Thu, 1 Nov 2018 16:07:08 -0400 Subject: [FieldTrip] Post hoc test following significant interaction Message-ID: Dear FieldTrip community, I have a 2 x 2 mixed study design (within factor being pre post, and between factor being group 1 and group 2) in which I am hoping to use an ANOVA to analyze. Since I have a 2 x 2 factorial design, I have been following the tutorial: -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Thu Nov 1 21:21:38 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Thu, 1 Nov 2018 16:21:38 -0400 Subject: [FieldTrip] Post hoc test following significant interaction Message-ID: Dear FieldTrip community, I have a 2 x 2 mixed study design (within factor being pre post, and between factor being group 1 and group 2) in which I am hoping to use an ANOVA in the cluster based permutation framework to analyze. Since I have a 2 x 2 factorial design, I have been following the tutorial: http://www.fieldtriptoolbox.org/faq/how_can_i_test_an_interaction_effect_using_cluster-based_permutation_tests as well as various previous responses on the mailing list. In the context that I have an interaction in certain electrodes at certain time points, my questions are: 1. How would I go about testing the simple effects to disentangle the interaction. Would it simply be a case of skipping the main effects and running the appropriate indep/dep t-test between each 4 of the possible contrasts for simple effects? (i.e. group 1 vs group 2 at pre, group 1 vs group 2 at post, group 1 at pre vs group 1 at post, group 2 at pre vs group 2 at post). 2). If an interaction is initially found, should the post-hoc (e.g. simple main effects) be limited to the electrodes and time in which the interactions are present, or would the simple effect contrasts be run across all electrodes and time points as were run in the initial interaction test. Any insight would be greatly appreciated. Thank you, Paul -------------- next part -------------- An HTML attachment was scrubbed... URL: From pascualm at key.uzh.ch Fri Nov 2 02:36:01 2018 From: pascualm at key.uzh.ch (pascualm at key.uzh.ch) Date: Fri, 2 Nov 2018 10:36:01 +0900 Subject: [FieldTrip] Comparison of measures of electrophysiological connectivity Message-ID: Dear Colleagues, The preprint entitled: "A comparison of bivariate frequency domain measures of electrophysiological connectivity" at: https://doi.org/10.1101/459503 might be of interest to those performing research related to electrophysiological connectivity inference. The abstract can be found below. Cordially, Roberto ... Roberto D. Pascual-Marqui, PhD, PD The KEY Institute for Brain-Mind Research, University of Zurich Visiting Professor at Neuropsychiatry, Kansai Medical University, Osaka [https://www.uzh.ch/keyinst/loreta] [scholar.google.com/citations?user=pascualmarqui] ... Abstract: The problem of interest here concerns electrophysiological signals from two cortical sites, acquired as invasive intracranial recordings, or from non-invasive estimates of cortical electric neuronal activity computed from EEG or MEG recordings (see e.g. https://doi.org/10.1101/269753). In the absence of other sources, these measured signals consist of an instantaneous linear mixture of the true, actual, unobserved local signals, due to low spatial resolution and volume conduction. A connectivity measure is unreliable as a true indicator of electrophysiological connectivity if it is not invariant to mixing, or if it reports a significant connection for a mixture of independent signals. In (Vinck et al 2011 Neuroimage 55:1548) it was shown that coherence, imaginary coherence, and phase locking value are not invariant to mixing, while the phase lag index (PLI) and the weighted version (wPLI) are invariant to mixing. Here we show that the lagged coherence (LagCoh) measure (2007, https://arxiv.org/abs/0711.1455), not studied in Vinck et al, is invariant to mixing. Additionally, we present here a new mixture-invariant connectivity statistic: the "standardized imaginary covariance" (sImCov). We also include in the comparisons the directed PLI (dPLI) by Stam et al (2012 Neuroimage 62:1415). Fourier coefficients for "N" trials are generated from a linear unidirectional causal time domain model with electrophysiological delay "k" and regression coefficient "b". 1000 random data sets of "N" trials are simulated, and for each one, and for each connectivity measure, non-parametric randomization tests are performed. The "true positive detection rate" is calculated as the fraction of 1000 cases that have significant connectivity at p<0.05, 0.1, and 0.2. The connectivity methods were compared in terms of detection rates, under non-mixed and mixed conditions, for small and large sample sizes "N", with and without jitter, and for different values of signal to noise. Under mixing, the results show that LagCoh outperforms wPLI, PLI, dPLI, and sImCov. Without mixing, LagCoh and sImCov outperform wPLI, PLI, and dPLI. Finally, it is shown that dPLI is an invalid estimator of flow direction, i.e. it reverses and "goes against the flow" by merely changing the sign of one of the time series, a fact that violates the basic definition of Granger causality. From maria.hakonen at gmail.com Fri Nov 2 07:37:44 2018 From: maria.hakonen at gmail.com (Maria Hakonen) Date: Fri, 2 Nov 2018 08:37:44 +0200 Subject: [FieldTrip] ft_mergealgin: high residual variance? In-Reply-To: <863F96C2-45E4-4441-B330-6C0292880D72@donders.ru.nl> References: <863F96C2-45E4-4441-B330-6C0292880D72@donders.ru.nl> Message-ID: Hi Jan-Mathias! Thank you for the answer! I changed the units of gradiometers and head model to cm. This clearly decreased the residual variances. Also, singleshell seems to work better than localspheres. However, the transformation seems to still increase the residual variance a lot. Here are some examples: original -> template RV 407.64 % original -> original RV 9.85 % original -> template -> original RV 10.75 % realigning trial 706 original -> template RV 393.13 % original -> original RV 9.00 % original -> template -> original RV 9.90 % realigning trial 707 original -> template RV 362.10 % original -> original RV 8.33 % original -> template -> original RV 9.18 % realigning trial 708 original -> template RV 377.15 % original -> original RV 9.43 % original -> template -> original RV 10.33 % The code is now as follows: load([data_path nameList{subj} '.mat']); grad = datafinal.grad; hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); cfg = []; cfg.method = 'singlesphere'; cfg.geom = hs; cfg.grad = grad; cfg.feedback = true; vol = ft_prepare_headmodel(cfg); vol = ft_convert_units(vol,'cm'); grad = ft_convert_units(grad,'cm'); cfg = []; cfg.template = template; cfg.inwardshift = 2.5; cfg.feedback ='no'; cfg.vol = vol; data = ft_megrealign(cfg, data); Best, Maria to 1. marrask. 2018 klo 10.35 Schoffelen, J.M. (Jan Mathijs) ( jan.schoffelen at donders.ru.nl) kirjoitti: > Hi Maria, > > Are you sure about the units in your headmodel (and gradiometers)? Using > the value 1.0 as an inwardshift parameter suggests ‘cm’. Not sure what will > happen when by accident the hs_file is in ‘m’ (which, as far as I know, is > usually the case). > > Best wishes, > > Jan-Mathijs > > J.M.Schoffelen, MD PhD > Senior Researcher, VIDI-fellow - PI, language in interaction > Telephone: +31-24-3614793 > Physical location: room 00.028 > Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands > > > > > On 29 Oct 2018, at 13:33, Maria Hakonen wrote: > > Dear FieldTrip experts, > > I have run ft_mergealign across subjects to align the head positions. > However, the residual variance between the original and the realigned data > seems to be high: > > original -> template RV 21232.46 % > original -> original RV 36.96 % > original -> template -> original RV 9579.95 % > > Could someone please let me know what would be the largest acceptable > change in the residual variance, and what should I do if the residual > variance is too high? Does the increase in residual variance mean that > there is a large shift in the head position? > > I have used ft_mergealign as follows: > > template = list of subjects (i.e. I want to calculate an average head > position over the subjects) > > grad = data.grad; > hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); > vol = ft_headmodel_localspheres(hs,grad); > > cfg = []; > cfg.template = template; > cfg.inwardshift = 1.0; > cfg.vol = vol; > data_aligned = ft_megrealign(cfg, data); > > Best, > Maria > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From tom.marshall at psy.ox.ac.uk Fri Nov 2 23:29:10 2018 From: tom.marshall at psy.ox.ac.uk (Tom Marshall) Date: Fri, 2 Nov 2018 22:29:10 +0000 Subject: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage In-Reply-To: <1541070268890.93169@donders.ru.nl> References: <1541070268890.93169@donders.ru.nl> Message-ID: Hey Cecilia, I guess you could do that with a combination of 'ft_selectdata', and 'ft_math'. Let's say you have datasets called 'stimlocked' and 'cuelocked' You'd do something like: % cut out baseline from cuelocked cfg = []; cfg.latency = [-0.5 0]; % or whatever your baseline window is baseline = ft_selectdata(cfg, cuelocked); % subtract baseline from stimlocked cfg = []; cfg.operation = 'subtract'; baseline_corrected_stimlocked = ft_math(cfg, stimlocked, baseline); I think that ft_math allows you to input algebraic expressions too. So if you wanted to do, for example, a 'relchange' baseline correction you should substitute (something like) cfg.operation = '(x1-x2)/x2'; Best, Tom ________________________________ From: fieldtrip on behalf of Mazzetti, C. (Cecilia) Sent: 01 November 2018 11:04:23 To: fieldtrip at science.ru.nl Subject: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage Dear everyone, I have some stimulus locked data which I have to baseline with respect to the same data but cue locked. I was wondering whether there is a setting in fieldtrip which allows to do that (i.e. set the baseline timw window according to a different dataset, the cue-locked data) and applying it to the stimulus locked data. Thanks in advance! Best, Cecilia Cecilia Mazzetti - Ph.D. Candidate Donders Centre for Cognitive Neuroimaging, room 0.068 Kapittelweg 29 | 6525 EN Nijmegen -------------- next part -------------- An HTML attachment was scrubbed... URL: From silvana.silva at upf.edu Mon Nov 5 16:14:35 2018 From: silvana.silva at upf.edu (SILVA PEREIRA, SILVANA) Date: Mon, 5 Nov 2018 16:14:35 +0100 Subject: [FieldTrip] ICA inspection: Error using ft_icabrowser.m Message-ID: Dear all, I'm trying to use the funtion ft_icabrowser.m, but I get the following error message: Error using topoplot_common (line 523) labels in data and labels in layout do not match Error in ft_topoplotIC (line 184) [cfg] = topoplot_common(cfg, comp); Error in ft_icabrowser (line 151) ft_topoplotIC(cfgtopo, comp); I understand that the error is due to a mismatch between the labels of the cfg struct and the layout I'm using. I modified the entries of the struct in acticap-64ch-standard2.mat, where I removed four of the channels, since we have in addition RM, LM, Heog and Veog. Adding these four labels to the original label list does not solve the problem. Any idea to work around this issue? Thank you! Silvana *Silvana Silva Pereira* /Postdoctoral Researcher/ Center for Brain and Cognition [image: Universitat Pompeu Fabra, Barcelona] -------------- next part -------------- An HTML attachment was scrubbed... URL: From dlozanosoldevilla at gmail.com Mon Nov 5 16:54:24 2018 From: dlozanosoldevilla at gmail.com (Diego Lozano-Soldevilla) Date: Mon, 5 Nov 2018 16:54:24 +0100 Subject: [FieldTrip] ICA inspection: Error using ft_icabrowser.m In-Reply-To: References: Message-ID: Hi Silvana, Could you try the component viewmode option? cfg = []; cfg.layout = 'acticap-64ch-standard'; cfg.viewmode = 'component'; % Mode specifically suited to browse through ICA data ft_databrowser(cfg, comp); Best, Diego On Mon, Nov 5, 2018, 16:38 SILVA PEREIRA, SILVANA Dear all, > > I'm trying to use the funtion ft_icabrowser.m, but I get the following > error message: > > Error using topoplot_common (line 523) > labels in data and labels in layout do not match > > Error in ft_topoplotIC (line 184) > [cfg] = topoplot_common(cfg, comp); > > Error in ft_icabrowser (line 151) > ft_topoplotIC(cfgtopo, comp); > > I understand that the error is due to a mismatch between the labels of the > cfg struct and the layout I'm using. I modified the entries of the struct > in acticap-64ch-standard2.mat, where I removed four of the channels, since > we have in addition RM, LM, Heog and Veog. Adding these four labels to the > original label list does not solve the problem. Any idea to work around > this issue? > > Thank you! > Silvana > > > *Silvana Silva Pereira* > /Postdoctoral Researcher/ > Center for Brain and Cognition > [image: Universitat Pompeu Fabra, Barcelona] > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Mon Nov 5 17:21:05 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Mon, 5 Nov 2018 17:21:05 +0100 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra Message-ID: Fellow FieldTrippers, I have identified two points of interest in the brain, between which I want to compute connectivity metrics. One is in early visual cortex, the other is in right temporal lobe, somewhat medial (compatible with hippocampus, but exact interpretation not relevant right now). I reconstructed activity for these grid points using LCMV beamformer (on MEG data) and computed source-level Fourier spectra (taper = 'dpss', tapsmofrq = 3) after applying a band-stop filter around 50 Hz and harmonics. Using ft_connectivityanalysis, I computed both debiased wPLI and Granger causality between the two points of interest. In both spectra, I see a clear peak at 60 Hz in the grand average across 36 subjects, which is also there in the majority of individual subjects (though very strong only in 2/36). Plots are attached. Now this would be exciting news if indeed it turns out to be a true highly band-limited gamma effect! But of course I suspect that this peak could very well be artifactual. I can't think of any artifact source I may have missed that would cause this, though. The projector refresh rate during the experiment was set to 120 Hz, and not 60. (Also note this is European data so AC frequency is 50 Hz and not 60, hence the band stop I mentioned earlier.) Does anyone have any idea what might be causing this sharp peak? (A genuine effect after all?) Thanks, Eelke -------------- next part -------------- A non-text attachment was scrubbed... Name: granger-spectrum.png Type: image/png Size: 14990 bytes Desc: not available URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: wpli-spectrum.png Type: image/png Size: 11765 bytes Desc: not available URL: From jan.schoffelen at donders.ru.nl Mon Nov 5 18:13:05 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Mon, 5 Nov 2018 17:13:05 +0000 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra In-Reply-To: References: Message-ID: This looks like an artifact to me. In addition, the zigzag in the Granger spectra is suggestive of convergence issues in the non-parametric spectral matrix factorization in at least a few of your subjects. This is often caused by strong discontinuities in the spectra (spectral lines!) or poor behaviour of the time domain data at the edges of your epochs, causing spectral leakage. JM > On 5 Nov 2018, at 17:21, Eelke Spaak wrote: > > Fellow FieldTrippers, > > I have identified two points of interest in the brain, between which I > want to compute connectivity metrics. One is in early visual cortex, > the other is in right temporal lobe, somewhat medial (compatible with > hippocampus, but exact interpretation not relevant right now). I > reconstructed activity for these grid points using LCMV beamformer (on > MEG data) and computed source-level Fourier spectra (taper = 'dpss', > tapsmofrq = 3) after applying a band-stop filter around 50 Hz and > harmonics. Using ft_connectivityanalysis, I computed both debiased > wPLI and Granger causality between the two points of interest. > > In both spectra, I see a clear peak at 60 Hz in the grand average > across 36 subjects, which is also there in the majority of individual > subjects (though very strong only in 2/36). Plots are attached. Now > this would be exciting news if indeed it turns out to be a true highly > band-limited gamma effect! > > But of course I suspect that this peak could very well be artifactual. > I can't think of any artifact source I may have missed that would > cause this, though. The projector refresh rate during the experiment > was set to 120 Hz, and not 60. (Also note this is European data so AC > frequency is 50 Hz and not 60, hence the band stop I mentioned > earlier.) > > Does anyone have any idea what might be causing this sharp peak? (A > genuine effect after all?) > > Thanks, > Eelke > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From silvana.silva at upf.edu Mon Nov 5 18:23:06 2018 From: silvana.silva at upf.edu (SILVA PEREIRA, SILVANA) Date: Mon, 5 Nov 2018 18:23:06 +0100 Subject: [FieldTrip] ICA inspection: Error using ft_icabrowser.m In-Reply-To: References: Message-ID: Hi Diego, Your suggestion works fine for ft_databrowser, but not for ft_icabrowser! best regards, Silvana El lun., 5 nov. 2018 a las 16:58, Diego Lozano-Soldevilla (< dlozanosoldevilla at gmail.com>) escribió: > Hi Silvana, > Could you try the component viewmode option? > > cfg = []; > cfg.layout = 'acticap-64ch-standard'; > cfg.viewmode = 'component'; % Mode specifically suited to browse through ICA data > ft_databrowser(cfg, comp); > > Best, > Diego > > > On Mon, Nov 5, 2018, 16:38 SILVA PEREIRA, SILVANA wrote: > >> Dear all, >> >> I'm trying to use the funtion ft_icabrowser.m, but I get the following >> error message: >> >> Error using topoplot_common (line 523) >> labels in data and labels in layout do not match >> >> Error in ft_topoplotIC (line 184) >> [cfg] = topoplot_common(cfg, comp); >> >> Error in ft_icabrowser (line 151) >> ft_topoplotIC(cfgtopo, comp); >> >> I understand that the error is due to a mismatch between the labels of >> the cfg struct and the layout I'm using. I modified the entries of the >> struct in acticap-64ch-standard2.mat, where I removed four of the channels, >> since we have in addition RM, LM, Heog and Veog. Adding these four labels >> to the original label list does not solve the problem. Any idea to work >> around this issue? >> >> Thank you! >> Silvana >> >> >> *Silvana Silva Pereira* >> /Postdoctoral Researcher/ >> Center for Brain and Cognition >> >> [image: Universitat Pompeu Fabra, Barcelona] >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From rdm146 at newark.rutgers.edu Mon Nov 5 18:26:35 2018 From: rdm146 at newark.rutgers.edu (Ravi Mill) Date: Mon, 5 Nov 2018 17:26:35 +0000 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> References: , <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> Message-ID: Hi Carsten and Johannes Many thanks for responding, and for developing these great tools! I'm in the process of acquiring a large diffusion MR dataset from which I can hopefully create an 'averaged' atlas. From your responses I think I have a sense of how to integrate the conductivity tensors derived from this atlas with the Fieldtrip FEM pipeline. But I was wondering if you had any advice on how to compute these conductivity tensors in the first place? From the paper that Carsten sent, It seems like the FDT program within FSL is what I need to compute diffusion tensors from the raw diffusion images (steps 1-6 from the FDT user guide https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Processing_pipeline)? Seemingly, these diffusion tensors need to then be converted to conductivity tensors - any advice on how to do this (or if you could point me to some example code) would be greatly appreciated. Thanks Ravi FDT/UserGuide - FslWiki - University of Oxford fsl.fmrib.ox.ac.uk Eddy Current Correction. Eddy currents in the gradient coils induce (approximate) stretches and shears in the diffusion weighted images. These distortions are different for different gradient directions. Thanks again Ravi ________________________________ From: fieldtrip on behalf of Johannes Vorwerk Sent: Monday, October 29, 2018 10:14:16 AM To: FieldTrip discussion list Subject: Re: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio Dear Ravi, as Carsten already said, calculating FEM with anisotropic conductivities is not directly supported by the FieldTrip-SimBio implementation. However, if you are willing to invest a bit of time it is possible to work around this. The „only“ thing that needs to be changed is the calculation of the FEM stiffness matrix, which is performed by the routine „calc_stiffness_matrix_val“ in the function sb_calc_stiff (usually called from ft_prepare_headmodel). The problem is that FieldTrip does not support anisotropic conductivities, so that you would have to call calc_stiffness_matrix_val directly. You can see the correct call in sb_calc_stiff. For anisotropic conductivities you have to replace the input „cond“ by a #elements x 6 matrix containing your anisotropic conductivities in the format "xx yy zz xy yz zx“. If you now follow the normal FieldTrip-SimBio workflow using the resulting stiffness matrix, you will get results for anisotropic conductivities. Best, Johannes Am 29.10.2018 um 12:31 schrieb Carsten Wolters >: Dear Ravi, 1) You can use the pure SimBio-code from https://www.mrt.uni-jena.de/simbio/index.php/Main_Page to treat WM anisotropy. While it would in principle also be possible to use anisotropic conductivities with FieldTrip-SimBio, this is currently not implemented using ft_prepare_headmodel. Johannes (in CC), who implemented Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: "Depending on your matlab skills and your available time, I could help you to give it a try though. It should be possible with using some direct function calls instead of the high-level fieldtrip-functions." 2) We recommend http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf on individual data. I could imagine that an atlas does a reasonable job w.r.t. the main bigger fiber tracts such as corpus callosum or pyramidal tracts, but that the finer details in the cortices are individual. We always measure T1, T2 and DTI from each subject and I personally do not have experience with such a group-level anisotropy compared to the individual one. Might be interesting to hear from others what they think!? BR Carsten Am 25.10.18 um 23:05 schrieb Ravi Mill: Dear Fieldtrippers I have applied the FEM simbio head modeling pipeline implemented in Fieldtrip to my EEG data. My understanding is that this pipeline assumes isotropic conductivities for 5 head compartments (as specified by cfg.conductivity in ft_prepare_headmodel). After reading some papers (e.g. Vorwerk et al 2014 https://doi.org/10.1016/j.neuroimage.2014.06.040), it seems like incorporating white matter conductivity anisotropy has a relatively small albeit significant effect on the source solution. I am interested in comparing FEM results when treating white matter as anisotropic. My questions are as follows: 1. Is there a way to implement the FEM simbio head model whilst treating WM as anisotropic within Fieldtrip? If so, how would one do this (or are there any resources available that demonstrate this)? 2. From previous papers and some simbio documentation (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples) it seems like diffusion MRI data is required to calculate the WM conductivity for each individual subject. I only have T1 and T2 scans for my subjects. So would it be possible to use WM anisotropic information obtained from some kind of diffusion MRI group average/atlas instead (accepting some loss in subject-level precision)? If so, does such a group average/atlas exist? Any help would be greatly appreciated! Thanks Ravi _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -- Prof. Dr.rer.nat. Carsten H. Wolters University of Münster Institute for Biomagnetism and Biosignalanalysis Malmedyweg 15 48149 Münster, Germany Phone: +49 (0)251 83 56904 +49 (0)251 83 56865 (secr.) Fax: +49 (0)251 83 56874 Email: carsten.wolters at uni-muenster.de Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From ekenaykut at gmail.com Mon Nov 5 18:41:35 2018 From: ekenaykut at gmail.com (Aykut Eken) Date: Mon, 5 Nov 2018 18:41:35 +0100 Subject: [FieldTrip] ICA inspection: Error using ft_icabrowser.m In-Reply-To: References: Message-ID: Hi Silvana, Can you check the match_str function? [seldat, sellay] = match_str(label, cfg.layout.label); if isempty(seldat) ft_error('labels in data and labels in layout do not match'); end Best Aykut > On 5 Nov 2018, at 18:23, SILVA PEREIRA, SILVANA wrote: > > > Hi Diego, > > Your suggestion works fine for ft_databrowser, but not for ft_icabrowser! > > best regards, > Silvana > > > El lun., 5 nov. 2018 a las 16:58, Diego Lozano-Soldevilla (>) escribió: > Hi Silvana, > Could you try the component viewmode option? > > cfg = []; > cfg.layout = 'acticap-64ch-standard'; > cfg.viewmode = 'component'; % Mode specifically suited to browse through ICA data > ft_databrowser(cfg, comp); > Best, > Diego > > > On Mon, Nov 5, 2018, 16:38 SILVA PEREIRA, SILVANA wrote: > Dear all, > > I'm trying to use the funtion ft_icabrowser.m, but I get the following error message: > > Error using topoplot_common (line 523) > labels in data and labels in layout do not match > > Error in ft_topoplotIC (line 184) > [cfg] = topoplot_common(cfg, comp); > > Error in ft_icabrowser (line 151) > ft_topoplotIC(cfgtopo, comp); > > I understand that the error is due to a mismatch between the labels of the cfg struct and the layout I'm using. I modified the entries of the struct in acticap-64ch-standard2.mat, where I removed four of the channels, since we have in addition RM, LM, Heog and Veog. Adding these four labels to the original label list does not solve the problem. Any idea to work around this issue? > > Thank you! > Silvana > > > Silvana Silva Pereira > /Postdoctoral Researcher/ > Center for Brain and Cognition > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From carsten.wolters at uni-muenster.de Tue Nov 6 12:10:36 2018 From: carsten.wolters at uni-muenster.de (Carsten Wolters) Date: Tue, 6 Nov 2018 12:10:36 +0100 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: References: <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> Message-ID: Hi Ravi, Marios (in CC) promised to send you the short Matlab-script that we use to transform the diffusion tensors to conductivity tensors using Tuch's effective medium approach. For this approach, please check e.g. the subsection "Calibrated Finite Element Head Model and Forward Solution" in https://link.springer.com/article/10.1007/s10548-017-0568-9 BR    Carsten Am 05.11.18 um 18:26 schrieb Ravi Mill: > > Hi Carsten and Johannes > > > Many thanks for responding, and for developing these great tools! > > > I'm in the process of acquiring a large diffusion MR dataset from > which I can hopefully create an 'averaged' atlas. From your > responses I think I have a sense of how to integrate the conductivity > tensors derived from this atlas with the Fieldtrip FEM pipeline. > > > But I was wondering if you had any advice on how to compute > these conductivity tensors in the first place? From the paper that > Carsten sent, It seems like the FDT program within FSL is what I need > to compute diffusion tensors from the raw diffusion images (steps 1-6 > from the FDT user guide > https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Processing_pipeline)? > Seemingly, these diffusion tensors need to then be converted to > conductivity tensors - any advice on how to do this (or if you could > point me to some example code) would be greatly appreciated. > > > Thanks > > Ravi > > > FDT/UserGuide - FslWiki - University of Oxford > > fsl.fmrib.ox.ac.uk > Eddy Current Correction. Eddy currents in the gradient coils induce > (approximate) stretches and shears in the diffusion weighted images. > These distortions are different for different gradient directions. > > > > Thanks again > Ravi > > > ------------------------------------------------------------------------ > *From:* fieldtrip on behalf of > Johannes Vorwerk > *Sent:* Monday, October 29, 2018 10:14:16 AM > *To:* FieldTrip discussion list > *Subject:* Re: [FieldTrip] Incorporating White Matter conductivity > anisotropy into FEM simbio > Dear Ravi, > > as Carsten already said, calculating FEM with anisotropic > conductivities is not directly supported by the FieldTrip-SimBio > implementation. However, if you are willing to invest a bit of time it > is possible to work around this. > > The „only“ thing that needs to be changed is the calculation of the > FEM stiffness matrix, which is performed by the routine > „calc_stiffness_matrix_val“ in the function sb_calc_stiff (usually > called from ft_prepare_headmodel). The problem is that FieldTrip does > not support anisotropic conductivities, so that you would have to call > calc_stiffness_matrix_val directly. You can see the correct call in > sb_calc_stiff. For anisotropic conductivities you have to replace the > input „cond“ by a #elements x 6 matrix containing your anisotropic > conductivities in the format "xx yy zz xy yz zx“. If you now follow > the normal FieldTrip-SimBio workflow using the resulting stiffness > matrix, you will get results for anisotropic conductivities. > > Best, > Johannes > >> Am 29.10.2018 um 12:31 schrieb Carsten Wolters >> > >: >> >> Dear Ravi, >> >> 1) You can use the pure SimBio-code from >> https://www.mrt.uni-jena.de/simbio/index.php/Main_Page >> >> to treat WM anisotropy. >> While it would in principle also be possible to use anisotropic >> conductivities with FieldTrip-SimBio, >> this is currently not implemented using ft_prepare_headmodel. >> Johannes (in CC), who implemented >> Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: >> "Depending on your matlab skills and your available time, I could >> help you to give it a >> try though. It should be possible with using some direct function >> calls instead of the high-level fieldtrip-functions." >> >> 2) We recommend >> http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf >> >> on individual data. I could imagine that an atlas does a reasonable >> job w.r.t. the main >> bigger fiber tracts such as corpus callosum or pyramidal tracts, but >> that the finer details >> in the cortices are individual. We always measure T1, T2 and DTI from >> each subject >> and I personally do not have experience with such a group-level >> anisotropy compared >> to the individual one. Might be interesting to hear from others what >> they think!? >> >> BR >>    Carsten >> >> >> >> Am 25.10.18 um 23:05 schrieb Ravi Mill: >>> Dear Fieldtrippers >>> >>> I have applied the FEM simbio head modeling pipeline implemented >>> in Fieldtrip to my EEG data. My understanding is that this pipeline >>> assumes isotropic conductivities for 5 head compartments (as >>> specified by cfg.conductivity in ft_prepare_headmodel). After >>> reading some papers (e.g. Vorwerk et al 2014 >>> https://doi.org/10.1016/j.neuroimage.2014.06.040 >>> ), >>> it seems like incorporating white matter conductivity anisotropy has >>> a relatively small albeit significant effect on the source solution. >>> I am interested in comparing FEM results when treating white matter >>> as anisotropic. My questions are as follows: >>> >>> 1. Is there a way to implement the FEM simbio head model whilst >>> treating WM as anisotropic within Fieldtrip? If so, how would >>> one do this (or are there any resources available that >>> demonstrate this)? >>> 2. From previous papers and some simbio documentation >>> (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples >>> ) >>> it seems like diffusion MRI data is required to calculate the WM >>> conductivity for each individual subject. I only have T1 and T2 >>> scans for my subjects. So would it be possible to use WM >>> anisotropic information obtained from some kind of diffusion >>> MRI group average/atlas instead (accepting some loss in >>> subject-level precision)? If so, does such a group average/atlas >>> exist? >>> >>> >>> Any help would be greatly appreciated! >>> >>> Thanks >>> Ravi >>> >>> >>> _______________________________________________ >>> fieldtrip mailing list >>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >>> https://doi.org/10.1371/journal.pcbi.1002202 >> >> >> -- >> Prof. Dr.rer.nat. Carsten H. Wolters >> University of Münster >> Institute for Biomagnetism and Biosignalanalysis >> Malmedyweg 15 >> 48149 Münster, Germany >> >> Phone: >> +49 (0)251 83 56904 >> +49 (0)251 83 56865 (secr.) >> >> Fax: >> +49 (0)251 83 56874 >> >> Email:carsten.wolters at uni-muenster.de >> Web:https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -- Prof. Dr.rer.nat. Carsten H. Wolters University of Münster Institute for Biomagnetism and Biosignalanalysis Malmedyweg 15 48149 Münster, Germany Phone: +49 (0)251 83 56904 +49 (0)251 83 56865 (secr.) Fax: +49 (0)251 83 56874 Email: carsten.wolters at uni-muenster.de Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From antoine.ducorps at orange.fr Tue Nov 6 12:44:17 2018 From: antoine.ducorps at orange.fr (Antoine Ducorps) Date: Tue, 6 Nov 2018 12:44:17 +0100 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger(fieldtrip Digest, Vol 96, Issue 4) In-Reply-To: References: Message-ID: <1D61E3DF-EEDF-473C-BF56-EA9A1E1772BC@orange.fr> Dear Eelke, I agree that a sharp 60 Hz peak is certainly an artefact. You mention that your projector is set to 120 Hz, but unless you use a very sophisticated one, the internal rate for most (if not all) commercial devices is still 60 Hz, and any input rate will be interpolated to that fundamental. In your case, every second frame would be displayed, or an average light value of two frames, I am not sure. You can check that on your technical manual, or alternating black/white frames and measuring the light output with a photodiode. If it really happens that your projector runs internally at 60 Hz, then you should also set your computer output at the same value, otherwise you don’t know what the interpolation or down sampling will do, most probably not documented by the vendor. Then, either you have a shielding problem with your projector electronics, or the 60 Hz rate is a real light effect in your stimulus, reflected in the visual cortex. Best, Antoine. > ------------------------------ > > Message: 3 > Date: Mon, 5 Nov 2018 17:21:05 +0100 > From: Eelke Spaak > To: FieldTrip discussion list > Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger > spectra > Message-ID: > > Content-Type: text/plain; charset="utf-8" > > Fellow FieldTrippers, > > I have identified two points of interest in the brain, between which I > want to compute connectivity metrics. One is in early visual cortex, > the other is in right temporal lobe, somewhat medial (compatible with > hippocampus, but exact interpretation not relevant right now). I > reconstructed activity for these grid points using LCMV beamformer (on > MEG data) and computed source-level Fourier spectra (taper = 'dpss', > tapsmofrq = 3) after applying a band-stop filter around 50 Hz and > harmonics. Using ft_connectivityanalysis, I computed both debiased > wPLI and Granger causality between the two points of interest. > > In both spectra, I see a clear peak at 60 Hz in the grand average > across 36 subjects, which is also there in the majority of individual > subjects (though very strong only in 2/36). Plots are attached. Now > this would be exciting news if indeed it turns out to be a true highly > band-limited gamma effect! > > But of course I suspect that this peak could very well be artifactual. > I can't think of any artifact source I may have missed that would > cause this, though. The projector refresh rate during the experiment > was set to 120 Hz, and not 60. (Also note this is European data so AC > frequency is 50 Hz and not 60, hence the band stop I mentioned > earlier.) > > Does anyone have any idea what might be causing this sharp peak? (A > genuine effect after all?) > > Thanks, > Eelke > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: granger-spectrum.png > Type: image/png > Size: 14990 bytes > Desc: not available > URL: > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: wpli-spectrum.png > Type: image/png > Size: 11765 bytes > Desc: not available > URL: > > ------------------------------ > > Message: 4 > Date: Mon, 5 Nov 2018 17:13:05 +0000 > From: "Schoffelen, J.M. (Jan Mathijs)" > To: FieldTrip discussion list > Subject: Re: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and > Granger spectra > Message-ID: > Content-Type: text/plain; charset="us-ascii" > > This looks like an artifact to me. > > In addition, the zigzag in the Granger spectra is suggestive of convergence issues in the non-parametric spectral matrix factorization in at least a few of your subjects. This is often caused by strong discontinuities in the spectra (spectral lines!) or poor behaviour of the time domain data at the edges of your epochs, causing spectral leakage. > > JM > > From antonakakismar at gmail.com Tue Nov 6 14:21:56 2018 From: antonakakismar at gmail.com (Marios Antonakakis) Date: Tue, 6 Nov 2018 14:21:56 +0100 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: References: <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> Message-ID: Hi Ravi, assuming that you work under Linux and you have already the DTI data registered to MRI, you need then to run the below script. *%%1, calculate conductivity tensor for every voxel* % mri_segmented_final, V1, ... L1 variables have ft MRI structure %skin,skull compacta, skull spongiosa, CSF, GM, WM conductivity = [0.4300, 0.0024, 0.0086, 1.7900, 0.3300, 0.1400]; cfg =[]; cfg.compartments = mri_segmented_final.anatomylabel; % anatomical labels, skin: 0, skull spongiosa 2 and show on... cfg.conductivity = conductivity; [condcell, s, fail] = sb_calcTensorCond_tuch(cfg,mri_segmented_final,V1,V2,V3,L1,L2,L3); *%% 2. assign the conductivity tensor with the hex mesh * [mesh.nodes,mesh.elements,mesh.labels]= read_vista_mesh('foo_mesh.v); mask = mri_segmented_final; mask.anatomy((mask.anatomy~=5) & (mask.anatomy~=6))=0; mask.anatomy((mask.anatomy==5) | (mask.anatomy==6))=1; tensors = sb_assiTensorCond(mask,mesh.nodes,mesh.elements,mesh.labels,condcell); write_vista_mesh('foo_mesh_aniso.v',mesh.nodes,mesh.elements,mesh.labels,tensors'); Do not hesitate to ask further questions. **Note that write_vista_mesh and read_vista_mesh are private ft functions.* Best regards, Marios ᐧ Στις Τρί, 6 Νοε 2018 στις 12:10 μ.μ., ο/η Carsten Wolters < carsten.wolters at uni-muenster.de> έγραψε: > Hi Ravi, > > Marios (in CC) promised to send you the short Matlab-script that we use to > transform the diffusion tensors > to conductivity tensors using Tuch's effective medium approach. > > For this approach, please check e.g. the subsection > "Calibrated Finite Element Head Model and Forward Solution" > in > https://link.springer.com/article/10.1007/s10548-017-0568-9 > > BR > Carsten > > Am 05.11.18 um 18:26 schrieb Ravi Mill: > > Hi Carsten and Johannes > > > Many thanks for responding, and for developing these great tools! > > > I'm in the process of acquiring a large diffusion MR dataset from which I > can hopefully create an 'averaged' atlas. From your responses I think I > have a sense of how to integrate the conductivity tensors derived from this > atlas with the Fieldtrip FEM pipeline. > > > But I was wondering if you had any advice on how to compute > these conductivity tensors in the first place? From the paper that Carsten > sent, It seems like the FDT program within FSL is what I need to compute > diffusion tensors from the raw diffusion images (steps 1-6 from the FDT > user guide > https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Processing_pipeline)? > Seemingly, these diffusion tensors need to then be converted to > conductivity tensors - any advice on how to do this (or if you could point > me to some example code) would be greatly appreciated. > > > Thanks > > Ravi > > > FDT/UserGuide - FslWiki - University of Oxford > > fsl.fmrib.ox.ac.uk > Eddy Current Correction. Eddy currents in the gradient coils induce > (approximate) stretches and shears in the diffusion weighted images. These > distortions are different for different gradient directions. > > > Thanks again > Ravi > > > ------------------------------ > *From:* fieldtrip > on behalf of Johannes Vorwerk > > *Sent:* Monday, October 29, 2018 10:14:16 AM > *To:* FieldTrip discussion list > *Subject:* Re: [FieldTrip] Incorporating White Matter conductivity > anisotropy into FEM simbio > > Dear Ravi, > > as Carsten already said, calculating FEM with anisotropic conductivities > is not directly supported by the FieldTrip-SimBio implementation. However, > if you are willing to invest a bit of time it is possible to work around > this. > > The „only“ thing that needs to be changed is the calculation of the FEM > stiffness matrix, which is performed by the routine > „calc_stiffness_matrix_val“ in the function sb_calc_stiff (usually called > from ft_prepare_headmodel). The problem is that FieldTrip does not support > anisotropic conductivities, so that you would have to call > calc_stiffness_matrix_val directly. You can see the correct call in > sb_calc_stiff. For anisotropic conductivities you have to replace the input > „cond“ by a #elements x 6 matrix containing your anisotropic conductivities > in the format "xx yy zz xy yz zx“. If you now follow the normal > FieldTrip-SimBio workflow using the resulting stiffness matrix, you will > get results for anisotropic conductivities. > > Best, > Johannes > > Am 29.10.2018 um 12:31 schrieb Carsten Wolters < > carsten.wolters at uni-muenster.de>: > > Dear Ravi, > > 1) You can use the pure SimBio-code from > https://www.mrt.uni-jena.de/simbio/index.php/Main_Page > > to treat WM anisotropy. > While it would in principle also be possible to use anisotropic > conductivities with FieldTrip-SimBio, > this is currently not implemented using ft_prepare_headmodel. Johannes (in > CC), who implemented > Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: > "Depending on your matlab skills and your available time, I could help you > to give it a > try though. It should be possible with using some direct function calls > instead of the high-level fieldtrip-functions." > > 2) We recommend > > http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf > > on individual data. I could imagine that an atlas does a reasonable job > w.r.t. the main > bigger fiber tracts such as corpus callosum or pyramidal tracts, but that > the finer details > in the cortices are individual. We always measure T1, T2 and DTI from each > subject > and I personally do not have experience with such a group-level anisotropy > compared > to the individual one. Might be interesting to hear from others what they > think!? > > BR > Carsten > > > > Am 25.10.18 um 23:05 schrieb Ravi Mill: > > Dear Fieldtrippers > > I have applied the FEM simbio head modeling pipeline implemented > in Fieldtrip to my EEG data. My understanding is that this pipeline > assumes isotropic conductivities for 5 head compartments (as specified by > cfg.conductivity in ft_prepare_headmodel). After reading some papers > (e.g. Vorwerk et al 2014 https://doi.org/10.1016/j.neuroimage.2014.06.040 > ), > it seems like incorporating white matter conductivity anisotropy has a > relatively small albeit significant effect on the source solution. I am > interested in comparing FEM results when treating white matter as > anisotropic. My questions are as follows: > > > 1. Is there a way to implement the FEM simbio head model whilst > treating WM as anisotropic within Fieldtrip? If so, how would one do this > (or are there any resources available that demonstrate this)? > 2. From previous papers and some simbio documentation ( > https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples > ) > it seems like diffusion MRI data is required to calculate the WM > conductivity for each individual subject. I only have T1 and T2 scans for > my subjects. So would it be possible to use WM anisotropic information > obtained from some kind of diffusion MRI group average/atlas instead > (accepting some loss in subject-level precision)? If so, does such a group > average/atlas exist? > > > Any help would be greatly appreciated! > > Thanks > Ravi > > > > _______________________________________________ > fieldtrip mailing listhttps://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 > > > > -- > Prof. Dr.rer.nat. Carsten H. Wolters > University of Münster > Institute for Biomagnetism and Biosignalanalysis > Malmedyweg 15 > 48149 Münster, Germany > > Phone: > +49 (0)251 83 56904 > +49 (0)251 83 56865 (secr.) > > Fax: > +49 (0)251 83 56874 > > Email: carsten.wolters at uni-muenster.de > Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ > > > > _______________________________________________ > fieldtrip mailing listhttps://mailman.science.ru.nl/mailman/listinfo/fieldtriphttps://doi.org/10.1371/journal.pcbi.1002202 > > > > -- > Prof. Dr.rer.nat. Carsten H. Wolters > University of Münster > Institute for Biomagnetism and Biosignalanalysis > Malmedyweg 15 > 48149 Münster, Germany > > Phone: > +49 (0)251 83 56904 > +49 (0)251 83 56865 (secr.) > > Fax: > +49 (0)251 83 56874 > > Email: carsten.wolters at uni-muenster.de > Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ > > -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- function condtensor = sb_assiTensorCond(mask,nodes,elem,labels,condcell) %% condtensor = zeros(9,size(elem,1)); count = 0; for i = 1 : size(elem) pos = nodes(elem(i,7),:); pos = round(pos); if (~(mask.anatomy(pos(1),pos(2),pos(3)) == 0)) if labels(i) ~= 5 && labels(i) ~= 6 labels(i) disp('not 5 or 6') end count = count+1; for j = 1 : 3 for k = 1 : 3 condtensor((j-1)*3 + k,i) = condcell{pos(1),pos(2),pos(3)}(j,k); end end end end count end -------------- next part -------------- function [condtensor, s, fail] = sb_calcTensorCond_tuch(cfg,mask,V1,V2,V3,L1,L2,L3) check1 = isequal(V1.dim,V2.dim,V3.dim); check2 = isequal(V1.dim,size(V1.anatomy),size(V2.anatomy),size(V3.anatomy)); check3 = isequal(L1.dim,L2.dim,L3.dim); check4 = isequal(L1.dim,size(L1.anatomy),size(L2.anatomy),size(L3.anatomy)); check5 = isequal(V1.dim(1:3),L1.dim,mask.dim); check6 = ~(isempty(cfg)||isempty(cfg.conductivity)); check7 = ~(length(cfg.conductivity)<6); if (check1 && check2 && check3 && check4 && check5 && check6 && check7) fail = 0; failnan = 0; condtensor = cell(mask.dim); N = zeros(1,3); vol = zeros(1,3); for i = 1 : V1.dim(1) for j = 1 : V1.dim(2) for k = 1 : V1.dim(3) if (((mask.anatomy(i,j,k) == 5)|(mask.anatomy(i,j,k) == 6))&(L1.anatomy(i,j,k)>10^-4)&(L2.anatomy(i,j,k)>10^-4)&(L3.anatomy(i,j,k)>10^-5)) S = zeros(3); S(:,1) = V1.anatomy(i,j,k,:); S(:,2) = V2.anatomy(i,j,k,:); S(:,3) = V3.anatomy(i,j,k,:); if(norm(S'*S-diag([1,1,1]),2)>10e-7) S(:,1) = S(:,1) / norm(S(:,1)); S(:,2) = S(:,2) - (S(:,1)'*S(:,2))*S(:,1); S(:,2) = S(:,2) / norm(S(:,2)); S(:,3) = S(:,3) - (S(:,1)'*S(:,3))*S(:,1) - (S(:,2)'*S(:,3))*S(:,2); S(:,3) = S(:,3) / norm(S(:,3)); failnan = failnan + 1; end if(sum(sum(isnan(S),1),2)>0) condtensor{i,j,k} = cfg.conductivity(mask.anatomy(i,j,k))*diag([1,1,1]); else D = diag([L1.anatomy(i,j,k),L2.anatomy(i,j,k),L3.anatomy(i,j,k)]); T = S * D * S'; %T = hier skalieren condtensor{i,j,k} = T; if (mask.anatomy(i,j,k) == 5) N(1) = N(1) + 1; vol(1) = vol(1) + L1.anatomy(i,j,k)*L2.anatomy(i,j,k)*L3.anatomy(i,j,k); elseif (mask.anatomy(i,j,k) == 6) N(2) = N(2) + 1; vol(2) = vol(2) + L1.anatomy(i,j,k)*L2.anatomy(i,j,k)*L3.anatomy(i,j,k); % elseif (mask.anatomy(i,j,k) == 3) % N(3) = N(3) + 1; % vol(3) = vol(3) + L1.anatomy(i,j,k)*L2.anatomy(i,j,k)*L3.anatomy(i,j,k); end end elseif(((mask.anatomy(i,j,k) == 5)|(mask.anatomy(i,j,k) == 6))) condtensor{i,j,k} = cfg.conductivity(mask.anatomy(i,j,k))*diag([1,1,1]); if(((mask.anatomy(i,j,k) == 5)|(mask.anatomy(i,j,k) == 6))&(~((L1.anatomy(i,j,k)>10^-4)&(L2.anatomy(i,j,k)>10^-4)&(L3.anatomy(i,j,k)>10^-5)))) fail = fail + 1; end else condtensor{i,j,k} = zeros(3); end end end end d(1) = vol(1) / N(1); d(1) = d(1)^(1/3); d(2) = vol(2) / N(2); d(2) = d(2)^(1/3); % d(3) = vol(3) / N(3); % d(3) = d(3)^(1/3); s = d(1)*cfg.conductivity(5)+d(2)*cfg.conductivity(6); s = s / (d(1)^2 + d(2)^2); fprintf('s*d = %.6f\n',s*(d(1)+d(2))) fprintf('s = %.6f\n',s) % failper = fail / (N(1)+N(2)); mx = -100000; mn = 100000; for i = 1 : V1.dim(1) for j = 1 : V1.dim(2) for k = 1 : V1.dim(3) if (((mask.anatomy(i,j,k) == 5)||(mask.anatomy(i,j,k) == 6))&(L2.anatomy(i,j,k)>10^-4)&(L3.anatomy(i,j,k)>10^-5)) condtensor{i,j,k} = s * condtensor{i,j,k}; %keep outliers wma bigger from the highest cond around cond of wm if max(condtensor{i,j,k}(:)) > cfg.conductivity(4) condtensor{i,j,k}(1:1+size(condtensor{i,j,k},1):end) = cfg.conductivity(6); end if mx < max(max(condtensor{i,j,k})) mx = max(max(condtensor{i,j,k})); fprintf('mx %d, %d, %d\n',i,j,k) end if mn > min(min(condtensor{i,j,k})) mn = min(min(condtensor{i,j,k})); fprintf('mn %d, %d, %d\n',i,j,k) end end end end end mn mx end end From silvana.silva at upf.edu Tue Nov 6 16:01:27 2018 From: silvana.silva at upf.edu (SILVA PEREIRA, SILVANA) Date: Tue, 6 Nov 2018 16:01:27 +0100 Subject: [FieldTrip] ICA inspection: Error using ft_icabrowser.m In-Reply-To: References: Message-ID: Ok, I finally found a solution, just modifying line 53 in ft_icabrowser from: lay = ft_prepare_layout(cfglay, comp); to: lay = ft_prepare_layout(cfglay); since the layout somehow gets modified if I add the ica data structure (comp) in the input parameters. Now it works! Best regards, Silvana El lun., 5 nov. 2018 a las 18:44, Aykut Eken () escribió: > Hi Silvana, > > Can you check the match_str function? > > [seldat, sellay] = match_str(label, cfg.layout.label); > if isempty(seldat) > ft_error('labels in data and labels in layout do not match'); > end > > Best > > Aykut > > On 5 Nov 2018, at 18:23, SILVA PEREIRA, SILVANA > wrote: > > > Hi Diego, > > Your suggestion works fine for ft_databrowser, but not for ft_icabrowser! > > best regards, > Silvana > > > El lun., 5 nov. 2018 a las 16:58, Diego Lozano-Soldevilla (< > dlozanosoldevilla at gmail.com>) escribió: > >> Hi Silvana, >> Could you try the component viewmode option? >> >> cfg = []; >> cfg.layout = 'acticap-64ch-standard'; >> cfg.viewmode = 'component'; % Mode specifically suited to browse through ICA data >> ft_databrowser(cfg, comp); >> >> Best, >> Diego >> >> >> On Mon, Nov 5, 2018, 16:38 SILVA PEREIRA, SILVANA > wrote: >> >>> Dear all, >>> >>> I'm trying to use the funtion ft_icabrowser.m, but I get the following >>> error message: >>> >>> Error using topoplot_common (line 523) >>> labels in data and labels in layout do not match >>> >>> Error in ft_topoplotIC (line 184) >>> [cfg] = topoplot_common(cfg, comp); >>> >>> Error in ft_icabrowser (line 151) >>> ft_topoplotIC(cfgtopo, comp); >>> >>> I understand that the error is due to a mismatch between the labels of >>> the cfg struct and the layout I'm using. I modified the entries of the >>> struct in acticap-64ch-standard2.mat, where I removed four of the channels, >>> since we have in addition RM, LM, Heog and Veog. Adding these four labels >>> to the original label list does not solve the problem. Any idea to work >>> around this issue? >>> >>> Thank you! >>> Silvana >>> >>> >>> *Silvana Silva Pereira* >>> /Postdoctoral Researcher/ >>> Center for Brain and Cognition >>> >>> [image: Universitat Pompeu Fabra, Barcelona] >>> _______________________________________________ >>> fieldtrip mailing list >>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >>> https://doi.org/10.1371/journal.pcbi.1002202 >>> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From tineke.snijders at donders.ru.nl Wed Nov 7 13:28:46 2018 From: tineke.snijders at donders.ru.nl (Snijders, T.M. (Tineke)) Date: Wed, 7 Nov 2018 12:28:46 +0000 Subject: [FieldTrip] ft_rejectvisual reduces trial length Message-ID: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl> Hi, I am using trials of different lengths, which seems to give a problem in ft_rejectvisual. It wasn't a problem before (I think in 2016) when I did the same analysis. When using ft_rejectvisual, in the current fieldtrip version in the output data all trial lengths are reduced to the shortest trial-length, cutting away the remainder of the trial. In the command window it gives a warning of 'inconsistent sampling info'. Is there a workaround to still get the full trial-lengths for the artifact rejected data? I do want to keep the complete trials at this point. Thanks, Tineke -- Dr T.M. Snijders Research Staff Max Planck Institute for Psycholinguistics, Nijmegen www.ru.nl/people/donders/snijders-t http://www.mpi.nl/departments/language-development MPI | Wundtlaan 1 | Room 246 | tel 024 35 21246 | PO Box 310 | 6500 AH Nijmegen -------------- next part -------------- An HTML attachment was scrubbed... URL: From vincent.fontanier at inserm.fr Wed Nov 7 13:53:52 2018 From: vincent.fontanier at inserm.fr (vincent.fontanier at inserm.fr) Date: Wed, 07 Nov 2018 13:53:52 +0100 Subject: [FieldTrip] Spike-field analysis (combine freq and spike ; using ft_spiketriggeredspectrum) Message-ID: Hi everybody! I want to do some spike-field analysis on my dataset and have some questions regarding how fieldtrip handle such data and about the use of some of the functions related to this topic. 1. I have not found a fieldtrip way to combine freq structure (typically output from ft_freqanalysis) and spike structures. If I got it right, the fieldtrip pipeline to do spike-field is as follow: assuming filt_trials is the epoched LFP and spike is a fieldtrip spike structure · EPOCH the spike data like the LFP % spike to trials based on the epoched raw signal cfg = []; cfg.hdr = filt_trials.hdr; % contains information for conversion of samples to timestamps cfg.trlunit = 'samples'; cfg.trl = filt_trials.cfg.trl; % now in samples spikeTrials = ft_spike_maketrials(cfg,spike); · Compute the spike triggered spectrum cfg = []; cfg.method = 'mtmfft'; %'mtmconvol' is more powerful with many neurons and great firing rate cfg.foilim = [0 40]; % cfg.timwin determines spacing cfg.taper = 'hanning'; cfg.timwin = [-0.1 0.1]; %time around each spike stsConvol = ft_spiketriggeredspectrum(cfg, filt_trials, spikeTrials); · Make some analysis on the spike triggered spectrum cfg = []; cfg.method = 'ppc0'; % compute the Pairwise Phase Consistency cfg.avgoverchan = 'unweighted'; % weight spike-LFP phases irrespective of LFP power cfg.timwin = 'all'; % compute over all available spikes in the window cfg.latency = [-1 3]; statSts = ft_spiketriggeredspectrum_stat(cfg, stsConvol ); However having already performed time-frequency decomposition of all my LFP data I find this inefficient having to compute them again. Furthermore the methods of TF decomposition implemented in ft_spiketriggeredspectrum are much more limited than the one in ft_freqanalysis. So is there a way to combine the two together? A workaround is to realign the two together taking the sample of each spike in spikeTrials.timestamp{1}; and the start and end sample of each trial from the freq structure (freq.cfg.previous.trl. But this does not keep the fieldtrip way of formatting the data. Moreover this would require adjustments for further fieldtrip computations such as pairwise-phase consistency analysis using ft_spiketriggeredspectrum_stat. 2. (Useless if there is a solution to 1.) In ft_spiketriggeredspectrum you can provide a time window around each spike in the input to compute the spectrum. However the output spectrum is not time-resolved. Basically the output is just the average spectrum during the provided time window. Thus it is impossible to reconstruct the spike triggered time-frequency representation of the data. It is possible to run many iteration of ft_spiketriggeredspectrum for each timebin and store the output in a {chan}_spike_lfpchan_freq_time cell but it sounds like a very inefficient way to go. Therefore I am wondering if there is one way to do that more efficiently, for example an option that I missed in ft_spiketriggeredspectrum? Additionally could this time-resolved spiketriggeredspectrum output be used as an input to ft_spiketriggeredspectrum_stat in order to have a time-resolved output of the analysis? Many thanks! -- Vincent Fontanier Inserm U1208 (ex-U846) Stem Cell and Brain Research Institute Team Neurobiology of Executive Functions https://www.labex-cortex.com/en/team/neurobiology-executive-functions 18 av Du Doyen Lepine 69675 Bron CEDEX, (Lyon) FRANCE From tineke.snijders at donders.ru.nl Wed Nov 7 14:15:30 2018 From: tineke.snijders at donders.ru.nl (Snijders, T.M. (Tineke)) Date: Wed, 7 Nov 2018 13:15:30 +0000 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger(fieldtrip Digest, Vol 96, Issue 4) In-Reply-To: <1D61E3DF-EEDF-473C-BF56-EA9A1E1772BC@orange.fr> References: , <1D61E3DF-EEDF-473C-BF56-EA9A1E1772BC@orange.fr> Message-ID: <1541596532255.87599@donders.ru.nl> Hi Eelke, Yes I would also interpret this as artefact due to the projector (or: real visual entrainment to the projected visual stimuli). Your effect reminds me of a very clear 60 Hz response I had in my data, which appeared to be related to the refresh rate of the screen. We ran a few subjects with a refresh rate of 75 Hz and then the frequency mostly shifted to 75 Hz. See Snijders et al 2013, https://doi.org/10.1016/j.nicl.2013.06.015 Best, Tineke ________________________________________ From: fieldtrip on behalf of Antoine Ducorps Sent: Tuesday, November 6, 2018 12:44 PM To: fieldtrip at science.ru.nl Subject: Re: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger(fieldtrip Digest, Vol 96, Issue 4) Dear Eelke, I agree that a sharp 60 Hz peak is certainly an artefact. You mention that your projector is set to 120 Hz, but unless you use a very sophisticated one, the internal rate for most (if not all) commercial devices is still 60 Hz, and any input rate will be interpolated to that fundamental. In your case, every second frame would be displayed, or an average light value of two frames, I am not sure. You can check that on your technical manual, or alternating black/white frames and measuring the light output with a photodiode. If it really happens that your projector runs internally at 60 Hz, then you should also set your computer output at the same value, otherwise you don’t know what the interpolation or down sampling will do, most probably not documented by the vendor. Then, either you have a shielding problem with your projector electronics, or the 60 Hz rate is a real light effect in your stimulus, reflected in the visual cortex. Best, Antoine. > ------------------------------ > > Message: 3 > Date: Mon, 5 Nov 2018 17:21:05 +0100 > From: Eelke Spaak > To: FieldTrip discussion list > Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger > spectra > Message-ID: > > Content-Type: text/plain; charset="utf-8" > > Fellow FieldTrippers, > > I have identified two points of interest in the brain, between which I > want to compute connectivity metrics. One is in early visual cortex, > the other is in right temporal lobe, somewhat medial (compatible with > hippocampus, but exact interpretation not relevant right now). I > reconstructed activity for these grid points using LCMV beamformer (on > MEG data) and computed source-level Fourier spectra (taper = 'dpss', > tapsmofrq = 3) after applying a band-stop filter around 50 Hz and > harmonics. Using ft_connectivityanalysis, I computed both debiased > wPLI and Granger causality between the two points of interest. > > In both spectra, I see a clear peak at 60 Hz in the grand average > across 36 subjects, which is also there in the majority of individual > subjects (though very strong only in 2/36). Plots are attached. Now > this would be exciting news if indeed it turns out to be a true highly > band-limited gamma effect! > > But of course I suspect that this peak could very well be artifactual. > I can't think of any artifact source I may have missed that would > cause this, though. The projector refresh rate during the experiment > was set to 120 Hz, and not 60. (Also note this is European data so AC > frequency is 50 Hz and not 60, hence the band stop I mentioned > earlier.) > > Does anyone have any idea what might be causing this sharp peak? (A > genuine effect after all?) > > Thanks, > Eelke > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: granger-spectrum.png > Type: image/png > Size: 14990 bytes > Desc: not available > URL: > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: wpli-spectrum.png > Type: image/png > Size: 11765 bytes > Desc: not available > URL: > > ------------------------------ > > Message: 4 > Date: Mon, 5 Nov 2018 17:13:05 +0000 > From: "Schoffelen, J.M. (Jan Mathijs)" > To: FieldTrip discussion list > Subject: Re: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and > Granger spectra > Message-ID: > Content-Type: text/plain; charset="us-ascii" > > This looks like an artifact to me. > > In addition, the zigzag in the Granger spectra is suggestive of convergence issues in the non-parametric spectral matrix factorization in at least a few of your subjects. This is often caused by strong discontinuities in the spectra (spectral lines!) or poor behaviour of the time domain data at the edges of your epochs, causing spectral leakage. > > JM > > _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 From M.vanEs at donders.ru.nl Wed Nov 7 17:23:54 2018 From: M.vanEs at donders.ru.nl (Es, M.W.J. van (Mats)) Date: Wed, 7 Nov 2018 16:23:54 +0000 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra In-Reply-To: References: Message-ID: <3bda2dd4a5374570998b2f1d951a525d@EXPRD06.hosting.ru.nl> Hi Eelke, I actually had the exact same issue with one of my pilot datasets, recorded in February this year. It was recorded from the same MEG system in Nijmegen. I only found out a while later, so it wasn't possible to check if anything in the setup had changed. I assumed someone changed the refresh rate to 60 Hz and forgot to set it to the default 120 Hz afterwards, but I can't be certain. I do remember thinking whether it could be caused by the power shortages in Eastern Europe that were going on around that time, having heard that this affected internal circuitry of electrical devices. Not sure if it could cause this though. When were your data recorded? In the end I worked around this by putting a dft filter on 60 Hz, which worked reasonably well. Hope this mystery gets solved at some point! Cheers, Mats -----Original Message----- From: Eelke Spaak Sent: maandag 5 november 2018 17:21 To: FieldTrip discussion list Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra Fellow FieldTrippers, I have identified two points of interest in the brain, between which I want to compute connectivity metrics. One is in early visual cortex, the other is in right temporal lobe, somewhat medial (compatible with hippocampus, but exact interpretation not relevant right now). I reconstructed activity for these grid points using LCMV beamformer (on MEG data) and computed source-level Fourier spectra (taper = 'dpss', tapsmofrq = 3) after applying a band-stop filter around 50 Hz and harmonics. Using ft_connectivityanalysis, I computed both debiased wPLI and Granger causality between the two points of interest. In both spectra, I see a clear peak at 60 Hz in the grand average across 36 subjects, which is also there in the majority of individual subjects (though very strong only in 2/36). Plots are attached. Now this would be exciting news if indeed it turns out to be a true highly band-limited gamma effect! But of course I suspect that this peak could very well be artifactual. I can't think of any artifact source I may have missed that would cause this, though. The projector refresh rate during the experiment was set to 120 Hz, and not 60. (Also note this is European data so AC frequency is 50 Hz and not 60, hence the band stop I mentioned earlier.) Does anyone have any idea what might be causing this sharp peak? (A genuine effect after all?) Thanks, Eelke From maria.hakonen at gmail.com Thu Nov 8 08:35:20 2018 From: maria.hakonen at gmail.com (Maria Hakonen) Date: Thu, 8 Nov 2018 09:35:20 +0200 Subject: [FieldTrip] ft_mergealgin: high residual variance? In-Reply-To: References: <863F96C2-45E4-4441-B330-6C0292880D72@donders.ru.nl> Message-ID: Dear FieldTrip experts, Could the reason for the increased variance be that I have magnetometer data? However, the ft_megrealign and ft_prepare_headmodel don't have an option to specify whether I have magnetometers or gradiometers. Could you please let me know whether co-registration is needed before ft_megrealign (it is not needed with maxfilter)? Thank you already in advance! Best, Maria pe 2. marrask. 2018 klo 8.37 Maria Hakonen (maria.hakonen at gmail.com) kirjoitti: > Hi Jan-Mathias! > > Thank you for the answer! > I changed the units of gradiometers and head model to cm. > This clearly decreased the residual variances. > Also, singleshell seems to work better than localspheres. > > However, the transformation seems to still increase the residual variance > a lot. > > Here are some examples: > original -> template RV 407.64 % > original -> original RV 9.85 % > original -> template -> original RV 10.75 % > realigning trial 706 > original -> template RV 393.13 % > original -> original RV 9.00 % > original -> template -> original RV 9.90 % > realigning trial 707 > original -> template RV 362.10 % > original -> original RV 8.33 % > original -> template -> original RV 9.18 % > realigning trial 708 > original -> template RV 377.15 % > original -> original RV 9.43 % > original -> template -> original RV 10.33 % > > The code is now as follows: > load([data_path nameList{subj} '.mat']); > grad = datafinal.grad; > hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); > > cfg = []; > cfg.method = 'singlesphere'; > cfg.geom = hs; > cfg.grad = grad; > cfg.feedback = true; > vol = ft_prepare_headmodel(cfg); > > vol = ft_convert_units(vol,'cm'); > grad = ft_convert_units(grad,'cm'); > cfg = []; > cfg.template = template; > cfg.inwardshift = 2.5; > cfg.feedback ='no'; > cfg.vol = vol; > data = ft_megrealign(cfg, data); > > Best, > Maria > > to 1. marrask. 2018 klo 10.35 Schoffelen, J.M. (Jan Mathijs) ( > jan.schoffelen at donders.ru.nl) kirjoitti: > >> Hi Maria, >> >> Are you sure about the units in your headmodel (and gradiometers)? Using >> the value 1.0 as an inwardshift parameter suggests ‘cm’. Not sure what will >> happen when by accident the hs_file is in ‘m’ (which, as far as I know, is >> usually the case). >> >> Best wishes, >> >> Jan-Mathijs >> >> J.M.Schoffelen, MD PhD >> Senior Researcher, VIDI-fellow - PI, language in interaction >> Telephone: +31-24-3614793 >> Physical location: room 00.028 >> Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands >> >> >> >> >> On 29 Oct 2018, at 13:33, Maria Hakonen wrote: >> >> Dear FieldTrip experts, >> >> I have run ft_mergealign across subjects to align the head positions. >> However, the residual variance between the original and the realigned data >> seems to be high: >> >> original -> template RV 21232.46 % >> original -> original RV 36.96 % >> original -> template -> original RV 9579.95 % >> >> Could someone please let me know what would be the largest acceptable >> change in the residual variance, and what should I do if the residual >> variance is too high? Does the increase in residual variance mean that >> there is a large shift in the head position? >> >> I have used ft_mergealign as follows: >> >> template = list of subjects (i.e. I want to calculate an average head >> position over the subjects) >> >> grad = data.grad; >> hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); >> vol = ft_headmodel_localspheres(hs,grad); >> >> cfg = []; >> cfg.template = template; >> cfg.inwardshift = 1.0; >> cfg.vol = vol; >> data_aligned = ft_megrealign(cfg, data); >> >> Best, >> Maria >> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> >> >> >> >> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> > -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Thu Nov 8 09:33:17 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Thu, 8 Nov 2018 09:33:17 +0100 Subject: [FieldTrip] ft_rejectvisual reduces trial length In-Reply-To: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl> References: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl> Message-ID: Hi Tineke, That sounds like a bug to me. You could consider filing it: https://github.com/fieldtrip/fieldtrip/issues Meanwhile, as a workaround, I usually ensure that one of the columns of my data.trialinfo contains unique trial identifiers (e.g. data.trialinfo(:,end+1) = 1:numel(data.trial);). Then, after datclean = ft_rejectvisual(cfg, data), the datclean.trialinfo(:,end) will contain those trial IDs that are kept. Store those IDs somewhere and then you can select the appropriate trials from the original data using ft_selectdata. Hope that helps, Eelke On Wed, 7 Nov 2018 at 13:28, Snijders, T.M. (Tineke) wrote: > > Hi, > > > > I am using trials of different lengths, which seems to give a problem in ft_rejectvisual. It wasn’t a problem before (I think in 2016) when I did the same analysis. > > > > When using ft_rejectvisual, in the current fieldtrip version in the output data all trial lengths are reduced to the shortest trial-length, cutting away the remainder of the trial. In the command window it gives a warning of ‘inconsistent sampling info’. > > > > Is there a workaround to still get the full trial-lengths for the artifact rejected data? I do want to keep the complete trials at this point. > > > > Thanks, > > > > Tineke > > > > -- > Dr T.M. Snijders > Research Staff > Max Planck Institute for Psycholinguistics, Nijmegen > > www.ru.nl/people/donders/snijders-t > > http://www.mpi.nl/departments/language-development > > MPI | Wundtlaan 1 | Room 246 | tel 024 35 21246 | PO Box 310 | 6500 AH Nijmegen > > > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From e.spaak at donders.ru.nl Thu Nov 8 09:51:51 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Thu, 8 Nov 2018 09:51:51 +0100 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra In-Reply-To: <3bda2dd4a5374570998b2f1d951a525d@EXPRD06.hosting.ru.nl> References: <3bda2dd4a5374570998b2f1d951a525d@EXPRD06.hosting.ru.nl> Message-ID: Thanks a lot JM, Lasha, Antoine, Tineke, & Mats for all the fast responses! Just to be clear: I agree that it's extremely unlikely that the peak reflects an endogenous rhythm. @Antoine: the projector is supposed to be very high-end (vPixx Propixx) and I've used it in the past at 1440 Hz, which worked well. So I would assume it's not a matter of mismatch between the GPU refresh rate and the projector's. But this is worth checking with a photodiode. Also I guess it's possible that I forgot to check the computer's refresh rate on some of the recording sessions and that it was actually 60 Hz instead of 120. For now I think I will deal with this with a notch filter. @Mats: the data were recorded between April and June of this year (2018). Cheers, Eelke On Wed, 7 Nov 2018 at 17:23, Es, M.W.J. van (Mats) wrote: > > Hi Eelke, > > I actually had the exact same issue with one of my pilot datasets, recorded in February this year. It was recorded from the same MEG system in Nijmegen. I only found out a while later, so it wasn't possible to check if anything in the setup had changed. I assumed someone changed the refresh rate to 60 Hz and forgot to set it to the default 120 Hz afterwards, but I can't be certain. I do remember thinking whether it could be caused by the power shortages in Eastern Europe that were going on around that time, having heard that this affected internal circuitry of electrical devices. Not sure if it could cause this though. When were your data recorded? > In the end I worked around this by putting a dft filter on 60 Hz, which worked reasonably well. > > Hope this mystery gets solved at some point! > Cheers, > Mats > > -----Original Message----- > From: Eelke Spaak > Sent: maandag 5 november 2018 17:21 > To: FieldTrip discussion list > Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra > > Fellow FieldTrippers, > > I have identified two points of interest in the brain, between which I want to compute connectivity metrics. One is in early visual cortex, the other is in right temporal lobe, somewhat medial (compatible with hippocampus, but exact interpretation not relevant right now). I reconstructed activity for these grid points using LCMV beamformer (on MEG data) and computed source-level Fourier spectra (taper = 'dpss', tapsmofrq = 3) after applying a band-stop filter around 50 Hz and harmonics. Using ft_connectivityanalysis, I computed both debiased wPLI and Granger causality between the two points of interest. > > In both spectra, I see a clear peak at 60 Hz in the grand average across 36 subjects, which is also there in the majority of individual subjects (though very strong only in 2/36). Plots are attached. Now this would be exciting news if indeed it turns out to be a true highly band-limited gamma effect! > > But of course I suspect that this peak could very well be artifactual. > I can't think of any artifact source I may have missed that would cause this, though. The projector refresh rate during the experiment was set to 120 Hz, and not 60. (Also note this is European data so AC frequency is 50 Hz and not 60, hence the band stop I mentioned > earlier.) > > Does anyone have any idea what might be causing this sharp peak? (A genuine effect after all?) > > Thanks, > Eelke > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From Silvia.Formica at UGent.be Thu Nov 8 11:40:06 2018 From: Silvia.Formica at UGent.be (Silvia Formica) Date: Thu, 8 Nov 2018 10:40:06 +0000 Subject: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage In-Reply-To: References: <1541070268890.93169@donders.ru.nl>, Message-ID: <1541673605780.63773@UGent.be> Dear Cecilia and Tom, I am in a similar situation to the one described by Cecilia, so I thought of asking you for a suggestion. I have a dataset locked to the onset of the cue, and I would like to use the same baseline I used in this dataset to baseline-correct also the same dataset locked to the onset of the target. I tried the solution Tom suggested, but it is not working for me. The reason is that I preprocessed the cue-locked and target-locked datasets separately, therefore they end up having a slightly different number of trials. Do you have any idea of how this could be solved? Would it make sense to baseline-correct at the grandaverage level? Another option I have been thinking about is to use ft_redefinetrial. In this case I have other problems, though. If I try to use the ft_redefinetrial function after preprocessing and cleaning my cue-locked dataset, it will output all the trials in the raw data (therefore not accounting for the trial rejection I performed on the cue-locked dataset). Is this the right way to use this function or am I missing something? Thanks in advance for any input and sorry if my question is not very clear (still a newbie!) Best, Silvia ________________________________ From: fieldtrip on behalf of Tom Marshall Sent: 02 November 2018 23:29 To: FieldTrip discussion list Subject: Re: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage Hey Cecilia, I guess you could do that with a combination of 'ft_selectdata', and 'ft_math'. Let's say you have datasets called 'stimlocked' and 'cuelocked' You'd do something like: % cut out baseline from cuelocked cfg = []; cfg.latency = [-0.5 0]; % or whatever your baseline window is baseline = ft_selectdata(cfg, cuelocked); % subtract baseline from stimlocked cfg = []; cfg.operation = 'subtract'; baseline_corrected_stimlocked = ft_math(cfg, stimlocked, baseline); I think that ft_math allows you to input algebraic expressions too. So if you wanted to do, for example, a 'relchange' baseline correction you should substitute (something like) cfg.operation = '(x1-x2)/x2'; Best, Tom ________________________________ From: fieldtrip on behalf of Mazzetti, C. (Cecilia) Sent: 01 November 2018 11:04:23 To: fieldtrip at science.ru.nl Subject: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage Dear everyone, I have some stimulus locked data which I have to baseline with respect to the same data but cue locked. I was wondering whether there is a setting in fieldtrip which allows to do that (i.e. set the baseline timw window according to a different dataset, the cue-locked data) and applying it to the stimulus locked data. Thanks in advance! Best, Cecilia Cecilia Mazzetti - Ph.D. Candidate Donders Centre for Cognitive Neuroimaging, room 0.068 Kapittelweg 29 | 6525 EN Nijmegen -------------- next part -------------- An HTML attachment was scrubbed... URL: From afsamani at hst.aau.dk Thu Nov 8 12:29:29 2018 From: afsamani at hst.aau.dk (Afshin Samani) Date: Thu, 8 Nov 2018 11:29:29 +0000 Subject: [FieldTrip] explore cluster statistics Message-ID: <2E4AC5E2A691FE4B89DA44B0E70DF70A014154A5E6@AD-EXCHMBX3-3.aau.dk> Hi, I have never used fieldtrip before but I was trying to get familiar with its statistical analysis tools. I tried to run the online example from: http://www.fieldtriptoolbox.org/example/use_simulated_erps_to_explore_cluster_statistics I am using MATLAB Version: 9.4.0.813654 (R2018a) and I downloaded that latest version of fieldtrip on 5 nov 2018 I face two errors: first at ft_math function: Error using ft_math (line 151) the requested parameter is not present in the data Error in Nonparametric_statistical_testingEEGandMEG_data (line 45) difference = ft_math(cfg, timelock1, timelock2); % line 137 of ft_datatype_timelock removes the fields and if I comment that line out, I face another error at ft_timelcokstatistics Error using chol Matrix must be square. Error in montecarlo (line 13) R = chol(Sigma); % Y = R^{-T}X has covariance I Error in ft_timelockstatistics (line 185) [stat] = statmethod(cfg, dat, design); Error in Nonparametric_statistical_testingEEGandMEG_data (line 63) stat = ft_timelockstatistics(cfg, timelock1, timelock2); Any comment? Best regards, Associate Prof. Afshin Samani, PhD Sport Sciences Dept. of Health Science and Technology Aalborg University, Aalborg Denmark [Description: Beskrivelse: AAU_LINE_blue_rgb] -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image001.png Type: image/png Size: 4637 bytes Desc: image001.png URL: From hesham.elshafei at inserm.fr Thu Nov 8 16:40:42 2018 From: hesham.elshafei at inserm.fr (Hesham ElShafei) Date: Thu, 08 Nov 2018 16:40:42 +0100 Subject: [FieldTrip] Software Developer Opportunity in Lyon ! Message-ID: Hello fieldtrippers !! Our team is looking for a software developer to participate in the development of signal processing and visualization (topographies, time-frequency plots) tools for EEG and MEG signals. This project is in continuity with the ELAN software that has been developed and updated in our laboratory for more than 20 years (https://www.ncbi.nlm.nih.gov/pubmed/21687568) and will be in close collaboration with the MNE-Python development team (https://martinos.org/mne/stable/index.html) You will be responsible for developing a new graphic interface to the already available visualization tools. Regular interactions with team members will be organized to better sense the future users' needs. Moreover, different data sets (EEG, MEG) corresponding to various experimental conditions will be available to test the developed tools. You should have a master's degree computer science. Experience in (C/C++, Python programming) and knowledge of (Qt and signal processing). Knowledge of human electrophysiology (EEG/MEG) would be a plus. You should have strong organizational skills, be able to work independently, and have excellent interpersonal communication skills. You should be able to work in a dynamic, collaborative and international environment. Intended starting data is January 1st, 2019. Initial contract will be for 12 months with possibility of an extension According to education level and work experience salary will range between 1800 and 2500 euros net/month. For more information please contact : Pierre-Emmanuel Aguera : pe.aguera at inserm.fr Aurélie Bidet-Caulet : aurelie.bidet-caulet at inserm.fr Anne Caclin : anne.caclin at inserm.fr Or visit our website(s) ! https://crnl.univ-lyon1.fr/index.php/fr http://dycog.lyon.inserm.fr/ Cheers Hesham ps. maybe I'm not smart enough to figure out how to reply to answers to my questions , but thanks a lot for answering them !! :) -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Fri Nov 9 11:18:26 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Fri, 9 Nov 2018 10:18:26 +0000 Subject: [FieldTrip] ft_rejectvisual reduces trial length In-Reply-To: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl> References: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl> Message-ID: <17306DD6-842B-4153-A0D1-CFA9DA13CD90@donders.ru.nl> Hi Tineke, This is indeed inappropriate behaviour of the function, which has been tracked down to something going wrong in ft_selectdata. I have submitted a PR https://github.com/fieldtrip/fieldtrip/pull/873 on the github-repo which fixes the issue https://github.com/fieldtrip/fieldtrip/issues/871 I am pretty sure that this fix does not come with unwanted side effects, but since ft_selectdata is used almost everywhere I’d appreciate some moral support before merging. Let me know what you think. Jan-Mathijs PS: for those who are around: please do join Tineke and me this coming Sunday Nov 11, when we will commemorate the centenary of the 1918 armistice with a choral concert at the church of St. Anthony of Padua in Nijmegen. It starts at 15.30, see http://www.kamerkoormnemosyne.nl J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands On 7 Nov 2018, at 13:28, Snijders, T.M. (Tineke) > wrote: Hi, I am using trials of different lengths, which seems to give a problem in ft_rejectvisual. It wasn’t a problem before (I think in 2016) when I did the same analysis. When using ft_rejectvisual, in the current fieldtrip version in the output data all trial lengths are reduced to the shortest trial-length, cutting away the remainder of the trial. In the command window it gives a warning of ‘inconsistent sampling info’. Is there a workaround to still get the full trial-lengths for the artifact rejected data? I do want to keep the complete trials at this point. Thanks, Tineke -- Dr T.M. Snijders Research Staff Max Planck Institute for Psycholinguistics, Nijmegen www.ru.nl/people/donders/snijders-t http://www.mpi.nl/departments/language-development MPI | Wundtlaan 1 | Room 246 | tel 024 35 21246 | PO Box 310 | 6500 AH Nijmegen _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Fri Nov 9 15:05:33 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Fri, 9 Nov 2018 14:05:33 +0000 Subject: [FieldTrip] explore cluster statistics In-Reply-To: <2E4AC5E2A691FE4B89DA44B0E70DF70A014154A5E6@AD-EXCHMBX3-3.aau.dk> References: <2E4AC5E2A691FE4B89DA44B0E70DF70A014154A5E6@AD-EXCHMBX3-3.aau.dk> Message-ID: <2263141F-A069-45C9-8FDA-0C7D24149CCC@donders.ru.nl> Afshin, I face two errors: first at ft_math function: Error using ft_math (line 151) the requested parameter is not present in the data As the error indicates, the requested parameter ‘avg’ is apparently not in the data. In other words, the timelock1/2 structures shouldn't have an ‘avg’ field, causing ft_math to complain. The reason for this is that the example script pre-dates some changes to the fieldtrip code base, which caused the ‘avg’ field to disappear if ft_timelockanalysis is called with cfg.keeptrials = ‘yes’. Probably, if you use cfg.parameter = ‘trial’, rather than ‘avg’ in your call to ft_math, it’ll work. Error in Nonparametric_statistical_testingEEGandMEG_data (line 45) difference = ft_math(cfg, timelock1, timelock2); % line 137 of ft_datatype_timelock removes the fields and if I comment that line out, I face another error at ft_timelcokstatistics Error using chol Matrix must be square. Error in montecarlo (line 13) R = chol(Sigma); % Y = R^{-T}X has covariance I For some reason, you end up in a function called ‘montecarlo’, which is incorrect, since you should have ended up in ft_statistics_montecarlo. The cause of all this is probably that the folder that has the ‘montecarlo’ function is higher on the matlab search path than the fieldtrip folder. Jan-Mathijs J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands Error in ft_timelockstatistics (line 185) [stat] = statmethod(cfg, dat, design); Error in Nonparametric_statistical_testingEEGandMEG_data (line 63) stat = ft_timelockstatistics(cfg, timelock1, timelock2); Any comment? Best regards, Associate Prof. Afshin Samani, PhD Sport Sciences Dept. of Health Science and Technology Aalborg University, Aalborg Denmark _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From twater14 at student.aau.dk Sun Nov 11 21:40:19 2018 From: twater14 at student.aau.dk (Toby Steven Waterstone) Date: Sun, 11 Nov 2018 20:40:19 +0000 Subject: [FieldTrip] Comparing spatio-temporal data with FieldTrip toolbox Message-ID: <63EFD5634C86B743A236791097CE83866D363D17@AD-EXCHMBX3-2.aau.dk> Hi, I am analyzing high density surface electromyography (HD-sEMG) data, where I am calculating the similarities between each channel in the sensor grid. This is done in two conditions to see if there are any effect from a specific intervention, from 12 subjects, where pre and post recordings has been performed for both a control and intervention group. For the similarity measure I have calculated normalised mutual information (NMI), where I have two different outcome measures from my calculations. NMI heatmaps (12x5 matrices) representing the aggregated NMI over the HD-sEMG sensor grid, and connectivity maps (60x60 matrices) representing the similarity between each electrode pair. I have attached examples of the data, so you get the idea. I am trying to find a way to compare this data, which has a spatio-temporal pattern. The FieldTrip toolbox has a lot of functions to analyse EEG/MEG data and as my data (HD-sEMG) has many of the same characteristics as EEG data, this toolbox might be useful to analyse my data. But in my case, I have already analysed the EMG signals by calculating NMI, and only need to do the statistical comparison now. FieldTrip is provided with a couple of statistical functions, but I'm unsure about how to use these on my data and if it is possible with this toolbox. The approach I'm looking into is FieldTrip cluster-based permutation tests, but it cannot directly be transferred to my case, because I am comparing the already analysed data. So my data kind of already have the data structure of the output from the analysis of this tutorial (the plots). So my question is: How can I use the statistical functions of the FieldTrip toolbox for comparison of NMI (spatio-temporal data)? If this is possible? I want to compare the heatmaps (12x5 vs 12x5 data structures) to each other and connectivity maps (60x60 vs 60x60 data structures) to each other. Thank you Best regards Toby S. Waterstone -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Heatmap.png Type: image/png Size: 162770 bytes Desc: Heatmap.png URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Connectivity_map.png Type: image/png Size: 306140 bytes Desc: Connectivity_map.png URL: From rdm146 at newark.rutgers.edu Mon Nov 12 17:36:29 2018 From: rdm146 at newark.rutgers.edu (Ravi Mill) Date: Mon, 12 Nov 2018 16:36:29 +0000 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: References: <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> , Message-ID: Many thanks Marios and Carsten - I will try out the scripts you sent me and let you know if I have any issues. Best wishes Ravi ________________________________ From: Marios Antonakakis Sent: Tuesday, November 6, 2018 8:21:56 AM To: Carsten Wolters Cc: fieldtrip at science.ru.nl; Ravi Mill Subject: Re: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio Hi Ravi, assuming that you work under Linux and you have already the DTI data registered to MRI, you need then to run the below script. %%1, calculate conductivity tensor for every voxel % mri_segmented_final, V1, ... L1 variables have ft MRI structure %skin,skull compacta, skull spongiosa, CSF, GM, WM conductivity = [0.4300, 0.0024, 0.0086, 1.7900, 0.3300, 0.1400]; cfg =[]; cfg.compartments = mri_segmented_final.anatomylabel; % anatomical labels, skin: 0, skull spongiosa 2 and show on... cfg.conductivity = conductivity; [condcell, s, fail] = sb_calcTensorCond_tuch(cfg,mri_segmented_final,V1,V2,V3,L1,L2,L3); %% 2. assign the conductivity tensor with the hex mesh [mesh.nodes,mesh.elements,mesh.labels]= read_vista_mesh('foo_mesh.v); mask = mri_segmented_final; mask.anatomy((mask.anatomy~=5) & (mask.anatomy~=6))=0; mask.anatomy((mask.anatomy==5) | (mask.anatomy==6))=1; tensors = sb_assiTensorCond(mask,mesh.nodes,mesh.elements,mesh.labels,condcell); write_vista_mesh('foo_mesh_aniso.v',mesh.nodes,mesh.elements,mesh.labels,tensors'); Do not hesitate to ask further questions. *Note that write_vista_mesh and read_vista_mesh are private ft functions. Best regards, Marios [https://mailfoogae.appspot.com/t?sender=aYW50b25ha2FraXNtYXJAZ21haWwuY29t&type=zerocontent&guid=420ef8db-499e-48d0-b63b-37a4d6551822]ᐧ Στις Τρί, 6 Νοε 2018 στις 12:10 μ.μ., ο/η Carsten Wolters > έγραψε: Hi Ravi, Marios (in CC) promised to send you the short Matlab-script that we use to transform the diffusion tensors to conductivity tensors using Tuch's effective medium approach. For this approach, please check e.g. the subsection "Calibrated Finite Element Head Model and Forward Solution" in https://link.springer.com/article/10.1007/s10548-017-0568-9 BR Carsten Am 05.11.18 um 18:26 schrieb Ravi Mill: Hi Carsten and Johannes Many thanks for responding, and for developing these great tools! I'm in the process of acquiring a large diffusion MR dataset from which I can hopefully create an 'averaged' atlas. From your responses I think I have a sense of how to integrate the conductivity tensors derived from this atlas with the Fieldtrip FEM pipeline. But I was wondering if you had any advice on how to compute these conductivity tensors in the first place? From the paper that Carsten sent, It seems like the FDT program within FSL is what I need to compute diffusion tensors from the raw diffusion images (steps 1-6 from the FDT user guide https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Processing_pipeline)? Seemingly, these diffusion tensors need to then be converted to conductivity tensors - any advice on how to do this (or if you could point me to some example code) would be greatly appreciated. Thanks Ravi FDT/UserGuide - FslWiki - University of Oxford fsl.fmrib.ox.ac.uk Eddy Current Correction. Eddy currents in the gradient coils induce (approximate) stretches and shears in the diffusion weighted images. These distortions are different for different gradient directions. Thanks again Ravi ________________________________ From: fieldtrip on behalf of Johannes Vorwerk Sent: Monday, October 29, 2018 10:14:16 AM To: FieldTrip discussion list Subject: Re: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio Dear Ravi, as Carsten already said, calculating FEM with anisotropic conductivities is not directly supported by the FieldTrip-SimBio implementation. However, if you are willing to invest a bit of time it is possible to work around this. The „only“ thing that needs to be changed is the calculation of the FEM stiffness matrix, which is performed by the routine „calc_stiffness_matrix_val“ in the function sb_calc_stiff (usually called from ft_prepare_headmodel). The problem is that FieldTrip does not support anisotropic conductivities, so that you would have to call calc_stiffness_matrix_val directly. You can see the correct call in sb_calc_stiff. For anisotropic conductivities you have to replace the input „cond“ by a #elements x 6 matrix containing your anisotropic conductivities in the format "xx yy zz xy yz zx“. If you now follow the normal FieldTrip-SimBio workflow using the resulting stiffness matrix, you will get results for anisotropic conductivities. Best, Johannes Am 29.10.2018 um 12:31 schrieb Carsten Wolters >: Dear Ravi, 1) You can use the pure SimBio-code from https://www.mrt.uni-jena.de/simbio/index.php/Main_Page to treat WM anisotropy. While it would in principle also be possible to use anisotropic conductivities with FieldTrip-SimBio, this is currently not implemented using ft_prepare_headmodel. Johannes (in CC), who implemented Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: "Depending on your matlab skills and your available time, I could help you to give it a try though. It should be possible with using some direct function calls instead of the high-level fieldtrip-functions." 2) We recommend http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf on individual data. I could imagine that an atlas does a reasonable job w.r.t. the main bigger fiber tracts such as corpus callosum or pyramidal tracts, but that the finer details in the cortices are individual. We always measure T1, T2 and DTI from each subject and I personally do not have experience with such a group-level anisotropy compared to the individual one. Might be interesting to hear from others what they think!? BR Carsten Am 25.10.18 um 23:05 schrieb Ravi Mill: Dear Fieldtrippers I have applied the FEM simbio head modeling pipeline implemented in Fieldtrip to my EEG data. My understanding is that this pipeline assumes isotropic conductivities for 5 head compartments (as specified by cfg.conductivity in ft_prepare_headmodel). After reading some papers (e.g. Vorwerk et al 2014 https://doi.org/10.1016/j.neuroimage.2014.06.040), it seems like incorporating white matter conductivity anisotropy has a relatively small albeit significant effect on the source solution. I am interested in comparing FEM results when treating white matter as anisotropic. My questions are as follows: 1. Is there a way to implement the FEM simbio head model whilst treating WM as anisotropic within Fieldtrip? If so, how would one do this (or are there any resources available that demonstrate this)? 2. From previous papers and some simbio documentation (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples) it seems like diffusion MRI data is required to calculate the WM conductivity for each individual subject. I only have T1 and T2 scans for my subjects. So would it be possible to use WM anisotropic information obtained from some kind of diffusion MRI group average/atlas instead (accepting some loss in subject-level precision)? If so, does such a group average/atlas exist? Any help would be greatly appreciated! Thanks Ravi _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -- Prof. Dr.rer.nat. Carsten H. Wolters University of Münster Institute for Biomagnetism and Biosignalanalysis Malmedyweg 15 48149 Münster, Germany Phone: +49 (0)251 83 56904 +49 (0)251 83 56865 (secr.) Fax: +49 (0)251 83 56874 Email: carsten.wolters at uni-muenster.de Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -- Prof. Dr.rer.nat. Carsten H. Wolters University of Münster Institute for Biomagnetism and Biosignalanalysis Malmedyweg 15 48149 Münster, Germany Phone: +49 (0)251 83 56904 +49 (0)251 83 56865 (secr.) Fax: +49 (0)251 83 56874 Email: carsten.wolters at uni-muenster.de Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Wed Nov 14 12:28:37 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Wed, 14 Nov 2018 11:28:37 +0000 Subject: [FieldTrip] Comparing spatio-temporal data with FieldTrip toolbox In-Reply-To: <63EFD5634C86B743A236791097CE83866D363D17@AD-EXCHMBX3-2.aau.dk> References: <63EFD5634C86B743A236791097CE83866D363D17@AD-EXCHMBX3-2.aau.dk> Message-ID: <230E33B4-CF3E-448C-A6C9-AB2D94688863@donders.ru.nl> Dear Toby, I think that this is not altogether too difficult. What you would need to do is to put your numeric data into a structure that FieldTrip can work with. Specifically, if you manage to create a so-called ’timelock’ or ‘freq’ representation of your data, you can use ft_timelockstatistics or ft_freqstatistics for the statistical inference. (as a side note, I think it’s up to you to think whether it makes sense to use the spatial clustering heuristic for family-wise error control when comparing the connectivity matrices; yet, you can still do a permutation test to test the null hypothesis of exchangeability between groups). Long story short for the 12x5 NMI data, I’d create 2 data structures, let’s call them freq1 (intervention group) and freq2 (controls), with the following fields freq1.label = {‘thenameofthisisnotrelevant’}; freq1.freq = 1:12 freq1.time = 1:5 freq1.dimord = ‘rpt_chan_freq_time’; freq1.powspctrm = zeros(number-of-subjects, 1, 12, 5); for i = 1:nsubj freq1.powspctrm(i,1,:,:) = nmi; % this should yield a 1 x 12 x 5 matrix end and the same thing for freq2. Then you can use ft_freqstatistics for statistical inference, with optional clustering for multiple comparison correction. In this case, the clustering will take place across the ‘freq’, and ‘time’ dimensions, which in your case boils down to spatial clustering across adjacent electrodes in the x and y directions, respectively. For the connectivity matrices, I’d convert the single subject matrices into a vector (using the lower triangular part only), but I’d say: first things first. Best wishes, Jan-Mathijs On 11 Nov 2018, at 21:40, Toby Steven Waterstone > wrote: Hi, I am analyzing high density surface electromyography (HD-sEMG) data, where I am calculating the similarities between each channel in the sensor grid. This is done in two conditions to see if there are any effect from a specific intervention, from 12 subjects, where pre and post recordings has been performed for both a control and intervention group. For the similarity measure I have calculated normalised mutual information (NMI), where I have two different outcome measures from my calculations. NMI heatmaps (12x5 matrices) representing the aggregated NMI over the HD-sEMG sensor grid, and connectivity maps (60x60 matrices) representing the similarity between each electrode pair. I have attached examples of the data, so you get the idea. I am trying to find a way to compare this data, which has a spatio-temporal pattern. The FieldTrip toolbox has a lot of functions to analyse EEG/MEG data and as my data (HD-sEMG) has many of the same characteristics as EEG data, this toolbox might be useful to analyse my data. But in my case, I have already analysed the EMG signals by calculating NMI, and only need to do the statistical comparison now. FieldTrip is provided with a couple of statistical functions, but I'm unsure about how to use these on my data and if it is possible with this toolbox. The approach I'm looking into is FieldTrip cluster-based permutation tests, but it cannot directly be transferred to my case, because I am comparing the already analysed data. So my data kind of already have the data structure of the output from the analysis of this tutorial (the plots). So my question is: How can I use the statistical functions of the FieldTrip toolbox for comparison of NMI (spatio-temporal data)? If this is possible? I want to compare the heatmaps (12x5 vs 12x5 data structures) to each other and connectivity maps (60x60 vs 60x60 data structures) to each other. Thank you Best regards Toby S. Waterstone _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From stanabe at wisc.edu Wed Nov 14 21:26:33 2018 From: stanabe at wisc.edu (SEAN TANABE) Date: Wed, 14 Nov 2018 20:26:33 +0000 Subject: [FieldTrip] Concerning powcorr_orth method in ft_connectivityanalysis Message-ID: Dear all, I am trying to use the powcorr_orth method in ft_connectivityanalysis and noticed the results always produce a random connectivity pattern. A similar issue has been raised a year ago here; https://mailman.science.ru.nl/pipermail/fieldtrip/2017-July/011695.html I found after editing a line from ft_connectivityanalysis the results look more reasonable, and wanted to confirm if this editing is correct with the fieldtrip developers. In fieldtrip-20170820 ft_connectivityanalysis I noticed a line which reshapes the fourierspctrm before inputting to ft_connectivity_powcorr_orth (line 852 in fieldtrip-20181113). dat = reshape(data.fourierspctrm(:,:,i)', nrpttap, nchan).'; datout{i} = ft_connectivity_powcorr_ortho(dat, optarg{:}); This seems to not fit the description of ft_connectivity_powcorr_orth, which requires "dat" to have nchan rows. I changed this to dat = data.fourierspctrm(:,:,i)'; datout{i} = ft_connectivity_powcorr_ortho(dat, optarg{:}); The above gave natural looking blobs of connectivity in the topology. Thank you. Sean -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Thu Nov 15 09:25:39 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 15 Nov 2018 08:25:39 +0000 Subject: [FieldTrip] Concerning powcorr_orth method in ft_connectivityanalysis In-Reply-To: References: Message-ID: <3E50C67C-9EF0-4C04-8988-AAA879C87134@donders.ru.nl> Dear Sean, Thanks for catching this. The original line 852 does not make sense at all, indeed. May I suggest to replace this with data.fourierspctrm(:,:,i).’ (note the ‘.’)? Can you please submit this as a pull request on github? http://www.fieldtriptoolbox.org/development/git Many thanks, Jan-Mathijs On 14 Nov 2018, at 21:26, SEAN TANABE > wrote: Dear all, I am trying to use the powcorr_orth method in ft_connectivityanalysis and noticed the results always produce a random connectivity pattern. A similar issue has been raised a year ago here; https://mailman.science.ru.nl/pipermail/fieldtrip/2017-July/011695.html I found after editing a line from ft_connectivityanalysis the results look more reasonable, and wanted to confirm if this editing is correct with the fieldtrip developers. In fieldtrip-20170820 ft_connectivityanalysis I noticed a line which reshapes the fourierspctrm before inputting to ft_connectivity_powcorr_orth (line 852 in fieldtrip-20181113). dat = reshape(data.fourierspctrm(:,:,i)', nrpttap, nchan).'; datout{i} = ft_connectivity_powcorr_ortho(dat, optarg{:}); This seems to not fit the description of ft_connectivity_powcorr_orth, which requires "dat" to have nchan rows. I changed this to dat = data.fourierspctrm(:,:,i)'; datout{i} = ft_connectivity_powcorr_ortho(dat, optarg{:}); The above gave natural looking blobs of connectivity in the topology. Thank you. Sean _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From tineke.snijders at donders.ru.nl Thu Nov 15 12:27:26 2018 From: tineke.snijders at donders.ru.nl (Snijders, T.M. (Tineke)) Date: Thu, 15 Nov 2018 11:27:26 +0000 Subject: [FieldTrip] ft_rejectvisual reduces trial length In-Reply-To: <17306DD6-842B-4153-A0D1-CFA9DA13CD90@donders.ru.nl> References: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl>, <17306DD6-842B-4153-A0D1-CFA9DA13CD90@donders.ru.nl> Message-ID: <1542281246035.19816@donders.ru.nl> Thanks for fixing this Jan-Mathijs, it works beautifully now! Tineke ________________________________ From: fieldtrip on behalf of Schoffelen, J.M. (Jan Mathijs) Sent: Friday, November 9, 2018 11:18 AM To: FieldTrip discussion list Subject: Re: [FieldTrip] ft_rejectvisual reduces trial length Hi Tineke, This is indeed inappropriate behaviour of the function, which has been tracked down to something going wrong in ft_selectdata. I have submitted a PR https://github.com/fieldtrip/fieldtrip/pull/873 on the github-repo which fixes the issue https://github.com/fieldtrip/fieldtrip/issues/871 I am pretty sure that this fix does not come with unwanted side effects, but since ft_selectdata is used almost everywhere I'd appreciate some moral support before merging. Let me know what you think. Jan-Mathijs PS: for those who are around: please do join Tineke and me this coming Sunday Nov 11, when we will commemorate the centenary of the 1918 armistice with a choral concert at the church of St. Anthony of Padua in Nijmegen. It starts at 15.30, see http://www.kamerkoormnemosyne.nl J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands On 7 Nov 2018, at 13:28, Snijders, T.M. (Tineke) > wrote: Hi, I am using trials of different lengths, which seems to give a problem in ft_rejectvisual. It wasn't a problem before (I think in 2016) when I did the same analysis. When using ft_rejectvisual, in the current fieldtrip version in the output data all trial lengths are reduced to the shortest trial-length, cutting away the remainder of the trial. In the command window it gives a warning of 'inconsistent sampling info'. Is there a workaround to still get the full trial-lengths for the artifact rejected data? I do want to keep the complete trials at this point. Thanks, Tineke -- Dr T.M. Snijders Research Staff Max Planck Institute for Psycholinguistics, Nijmegen www.ru.nl/people/donders/snijders-t http://www.mpi.nl/departments/language-development MPI | Wundtlaan 1 | Room 246 | tel 024 35 21246 | PO Box 310 | 6500 AH Nijmegen _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From kai.hwang at gmail.com Fri Nov 16 17:47:38 2018 From: kai.hwang at gmail.com (Kai Hwang) Date: Fri, 16 Nov 2018 10:47:38 -0600 Subject: [FieldTrip] Postdoc Position | Cognitive Neuroscience | University of Iowa Message-ID: The Hwang lab for Neurocognitive Dynamics in the Psychological and Brain Sciences Department at the University of Iowa has a fully funded postdoc position available. Our lab focuses on brain network mechanisms, cognitive control, and their developmental processes with a strong emphasis on the human thalamocortical system and neural oscillations. Our research utilizes multimodal neuroimaging (EEG and fMRI), TMS, and lesion studies in combination with network neuroscience approaches. For more info, please see: https://kaihwang.github.io/ The lab is affiliated with the DeLTA Center and the Iowa Neuroscience Institute, which offers a collaborative research environment with access to research dedicated 3T and 7T MRI systems, TMS, EEG, neurosurgery patients, and a large lesion patient registry. Postdoc Candidate Qualifications - Ph.D. in Psychology, Neuroscience, or other related disciplines. - Experience with neuroimaging (fMRI, EEG/MEG). - Strong Programming skills (Matlab or Python) The postdoc position is opened immediately until filled. To apply, please send a cover letter and CV to: kai-hwang at uiowa.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From Eliana.Klier at uth.tmc.edu Fri Nov 16 19:33:21 2018 From: Eliana.Klier at uth.tmc.edu (Klier, Eliana M) Date: Fri, 16 Nov 2018 18:33:21 +0000 Subject: [FieldTrip] Postdoc Job Posting Message-ID: <0e5edb17c01c46b8a4516640461d11b4@uth.tmc.edu> Hello Fieldtrip, Is it possible for the Tandon Lab to post the attached postdoctoral ad to your mailing list? Sincerely, Eliana Eliana M Klier, Ph.D. Senior Program Manager - Research McGovern Medical School part of UTHealth | The University of Texas Health Science Center at Houston Department of Neurosurgery 6431 Fannin St | Rm G.550G | Houston, TX 77030 Phone: 713-500-5442 Email: Eliana.Klier at uth.tmc.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Tandon Lab Post Doc.docx Type: application/vnd.openxmlformats-officedocument.wordprocessingml.document Size: 159409 bytes Desc: Tandon Lab Post Doc.docx URL: From Oscar.Woolnough at uth.tmc.edu Fri Nov 16 17:39:54 2018 From: Oscar.Woolnough at uth.tmc.edu (Woolnough, Oscar) Date: Fri, 16 Nov 2018 16:39:54 +0000 Subject: [FieldTrip] Multiple Postdocs available in the Neurobiology of Language Message-ID: <064DCE91-3B14-4E59-91E5-F1AD8A0EE289@uth.tmc.edu> POSTDOCTORAL RESEARCH POSITIONS Multiple Postdoctoral research positions are available in the Tandon Lab at The University of Texas in Houston as part of the newly formed Texas Epilepsy Neurotechnologies and Neuroinformatics (TENN) Institute. Positions are funded either via multi year Institute funding or by NIH funds (an R01 and a U01). The lab uses multimodal approaches – fMRI, lesional analysis following epilepsy surgery, intracranial recordings and direct stimulation to create validate network level representations of language. Lab Collaborators include Greg Hickok (UCI), Stanislas Dehaene (NeuroSpin), Nathan Crone (JHU), Simon Fisher Baum (Rice) and Xaq Pitkow (Rice-Baylor); the post-doc will benefit from a close interaction with these experts in the fields of reading, semantics, speech production and computational neuroscience. The selected individual must have a Ph.D. in one or more of the following: neuroscience, psychology, cognitive science, mathematics, electrical engineering or computer science. Previous experience in neural time series data analysis, functional imaging studies of language, or studies of speech production are desirable – but not crucial. They must possess the ability to independently code in any or all of the following: MATLAB, R or python. They are expected to be highly motivated, team players with a passion to study cognitive processes using any or all of the various modalities available in the lab - imaging, direct recordings and closed-loop cortical stimulation in humans. Given the multiple unpredictable variables and privacy issues around data collection in human patients, the individual must possess high ethical and professional standards and be adaptable. A strong publication record and excellent academic credentials are highly desirable. CONTACT: Nitin.Tandon at uth.tmc.edu Eliana.Klier at uth.tmc.edu More information @ www.tandonlab.org -------------- next part -------------- An HTML attachment was scrubbed... URL: From p.gaur at ulster.ac.uk Mon Nov 19 19:19:55 2018 From: p.gaur at ulster.ac.uk (Gaur, Pramod) Date: Mon, 19 Nov 2018 18:19:55 +0000 Subject: [FieldTrip] Combine multiple blocks Message-ID: Dear Team, I have a quick concern, I have a cognitive task and recorded three blocks. Is there any way in which I can combine all the three blocks before the preprocessing. I have alternate to preprocess the blocks separately and then combining them. In this case, I lose the data_MEG_filt .grad.chanpos location. cfg = []; cfg.dataset = filename; % % cfg = ft_definetrial(cfg); cfg.trialdef.eventtype = 'STI101'; %cfg.trialdef.eventtype = 'gui'; cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own cfg.trialdef.prestim = prestim; cfg.trialdef.poststim = poststim; % cfg.length = 1; cfg = ft_definetrial(cfg); % read in the data from the magnetometer % cfg.channel = {'MEGMAG','STI101'}; cfg.channel = {'MEG'};%,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};% % cfg.lpfilter = 'yes'; % cfg.lpfreq = 40; % cfg.hpfilter = 'yes'; % cfg.hpfreq = 1; cfg.continuous = 'yes'; cfg.detrend = 'no'; cfg.demean = 'yes'; cfg.dftfilter = 'yes'; % cfg.dftfreq =[50 100 150];% power line noise cfg.bpfreq = [1 48]; cfg.metric = 'zvalue'; cfg.layout = 'neuromag306all.lay'; cfg.baselinewindow = [-0.5 0]; data_MEG_filt = ft_preprocessing(cfg); if isempty(data_MEG_filt) data_MEG_filt=data; grad = data.grad; else cfg = []; data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data); end Any advice will be highly appreciated. [Ulster University] Dr Pramod Gaur Research Assistant in Neuro-Imaging Technology School of Computing, Engineering and Intelligent Systems Magee Campus E: p.gaur at ulster.ac.uk W: www.ulster.ac.uk Social: Twitter: @SceisUni Facebook: @UlsterUniComputingEngineeringIntelligentSystems [cid:image004.jpg at 01D3EDDB.BF0D58A0] This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image001.jpg Type: image/jpeg Size: 37467 bytes Desc: image001.jpg URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image003.jpg Type: image/jpeg Size: 3389 bytes Desc: image003.jpg URL: From jan.schoffelen at donders.ru.nl Mon Nov 19 20:50:58 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Mon, 19 Nov 2018 19:50:58 +0000 Subject: [FieldTrip] Combine multiple blocks In-Reply-To: References: Message-ID: Pramod, I don’t understand how ‘combining all the three blocks before the preprocessing’ would help you to preserve the channel positions. Apparently, these are different for each of the blocks in your case. It’s also not clear to me, why this is causes problem in your specific case. Anyway, ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs. If you still want to obtain some kind of ‘average’ representation of the channel position, you can use ft_average_sens. If the positions are not altogether too different, it might be OK to average the positions across blocks, although technically it’s of course incorrect to do so. Yet, if for each of the blocks you have applied block-specific spatial transformations (e.g. maxfilter) then it’s a different story altogether, and things will become hairy very rapidly. Best wishes, Jan-Mathijs On 19 Nov 2018, at 19:19, Gaur, Pramod > wrote: Dear Team, I have a quick concern, I have a cognitive task and recorded three blocks. Is there any way in which I can combine all the three blocks before the preprocessing. I have alternate to preprocess the blocks separately and then combining them. In this case, I lose the data_MEG_filt .grad.chanpos location. cfg = []; cfg.dataset = filename; % % cfg = ft_definetrial(cfg); cfg.trialdef.eventtype = 'STI101'; %cfg.trialdef.eventtype = 'gui'; cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own cfg.trialdef.prestim = prestim; cfg.trialdef.poststim = poststim; % cfg.length = 1; cfg = ft_definetrial(cfg); % read in the data from the magnetometer % cfg.channel = {'MEGMAG','STI101'}; cfg.channel = {'MEG'};%,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};% % cfg.lpfilter = 'yes'; % cfg.lpfreq = 40; % cfg.hpfilter = 'yes'; % cfg.hpfreq = 1; cfg.continuous = 'yes'; cfg.detrend = 'no'; cfg.demean = 'yes'; cfg.dftfilter = 'yes'; % cfg.dftfreq =[50 100 150];% power line noise cfg.bpfreq = [1 48]; cfg.metric = 'zvalue'; cfg.layout = 'neuromag306all.lay'; cfg.baselinewindow = [-0.5 0]; data_MEG_filt = ft_preprocessing(cfg); if isempty(data_MEG_filt) data_MEG_filt=data; grad = data.grad; else cfg = []; data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data); end Any advice will be highly appreciated. Dr Pramod Gaur Research Assistant in Neuro-Imaging Technology School of Computing, Engineering and Intelligent Systems Magee Campus E: p.gaur at ulster.ac.uk W: www.ulster.ac.uk Social: Twitter: @SceisUni Facebook: @UlsterUniComputingEngineeringIntelligentSystems This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From p.gaur at ulster.ac.uk Mon Nov 19 23:04:29 2018 From: p.gaur at ulster.ac.uk (Gaur, Pramod) Date: Mon, 19 Nov 2018 22:04:29 +0000 Subject: [FieldTrip] Combine multiple blocks In-Reply-To: References: Message-ID: Hi Jan-Mathijs, I want to do source analysis. I have 3 blocks with 40 trials each for one subject and want to do the source analysis. Yes, you pointed it correctly “ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs.” Please advise me how to do the source analysis on them. Best regards, Pramod From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Schoffelen, J.M. (Jan Mathijs) Sent: 19 November 2018 19:51 To: FieldTrip discussion list Subject: Re: [FieldTrip] Combine multiple blocks Pramod, I don’t understand how ‘combining all the three blocks before the preprocessing’ would help you to preserve the channel positions. Apparently, these are different for each of the blocks in your case. It’s also not clear to me, why this is causes problem in your specific case. Anyway, ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs. If you still want to obtain some kind of ‘average’ representation of the channel position, you can use ft_average_sens. If the positions are not altogether too different, it might be OK to average the positions across blocks, although technically it’s of course incorrect to do so. Yet, if for each of the blocks you have applied block-specific spatial transformations (e.g. maxfilter) then it’s a different story altogether, and things will become hairy very rapidly. Best wishes, Jan-Mathijs On 19 Nov 2018, at 19:19, Gaur, Pramod > wrote: Dear Team, I have a quick concern, I have a cognitive task and recorded three blocks. Is there any way in which I can combine all the three blocks before the preprocessing. I have alternate to preprocess the blocks separately and then combining them. In this case, I lose the data_MEG_filt .grad.chanpos location. cfg = []; cfg.dataset = filename; % % cfg = ft_definetrial(cfg); cfg.trialdef.eventtype = 'STI101'; %cfg.trialdef.eventtype = 'gui'; cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own cfg.trialdef.prestim = prestim; cfg.trialdef.poststim = poststim; % cfg.length = 1; cfg = ft_definetrial(cfg); % read in the data from the magnetometer % cfg.channel = {'MEGMAG','STI101'}; cfg.channel = {'MEG'};%,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};% % cfg.lpfilter = 'yes'; % cfg.lpfreq = 40; % cfg.hpfilter = 'yes'; % cfg.hpfreq = 1; cfg.continuous = 'yes'; cfg.detrend = 'no'; cfg.demean = 'yes'; cfg.dftfilter = 'yes'; % cfg.dftfreq =[50 100 150];% power line noise cfg.bpfreq = [1 48]; cfg.metric = 'zvalue'; cfg.layout = 'neuromag306all.lay'; cfg.baselinewindow = [-0.5 0]; data_MEG_filt = ft_preprocessing(cfg); if isempty(data_MEG_filt) data_MEG_filt=data; grad = data.grad; else cfg = []; data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data); end Any advice will be highly appreciated. Dr Pramod Gaur Research Assistant in Neuro-Imaging Technology School of Computing, Engineering and Intelligent Systems Magee Campus E: p.gaur at ulster.ac.uk W: www.ulster.ac.uk Social: Twitter: @SceisUni Facebook: @UlsterUniComputingEngineeringIntelligentSystems This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Tue Nov 20 09:07:58 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Tue, 20 Nov 2018 08:07:58 +0000 Subject: [FieldTrip] Combine multiple blocks In-Reply-To: References: Message-ID: <1F1F8765-C52F-4B0C-A7E0-9AFC30E1ED69@donders.ru.nl> Pramod, Your queries are lacking detail: i.e. there is no detail as to what you actually want to do (e.g. type of source reconstruction etc.), nor any detail about what you have tried yourself so far. Therefore, it is hard to give constructive feedback/directions. I suggest to first try and get a running pipeline to do source reconstruction using data from a single block. Then it’s relatively straightforward to extend this to a multiple block setting, where the exact sensible directions to take depend on the source reconstruction algorithm and the quality of the data per block. One possibility would be, as I already mentioned, to recover a more or less useable grad structure, using ft_average_sens. Alternatively, you could do the source reconstruction per block, and combine afterwards. Good luck, Jan-Mathijs J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands On 19 Nov 2018, at 23:04, Gaur, Pramod > wrote: Hi Jan-Mathijs, I want to do source analysis. I have 3 blocks with 40 trials each for one subject and want to do the source analysis. Yes, you pointed it correctly “ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs.” Please advise me how to do the source analysis on them. Best regards, Pramod From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Schoffelen, J.M. (Jan Mathijs) Sent: 19 November 2018 19:51 To: FieldTrip discussion list > Subject: Re: [FieldTrip] Combine multiple blocks Pramod, I don’t understand how ‘combining all the three blocks before the preprocessing’ would help you to preserve the channel positions. Apparently, these are different for each of the blocks in your case. It’s also not clear to me, why this is causes problem in your specific case. Anyway, ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs. If you still want to obtain some kind of ‘average’ representation of the channel position, you can use ft_average_sens. If the positions are not altogether too different, it might be OK to average the positions across blocks, although technically it’s of course incorrect to do so. Yet, if for each of the blocks you have applied block-specific spatial transformations (e.g. maxfilter) then it’s a different story altogether, and things will become hairy very rapidly. Best wishes, Jan-Mathijs On 19 Nov 2018, at 19:19, Gaur, Pramod > wrote: Dear Team, I have a quick concern, I have a cognitive task and recorded three blocks. Is there any way in which I can combine all the three blocks before the preprocessing. I have alternate to preprocess the blocks separately and then combining them. In this case, I lose the data_MEG_filt .grad.chanpos location. cfg = []; cfg.dataset = filename; % % cfg = ft_definetrial(cfg); cfg.trialdef.eventtype = 'STI101'; %cfg.trialdef.eventtype = 'gui'; cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own cfg.trialdef.prestim = prestim; cfg.trialdef.poststim = poststim; % cfg.length = 1; cfg = ft_definetrial(cfg); % read in the data from the magnetometer % cfg.channel = {'MEGMAG','STI101'}; cfg.channel = {'MEG'};%,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};% % cfg.lpfilter = 'yes'; % cfg.lpfreq = 40; % cfg.hpfilter = 'yes'; % cfg.hpfreq = 1; cfg.continuous = 'yes'; cfg.detrend = 'no'; cfg.demean = 'yes'; cfg.dftfilter = 'yes'; % cfg.dftfreq =[50 100 150];% power line noise cfg.bpfreq = [1 48]; cfg.metric = 'zvalue'; cfg.layout = 'neuromag306all.lay'; cfg.baselinewindow = [-0.5 0]; data_MEG_filt = ft_preprocessing(cfg); if isempty(data_MEG_filt) data_MEG_filt=data; grad = data.grad; else cfg = []; data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data); end Any advice will be highly appreciated. Dr Pramod Gaur Research Assistant in Neuro-Imaging Technology School of Computing, Engineering and Intelligent Systems Magee Campus E: p.gaur at ulster.ac.uk W: www.ulster.ac.uk Social: Twitter: @SceisUni Facebook: @UlsterUniComputingEngineeringIntelligentSystems This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From stephen.whitmarsh at gmail.com Tue Nov 20 09:43:48 2018 From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh) Date: Tue, 20 Nov 2018 09:43:48 +0100 Subject: [FieldTrip] Combine multiple blocks In-Reply-To: <1F1F8765-C52F-4B0C-A7E0-9AFC30E1ED69@donders.ru.nl> References: <1F1F8765-C52F-4B0C-A7E0-9AFC30E1ED69@donders.ru.nl> Message-ID: Pramod, *If *you can use MaxFilter (on NeuroMag system), you can also consider using MaxFilter to artificially align sensors locations to sensor position of e.g. the first recording. See their manual for that. However, I agree with Jan-Mathijs that that's technically questionable and probably unnecessary, and that combining blocks e.g. on source level, if data quality permits, would be a more proper approach. Cheers, Stephen On Tue, 20 Nov 2018 at 09:36, Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > Pramod, > > Your queries are lacking detail: i.e. there is no detail as to what you > actually want to do (e.g. type of source reconstruction etc.), nor any > detail about what you have tried yourself so far. Therefore, it is hard to > give constructive feedback/directions. > > I suggest to first try and get a running pipeline to do source > reconstruction using data from a single block. Then it’s relatively > straightforward to extend this to a multiple block setting, where the exact > sensible directions to take depend on the source reconstruction algorithm > and the quality of the data per block. One possibility would be, as I > already mentioned, to recover a more or less useable grad structure, using > ft_average_sens. Alternatively, you could do the source reconstruction per > block, and combine afterwards. > > Good luck, > Jan-Mathijs > > > > J.M.Schoffelen, MD PhD > Senior Researcher, VIDI-fellow - PI, language in interaction > Telephone: +31-24-3614793 > Physical location: room 00.028 > Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands > > On 19 Nov 2018, at 23:04, Gaur, Pramod wrote: > > Hi Jan-Mathijs, > > I want to do source analysis. I have 3 blocks with 40 trials each for one > subject and want to do the source analysis. Yes, you pointed it correctly “*ft_appenddata > throws the grad-field away, since it recognizes that the channel (and coil) > positions are different in each of the runs.*” Please advise me how to do > the source analysis on them. > > Best regards, > Pramod > > *From:* fieldtrip [mailto:fieldtrip-bounces at science.ru.nl > ] *On Behalf Of *Schoffelen, J.M. (Jan > Mathijs) > *Sent:* 19 November 2018 19:51 > *To:* FieldTrip discussion list > *Subject:* Re: [FieldTrip] Combine multiple blocks > > Pramod, > > I don’t understand how ‘combining all the three blocks before the > preprocessing’ would help you to preserve the channel positions. > Apparently, these are different for each of the blocks in your case. It’s > also not clear to me, why this is causes problem in your specific case. > > Anyway, ft_appenddata throws the grad-field away, since it recognizes that > the channel (and coil) positions are different in each of the runs. > If you still want to obtain some kind of ‘average’ representation of the > channel position, you can use ft_average_sens. If the positions are not > altogether too different, it might be OK to average the positions across > blocks, although technically it’s of course incorrect to do so. Yet, if for > each of the blocks you have applied block-specific spatial transformations > (e.g. maxfilter) then it’s a different story altogether, and things will > become hairy very rapidly. > > Best wishes, > > Jan-Mathijs > > > > On 19 Nov 2018, at 19:19, Gaur, Pramod wrote: > > Dear Team, > > I have a quick concern, I have a cognitive task and recorded three blocks. > Is there any way in which I can combine all the three blocks before the > preprocessing. I have alternate to preprocess the blocks separately and > then combining them. In this case, I lose the data_MEG_filt > .grad.chanpos location. > > cfg = []; > cfg.dataset = filename; > % % cfg = ft_definetrial(cfg); > cfg.trialdef.eventtype = 'STI101'; > %cfg.trialdef.eventtype = 'gui'; > cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own > cfg.trialdef.prestim = prestim; > cfg.trialdef.poststim = poststim; > % cfg.length = 1; > cfg = ft_definetrial(cfg); > > % read in the data from the magnetometer > % cfg.channel = {'MEGMAG','STI101'}; > cfg.channel = {'MEG'}; > %,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};% > % cfg.lpfilter = 'yes'; > % cfg.lpfreq = 40; > % cfg.hpfilter = 'yes'; > % cfg.hpfreq = 1; > cfg.continuous = 'yes'; > cfg.detrend = 'no'; > cfg.demean = 'yes'; > cfg.dftfilter = 'yes'; > % cfg.dftfreq =[50 100 150];% power line noise > cfg.bpfreq = [1 48]; > cfg.metric = 'zvalue'; > cfg.layout = 'neuromag306all.lay'; > cfg.baselinewindow = [-0.5 0]; > data_MEG_filt = ft_preprocessing(cfg); > > if isempty(data_MEG_filt) > data_MEG_filt=data; > grad = data.grad; > else > cfg = []; > data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data); > end > > > Any advice will be highly appreciated. > > > > > *Dr Pramod Gaur* > Research Assistant in Neuro-Imaging Technology > > School of Computing, Engineering and Intelligent Systems > > Magee Campus > > *E:* p.gaur at ulster.ac.uk *W:* www.ulster.ac.uk > *Social:* Twitter: @SceisUni > Facebook: @UlsterUniComputingEngineeringIntelligentSystems > > > > > > > > > > > > > This email and any attachments are confidential and intended solely for > the use of the addressee and may contain information which is covered by > legal, professional or other privilege. If you have received this email in > error please notify the system manager at postmaster at ulster.ac.uk and > delete this email immediately. Any views or opinions expressed are solely > those of the author and do not necessarily represent those of Ulster > University. > The University's computer systems may be monitored and communications > carried out on them may be recorded to secure the effective operation of > the system and for other lawful purposes. Ulster University does not > guarantee that this email or any attachments are free from viruses or 100% > secure. Unless expressly stated in the body of a separate attachment, the > text of email is not intended to form a binding contract. Correspondence to > and from the University may be subject to requests for disclosure by 3rd > parties under relevant legislation. > *The Ulster University was founded by Royal Charter in 1984 and is > registered with company number RC000726 and VAT registered number > GB672390524.The primary contact address for Ulster University in Northern > Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA* > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > > > This email and any attachments are confidential and intended solely for > the use of the addressee and may contain information which is covered by > legal, professional or other privilege. If you have received this email in > error please notify the system manager at postmaster at ulster.ac.uk and > delete this email immediately. Any views or opinions expressed are solely > those of the author and do not necessarily represent those of Ulster > University. > The University's computer systems may be monitored and communications > carried out on them may be recorded to secure the effective operation of > the system and for other lawful purposes. Ulster University does not > guarantee that this email or any attachments are free from viruses or 100% > secure. Unless expressly stated in the body of a separate attachment, the > text of email is not intended to form a binding contract. Correspondence to > and from the University may be subject to requests for disclosure by 3rd > parties under relevant legislation. > *The Ulster University was founded by Royal Charter in 1984 and is > registered with company number RC000726 and VAT registered number > GB672390524.The primary contact address for Ulster University in Northern > Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA* > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Wed Nov 21 14:47:07 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Wed, 21 Nov 2018 13:47:07 +0000 Subject: [FieldTrip] code update: ft_timelockanalysis Message-ID: <5032C771-6BB8-4379-ADD3-C2557FD6F2C9@donders.ru.nl> Dear community, As you know, the FieldTrip development model is based on continuous incremental improvements, where changes are made to the code repository on github, sometimes multiple times per day. In our implicit code of conduct, one of our core values is to maintain backward compatibility. This means that we not only aim not to break properly functioning user scripts that have been written with a (not too outdated) slightly older version of the FieldTrip code, but also that we aim at function behavior being stable over time (unless we find a bug in the code), both in terms of settings of the default parameters, and in terms of numerical output. Occasionally, we decide to slightly sacrifice backward compatibility at the benefit of overall code consistency and maintainability. Usually, we don’t write an e-mail to the list when this happens, because everything is properly documented on github or bugzilla, and we don’t want to bother you with these mundane issues, but today I make an exception. The reason for this is that I re-implemented ft_timelockanalysis, which is one of the oldest FieldTrip functions and a loyal companion in many an analysis project over the past 15 years. Since I assume that this is a function that many of you use, I just want to make you aware of this. The most noticeable (if at all) changes are: 1) the option cfg.vartrllength has been deprecated. 2) the default behavior has changed from expecting the trials of the input data to have fixed length (and throwing an error otherwise) into full support of variable trial lengths, representing missing data as NaNs. 3) if you want to explicitly use the old option cfg.vartrllength=0 you should now specify cfg.latency = ‘minperiod’ 4) with the cfg.keeptrials you will either get an output structure with a single trial representation or with an average representation, not with both. This means that structures with both a ‘trial’, and ‘avg’ field will not be generated anymore. Probably, you won’t need to change anything in your scripts, but since it is difficult to foresee all possible scenarios you might want to think this over yourself. Happy computing, Jan-Mathijs J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands -------------- next part -------------- An HTML attachment was scrubbed... URL: From martin.rosenfelder at uni-ulm.de Wed Nov 21 16:14:39 2018 From: martin.rosenfelder at uni-ulm.de (Martin Rosenfelder) Date: Wed, 21 Nov 2018 16:14:39 +0100 Subject: [FieldTrip] Support vector machine Message-ID: <3b26-5bf57680-1-3f44a540@39160932> Dear fieldtrip community, I am using the DMLT toolbox' svm in order to classify EEG data in two different conditions. More precisely, the SVM is trained on trigger-based trials for a mental motor imagery condition and a resting state condition. The classifier works fine with accuracy rates between 50-80 %. In order to boost the performance of the classifier I would now like to look into the parameterization of the classifier, e.g. how the classifier is build. This includes decision rules the classifier uses in order to analyze the data I am feeding in. I was able to detect the SVM behind the ft_crossvalidate function but not how it works or how I can customize it. Is there a possibility to 'personalize' the SVM in the DMLT Toolbox? Thank you very much in advance for your help and effort! Best, Martin -- M.Sc.-Psych. Martin Rosenfelder Wissenschaftlicher Mitarbeiter Klinische und Biologische Psychologie Universität Ulm Raum 47.2.259 +49 731-50 26592 martin.rosenfelder at uni-ulm.de From S.Arana at donders.ru.nl Wed Nov 21 18:10:21 2018 From: S.Arana at donders.ru.nl (Arana, S.L. (Sophie)) Date: Wed, 21 Nov 2018 17:10:21 +0000 Subject: [FieldTrip] Support vector machine In-Reply-To: <3b26-5bf57680-1-3f44a540@39160932> References: <3b26-5bf57680-1-3f44a540@39160932> Message-ID: <1542820221153.94026@donders.ru.nl> Hi Martin, the options for svm as supported by the dml toolbox are limited to the properties of the svm class, that is weights, regularization, precomputed kernels and output (see fieldtrip/external/dmlt/+dml/svm.m) You can adjust those via the config you pass to ft_timelockstatistics, such as for example: cfg = []; cfg.method = 'crossvalidate'; cfg.type = 'nfold' ... cfg.mva = dml.analysis({dml.svm('C',lambda,'anyotheroption',xx}) out = ft_timelockstatistics(cfg...) Concerning the implementation, I am not using the svm bit of the toolbox myself, so I am by no means an expert but let me give it a shot. From what I see you can either by option 'native'=true use the svm as implemented in the Bioinformatics toolbox, otherwise the toolbox will compute a linear kernel and compute the classifier with quadratic loss as specified in fieldtrip/external/dmlt/external/svm/l2svm_cg. It seems there are more options implemented there for finding the weights but I'm afraid in order to get at those you would have to adjust the input arguments yourself in the svm.m code (i.e. line 70 & 75). Hope this helps a bit. Best, Sophie ___________________ M.Sc. Sophie L. Arana Doctoral researcher Neurobiology of Language - MPI for Psycholinguistics Max Planck Institute for Psycholinguistics PO Box 310, 6500 AH Nijmegen Netherlands T +31 24-3610887 E sophie.arana at mpi.nl ________________________________________ From: fieldtrip on behalf of Martin Rosenfelder Sent: Wednesday, November 21, 2018 4:14 PM To: fieldtrip at science.ru.nl Subject: [FieldTrip] Support vector machine Dear fieldtrip community, I am using the DMLT toolbox' svm in order to classify EEG data in two different conditions. More precisely, the SVM is trained on trigger-based trials for a mental motor imagery condition and a resting state condition. The classifier works fine with accuracy rates between 50-80 %. In order to boost the performance of the classifier I would now like to look into the parameterization of the classifier, e.g. how the classifier is build. This includes decision rules the classifier uses in order to analyze the data I am feeding in. I was able to detect the SVM behind the ft_crossvalidate function but not how it works or how I can customize it. Is there a possibility to 'personalize' the SVM in the DMLT Toolbox? Thank you very much in advance for your help and effort! Best, Martin -- M.Sc.-Psych. Martin Rosenfelder Wissenschaftlicher Mitarbeiter Klinische und Biologische Psychologie Universität Ulm Raum 47.2.259 +49 731-50 26592 martin.rosenfelder at uni-ulm.de _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 From nemethd at gmail.com Fri Nov 23 11:13:16 2018 From: nemethd at gmail.com (Dezso Nemeth) Date: Fri, 23 Nov 2018 11:13:16 +0100 Subject: [FieldTrip] Posdoc in Lyon, France In-Reply-To: References: Message-ID: *Postdoc in Cognitive Neuroscience* · CRNL – Center for Research in Neuroscience in Lyon · Published: 25-10-2018 · Closing date: 01-12-2018 · Contract: Fixed-term, 2,5 years · Start date: March 01, 2019 (flexible) *Job description* Applications are invited for a highly motivated, enthusiastic postdoctoral researcher with a PhD in cognitive neuroscience (or related field) to join a well-supported, friendly research team, based in the internationally renowned Center for Research in Neuroscience in Lyon (University of Lyon, CRNS, INSERM). The postdoctoral position is part of a research project named REWIRING that is funded by IDEXLYON Fellowship. The postdoc will be embedded in the IDEXLYON team (PI: Dezso Nemeth) at CRNL, Lyon. Using methods of M/EEG, fMRI and non-invasive brain stimulation (e.g., TMS), the project aims to investigate how memory representations can be updated ('rewired') in the human brain. More specifically, we will investigate the entire process of how statistical and sequential regularities are extracted from the environment (memory formation), how the extracted knowledge is consolidated and how it can be rewired. For more details see the publications of Dezso Nemeth and Karolina Janacsek at http://nemethlab.com/publications/, and particularly the following paper: Szegedi-Hallgató, E., Janacsek, K., Vékony, T., Tasi, L. A., Kerepes, L., Hompoth, E. A., ... & Németh, D. (2017). Explicit instructions and consolidation promote rewiring of automatic behaviors in the human mind. Scientific Reports, 7(1), 4365. The overall aim of Project REWIRING is to improve human learning and memory performance and boost rewiring of automatic behaviors. Within this project, the post-holder will be responsible for designing and carrying out experiments, analyzing data, and writing up manuscripts. Additionally, the postdoc will be closely involved in daily supervision of PhD and MSc students who work on the project. *Profile (Person specification)* Candidates who only partially meet the following profile are nonetheless strongly encouraged to apply! · PhD in cognitive neuroscience or an adjacent field (psychological, biological, biomedical, or computer sciences, also physics and mathematics); · A strong academic track record including publications in leading (inter)disciplinary journals; · A strong interest for fundamental research in cognitive neurosciences; · Advanced computational and/or programming skills (Matlab, Python, or other languages); · Experience in functional connectivity analysis (EEG, MEG or MRI); · Experience and interest in training and supervising junior scientists; · Capacity to participate in an interdisciplinary and international research environment; · Excellent interpersonal and communication skills to effectively collaborate and communicate in academia; · A proactive and goal-directed attitude, good organizational skills; · Fluency in written and spoken English and motivation to learn French. *Organization* The project is embedded in the unique and excellent infrastructure of the CRNL - Center for Research in Neuroscience in Lyon. Researchers working on this theme jointly organize regular discussion meetings and lectures to promote integration of research conducted within systems, behavioral, and cognitive neurosciences. Read more about what it means *to work at * CRNL. *Employment conditions* Salary will be in accordance with the relevant national labor agreement and based on research experience and qualifications. The earliest start date for this position is March 2018 (later start possible upon agreement). *Comments and contact information* *Application* We request applicants to send the following documents: 1) A cover letter briefly describing how their skills and experience meet the profile as set out in the person specification (max 1 page) 2) A research statement explaining their research interests in relation to Project REWIRING or to the PI’s publications (max 2 pages) (optional) 3) A recent CV and publication list 4) Two writing samples of the applicant's most significant work (published or unpublished manuscripts). 5) Contact information of three professional references. *Information* All additional information about the vacancy can be obtained from Dezso Nemeth, Principal Investigator, via nemeth at nemethlab.com. Submit your application to the following email address: hr at nemethlab.com *Please apply before December 1 (23:59 GMT).* *We are committed to building a diverse, equitable and inclusive scientific community.* *For this post, we particularly welcome applications by women and ethnic minorities.* *Recruitment agencies are asked not to respond to this job posting.* -------------------------------------- NEMETH, Dezso (PhD, DSc) Brain, Memory and Language Lab: http://www.memory-and-language.com Phone: +36-1-4614500/3565, +36-1-4614500/3519 -------------- next part -------------- An HTML attachment was scrubbed... URL: From aitor.martinezegurcegui at uzh.ch Mon Nov 26 12:00:08 2018 From: aitor.martinezegurcegui at uzh.ch (Aitor Egurtzegi) Date: Mon, 26 Nov 2018 12:00:08 +0100 Subject: [FieldTrip] segmenting and merging trial data Message-ID: <95bab51c-4397-b725-79e1-d2860100ccca@uzh.ch> Dear researchers at Fieldtrip, I have defined a very long time window for each trial with ft_preprocessing. I'm actually only interested in the very beginning and the end of the trial, so now I'm trying to segment my trials by using ft_redefinetrial and specifying the time window of interest with cfg.toilim, which I run in two steps, one for the beginning and the other for the end of the trial. My problem arises when I try to merge both segments together, because I tried the various ft_append functions but they do not merge my trials the way I want, so I would like to know if there's a way to put the data together again, or on the other hand if I can use a different approach, such as slicing and throwing away the part of the trial I'm not interested in. Hence, instead of taking two slices of data for the trials and merging them, discarding what's in between my time window of interest for each trial. Thanks in advance, Aitor From jonas at obleser.de Mon Nov 26 12:36:59 2018 From: jonas at obleser.de (Jonas Obleser) Date: Mon, 26 Nov 2018 12:36:59 +0100 Subject: [FieldTrip] =?utf-8?q?4-y_postdoc_opportunity_in_L=C3=BCbeck=2C_?= =?utf-8?q?Germany?= Message-ID: <3C4AADDB-DB6E-46E0-88AA-D3E17127E356@obleser.de> Dear colleagues, New Postdoc opportunity, starting April 1: Come do a 4-y Postdoc with us in University of Lübeck! Modellers and Causal-inference-folks should feel especially targeted. Besides our own EEG lab, a shared research MR Scanner, we have ample data to play with (and a few undergrads to teach stats to now and then). Link: https://tinyurl.com/obleserlab-postdoc-EN Best wishes, Jonas Jonas Obleser Professor Chair in Physiological Psychology and Research Methods University of Lübeck Department of Psychology MFC 8, Maria-Goeppert-Straße 9a 23562 Lübeck, Germany Phone +49 (0)451 3101 3620 Mobile +49 (0)171 6993337 jonas.obleser at uni-luebeck.de http://jonasobleser.com From Silvia.Formica at UGent.be Mon Nov 26 14:19:22 2018 From: Silvia.Formica at UGent.be (Silvia Formica) Date: Mon, 26 Nov 2018 13:19:22 +0000 Subject: [FieldTrip] ft_redefinedata after pre-processing Message-ID: <1543238361133.67318@UGent.be> ?Dear all, I'm in need of suggestions on the correct analyses strategy for my data. I have an experiment with a cue followed (after ~2 seconds) by a target. I epoched the data cue-locked and pre-processed my dataset. In my preprocessing I'm downsampling, re-referencing, rejecting some trials, running ICA, etc etc. My problem is that I would want to also epoch my data target-locked, but I want to do it after pre-processing the cue-locked dataset. This is because I want to exclude the same trials and apply exactly the same corrections to the two different datasets. I tried to use the function ft_redefinetrial, creating the new probe-locked trial definition and applying it to the cue-locked dataset. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) But I do not obtain what I wished for, because I get a data_target dataset that doesn't account for the trial rejection I performed while pre-processing the cue-locked dataset. So my questions are: 1) is it possible to achieve my goal? 2) If yes, what is the right strategy to do it? Thanks for any input and suggestion! Best, Silvia -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Mon Nov 26 15:43:27 2018 From: christine.blume at sbg.ac.at (Blume Christine) Date: Mon, 26 Nov 2018 14:43:27 +0000 Subject: [FieldTrip] ft_redefinedata after pre-processing In-Reply-To: <1543238361133.67318@UGent.be> References: <1543238361133.67318@UGent.be> Message-ID: <257c087109ac4b95a365d036a6971f15@sbg.ac.at> Dear Silvia, Do I understand correctly that data_cue is epoched, but not pre-processed? You can apply ft_rejectartifact after ft_redefinetrial. We usually pre-process continuous datasets and save the cfg of this pre-processing (in the example below cfg_myartifactrejection). Only then we epoch the data and apply this artifactrejection. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) cfg = []; data_target_clean = ft_rejectartifact(cfg_myartifactrejection, data_target); I hope this helps… Best, Christine Von: fieldtrip Im Auftrag von Silvia Formica Gesendet: Montag, 26. November 2018 14:19 An: fieldtrip at science.ru.nl Betreff: [FieldTrip] ft_redefinedata after pre-processing ​Dear all, I'm in need of suggestions on the correct analyses strategy for my data. I have an experiment with a cue followed (after ~2 seconds) by a target. I epoched the data cue-locked and pre-processed my dataset. In my preprocessing I'm downsampling, re-referencing, rejecting some trials, running ICA, etc etc. My problem is that I would want to also epoch my data target-locked, but I want to do it after pre-processing the cue-locked dataset. This is because I want to exclude the same trials and apply exactly the same corrections to the two different datasets. I tried to use the function ft_redefinetrial, creating the new probe-locked trial definition and applying it to the cue-locked dataset. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) But I do not obtain what I wished for, because I get a data_target dataset that doesn't account for the trial rejection I performed while pre-processing the cue-locked dataset. So my questions are: 1) is it possible to achieve my goal? 2) If yes, what is the right strategy to do it? Thanks for any input and suggestion! Best, Silvia -------------- next part -------------- An HTML attachment was scrubbed... URL: From Silvia.Formica at UGent.be Mon Nov 26 16:11:48 2018 From: Silvia.Formica at UGent.be (Silvia Formica) Date: Mon, 26 Nov 2018 15:11:48 +0000 Subject: [FieldTrip] ft_redefinedata after pre-processing In-Reply-To: <257c087109ac4b95a365d036a6971f15@sbg.ac.at> References: <1543238361133.67318@UGent.be>, <257c087109ac4b95a365d036a6971f15@sbg.ac.at> Message-ID: <1543245106825.84804@UGent.be> Dear Christine, thank you for your quick response! In my example data_cue is epoched and pre-processed. Nevertheless, I think the approach you suggested might work also in my case. I also downsample, re-reference and filter the continuous data. After these first steps, I epoch the cue-locked dataset. After epoching, I perform artifact rejection and ICA. I guess I can apply your approach to my data by running this exact same pipeline and saving the "cfg_artifactrejection" and my ICA components. Then I would go back to the filtered continuous data, epoch them target-locked and apply to this target-locked dataset the "cfg_artifactrejection"? saved before and removing the same ICA components selected before. Do you think this would be an acceptable approach? Thanks a lot! Silvia ________________________________ From: fieldtrip on behalf of Blume Christine Sent: 26 November 2018 15:43 To: fieldtrip at science.ru.nl Subject: Re: [FieldTrip] ft_redefinedata after pre-processing Dear Silvia, Do I understand correctly that data_cue is epoched, but not pre-processed? You can apply ft_rejectartifact after ft_redefinetrial. We usually pre-process continuous datasets and save the cfg of this pre-processing (in the example below cfg_myartifactrejection). Only then we epoch the data and apply this artifactrejection. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) cfg = []; data_target_clean = ft_rejectartifact(cfg_myartifactrejection, data_target); I hope this helps... Best, Christine Von: fieldtrip Im Auftrag von Silvia Formica Gesendet: Montag, 26. November 2018 14:19 An: fieldtrip at science.ru.nl Betreff: [FieldTrip] ft_redefinedata after pre-processing ?Dear all, I'm in need of suggestions on the correct analyses strategy for my data. I have an experiment with a cue followed (after ~2 seconds) by a target. I epoched the data cue-locked and pre-processed my dataset. In my preprocessing I'm downsampling, re-referencing, rejecting some trials, running ICA, etc etc. My problem is that I would want to also epoch my data target-locked, but I want to do it after pre-processing the cue-locked dataset. This is because I want to exclude the same trials and apply exactly the same corrections to the two different datasets. I tried to use the function ft_redefinetrial, creating the new probe-locked trial definition and applying it to the cue-locked dataset. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) But I do not obtain what I wished for, because I get a data_target dataset that doesn't account for the trial rejection I performed while pre-processing the cue-locked dataset. So my questions are: 1) is it possible to achieve my goal? 2) If yes, what is the right strategy to do it? Thanks for any input and suggestion! Best, Silvia -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Mon Nov 26 18:30:47 2018 From: christine.blume at sbg.ac.at (Blume Christine) Date: Mon, 26 Nov 2018 17:30:47 +0000 Subject: [FieldTrip] ft_redefinedata after pre-processing In-Reply-To: <1543245106825.84804@UGent.be> References: <1543238361133.67318@UGent.be>, <257c087109ac4b95a365d036a6971f15@sbg.ac.at> <1543245106825.84804@UGent.be> Message-ID: Dear Silvia, What you could do is do your downsampling, re-referencing, filtering and ICA on continuous data and save the resulting dataset as data1. Then, you do the artefact rejection on continuous data (unless you have long episodes that are of no interest, then you may reconsider) and save this to cfg_artifactrejection. That way, you obtain an artefact rejection file that is independent from epoching, sometimes not the worst idea. Then you go back and load data1, do the epoching, and then do the artefact rejection. Best, Christine Von: fieldtrip Im Auftrag von Silvia Formica Gesendet: Montag, 26. November 2018 16:12 An: FieldTrip discussion list Betreff: Re: [FieldTrip] ft_redefinedata after pre-processing Dear Christine, thank you for your quick response! In my example data_cue is epoched and pre-processed. Nevertheless, I think the approach you suggested might work also in my case. I also downsample, re-reference and filter the continuous data. After these first steps, I epoch the cue-locked dataset. After epoching, I perform artifact rejection and ICA. I guess I can apply your approach to my data by running this exact same pipeline and saving the "cfg_artifactrejection" and my ICA components. Then I would go back to the filtered continuous data, epoch them target-locked and apply to this target-locked dataset the "cfg_artifactrejection"​ saved before and removing the same ICA components selected before. Do you think this would be an acceptable approach? Thanks a lot! Silvia ________________________________ From: fieldtrip > on behalf of Blume Christine > Sent: 26 November 2018 15:43 To: fieldtrip at science.ru.nl Subject: Re: [FieldTrip] ft_redefinedata after pre-processing Dear Silvia, Do I understand correctly that data_cue is epoched, but not pre-processed? You can apply ft_rejectartifact after ft_redefinetrial. We usually pre-process continuous datasets and save the cfg of this pre-processing (in the example below cfg_myartifactrejection). Only then we epoch the data and apply this artifactrejection. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) cfg = []; data_target_clean = ft_rejectartifact(cfg_myartifactrejection, data_target); I hope this helps… Best, Christine Von: fieldtrip > Im Auftrag von Silvia Formica Gesendet: Montag, 26. November 2018 14:19 An: fieldtrip at science.ru.nl Betreff: [FieldTrip] ft_redefinedata after pre-processing ​Dear all, I'm in need of suggestions on the correct analyses strategy for my data. I have an experiment with a cue followed (after ~2 seconds) by a target. I epoched the data cue-locked and pre-processed my dataset. In my preprocessing I'm downsampling, re-referencing, rejecting some trials, running ICA, etc etc. My problem is that I would want to also epoch my data target-locked, but I want to do it after pre-processing the cue-locked dataset. This is because I want to exclude the same trials and apply exactly the same corrections to the two different datasets. I tried to use the function ft_redefinetrial, creating the new probe-locked trial definition and applying it to the cue-locked dataset. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) But I do not obtain what I wished for, because I get a data_target dataset that doesn't account for the trial rejection I performed while pre-processing the cue-locked dataset. So my questions are: 1) is it possible to achieve my goal? 2) If yes, what is the right strategy to do it? Thanks for any input and suggestion! Best, Silvia -------------- next part -------------- An HTML attachment was scrubbed... URL: From junho0525 at gmail.com Tue Nov 27 13:18:27 2018 From: junho0525 at gmail.com (=?UTF-8?B?7IaQ7KSA7Zi4?=) Date: Tue, 27 Nov 2018 21:18:27 +0900 Subject: [FieldTrip] Predefined Source Orientations for Source Localization In-Reply-To: References: Message-ID: Dear fieldtrip community, I am currently trying to get source time series form my MEG data using beamformer LCMV. I have run ft_sourceanalysis with fixed orientation option, and I found that the estimated source orientations do not perpendicular to source surface. I thought that dendrites of pyramidal cells point perpendicular directions to the cortical surface, but the result was different from what I expected. Then I have tried to provide predefined orientations so that I use the provided orientations rather than estimate them. However, I also failed to get orientations that I expected. So, here is my question, 1) Why estimated source orientation does not perpendicular to source surface? 2) Are there any methods or options that use predefined source orientations for source localization? 3) If it is natural to get sources that are not perpendicular to the cortical surface, how can I explain those source activities in terms of neuronal structure and neuronal activity? Thank you, Junho -------------- next part -------------- An HTML attachment was scrubbed... URL: From aitor.martinezegurcegui at uzh.ch Wed Nov 28 11:49:06 2018 From: aitor.martinezegurcegui at uzh.ch (Aitor Egurtzegi) Date: Wed, 28 Nov 2018 11:49:06 +0100 Subject: [FieldTrip] automatic IC rejection Message-ID: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> Dear researchers at Fieldtrip, In order to make my work more reproducible, I would like to automatically reject ICs instead of doing visual inspection and rejection of the components. Unfortunately, I haven't found any documentation for such thing. Is there a way to do it in Fieldtrip? Best, Aitor From julian.keil at gmail.com Wed Nov 28 12:52:26 2018 From: julian.keil at gmail.com (Julian Keil) Date: Wed, 28 Nov 2018 12:52:26 +0100 Subject: [FieldTrip] automatic IC rejection In-Reply-To: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> References: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> Message-ID: Dear Aitor, irrespective of whether that is a good idea, I can suggest the EEGLab Toolbox SASICA (https://github.com/dnacombo/SASICA/ ). This toolbox can identify ICs based on a number of criteria and automatically reject these. So, if you manage to transform your FT-data into an EEGLab-like structure, this might work for you. Best, Julian > Am 28.11.2018 um 11:49 schrieb Aitor Egurtzegi : > > Dear researchers at Fieldtrip, > > > In order to make my work more reproducible, I would like to automatically reject ICs instead of doing visual inspection and rejection of the components. Unfortunately, I haven't found any documentation for such thing. Is there a way to do it in Fieldtrip? > > Best, > Aitor > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From david.schubring at uni-konstanz.de Wed Nov 28 13:47:00 2018 From: david.schubring at uni-konstanz.de (David Schubring) Date: Wed, 28 Nov 2018 12:47:00 +0000 Subject: [FieldTrip] automatic IC rejection In-Reply-To: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> References: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> Message-ID: <9b277ef4-067a-9a61-a11b-da7d504b9717@uni-konstanz.de> Dear Aitor, the closest thing I know of for a data-driven approach of selecting independent components is COMPASS, quote: "COMPASS is a MATLAB and EEGLAB based algorithm with the purpose of providing the user with a convenient technique for automatic Independent Component (IC) selection with respect to the contributions of the ICs to a certain ERP." Link to the toolbox: http://53450283.de.strato-hosting.eu/jrw/lab/e_compass.htm Paper: Wessel, J. R., & Ullsperger, M. (2011). Selection of independent components representing event-related brain potentials: a data-driven approach for greater objectivity. Neuroimage, 54(3), 2105-2115. https://doi.org/10.1016/j.neuroimage.2010.10.033 I have only theoretical experience with the toolbox as I only learned about it in a workshop and did not yet have the time to test and implement it in my personal FieldTrip workflow (even though it is on my ever growing to-do list). So far it looked like a useful thing to try out to me, especially as code can better be reproduced than "personal judgement". Best, David Am 28.11.2018 um 10:49 schrieb Aitor Egurtzegi: > Dear researchers at Fieldtrip, > > > In order to make my work more reproducible, I would like to > automatically reject ICs instead of doing visual inspection and > rejection of the components. Unfortunately, I haven't found any > documentation for such thing. Is there a way to do it in Fieldtrip? > > Best, > Aitor > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -- Dr. David Schubring General & Biological Psychology University of Konstanz Room C524 P.O. Box 36 78457 Konstanz Phone: +49-(0)7531-88-5350 Homepage: https://gpbp.uni-konstanz.de/people-page/david-schubring From MatthiasCMoeller at gmx.de Wed Nov 28 15:15:41 2018 From: MatthiasCMoeller at gmx.de (=?UTF-8?Q?=22Matthias_M=C3=B6ller=22?=) Date: Wed, 28 Nov 2018 15:15:41 +0100 Subject: [FieldTrip] Spike artifacts in EEG, 7-12Hz Message-ID: An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: 9 Hz Artefact.JPG Type: image/jpeg Size: 492043 bytes Desc: not available URL: From christine.blume at sbg.ac.at Wed Nov 28 16:04:32 2018 From: christine.blume at sbg.ac.at (Blume Christine) Date: Wed, 28 Nov 2018 15:04:32 +0000 Subject: [FieldTrip] Spike artifacts in EEG, 7-12Hz In-Reply-To: References: Message-ID: <07979b73a31a4cb5bd02398211e36ec6@sbg.ac.at> Dear Matthias, Admittedly, I do not know what this could be. While the first step should of course be to find the source and eliminate this (any devices in the EEG lab, artefact from the acoustic stimulation/headphones, …), in case you are unable to find it, you could remove the component(s) that correspond to the artefact. But as I said, the first goal should always be to record clean data… Best, Christine Von: fieldtrip Im Auftrag von "Matthias Möller" Gesendet: Mittwoch, 28. November 2018 15:16 An: fieldtrip at science.ru.nl Betreff: [FieldTrip] Spike artifacts in EEG, 7-12Hz Dear all, my name is Matthias and I am relatively new to the field of EEG analysis. I'm currently carrying out a study on the effects of natural sounds on the quantitative EEG in patients with Parkinson's disease at the universities of Vanvouver and Marburg. Right now I'm experiencing these weird artifacts as seen in the screenshot. It's sharp spikes, looking similar to ECG artifacts, but in frequencies of 7-12Hz. They are also showing up in the independent components after ICA as well. The recording was done at a sampling rate of 500 Hz, I've applied a Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz (recording took place in Canada). Has anyone seem similar artifacts before and maybe even knows how to get rid of them? Many thanks in advance and all the best, Matthias -------------- next part -------------- An HTML attachment was scrubbed... URL: From koculak.marcin at gmail.com Thu Nov 29 00:19:13 2018 From: koculak.marcin at gmail.com (Marcin Koculak) Date: Thu, 29 Nov 2018 00:19:13 +0100 Subject: [FieldTrip] Spike artifacts in EEG, 7-12Hz In-Reply-To: <07979b73a31a4cb5bd02398211e36ec6@sbg.ac.at> References: <07979b73a31a4cb5bd02398211e36ec6@sbg.ac.at> Message-ID: Dear Matthias, I have never seen such artifacts, but if you are working with patients with Parkinson's, have you checked if they have deep brain stimulation devices? Maybe that is causing the artifacts in the data? best, Marcin śr., 28 lis 2018 o 16:11 Blume Christine napisał(a): > Dear Matthias, > > > > Admittedly, I do not know what this could be. While the first step should > of course be to find the source and eliminate this (any devices in the EEG > lab, artefact from the acoustic stimulation/headphones, …), in case you are > unable to find it, you could remove the component(s) that correspond to the > artefact. But as I said, the first goal should always be to record clean > data… > > > > Best, > > Christine > > > > > > *Von:* fieldtrip *Im Auftrag von *"Matthias > Möller" > *Gesendet:* Mittwoch, 28. November 2018 15:16 > *An:* fieldtrip at science.ru.nl > *Betreff:* [FieldTrip] Spike artifacts in EEG, 7-12Hz > > > > Dear all, > > > > my name is Matthias and I am relatively new to the field of EEG analysis. > I'm currently carrying out a study on the effects of natural sounds on the > quantitative EEG in patients with Parkinson's disease at the universities > of Vanvouver and Marburg. > > > > Right now I'm experiencing these weird artifacts as seen in the > screenshot. It's sharp spikes, looking similar to ECG artifacts, but in > frequencies of 7-12Hz. > > They are also showing up in the independent components after ICA as well. > > > > The recording was done at a sampling rate of 500 Hz, I've applied a > Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz > (recording took place in Canada). > > > > Has anyone seem similar artifacts before and maybe even knows how to get > rid of them? > > > > Many thanks in advance and all the best, > > > > Matthias > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From anne.hauswald at me.com Thu Nov 29 09:26:20 2018 From: anne.hauswald at me.com (anne Hauswald) Date: Thu, 29 Nov 2018 09:26:20 +0100 Subject: [FieldTrip] Spike artifacts in EEG, 7-12Hz In-Reply-To: References: Message-ID: Dear Matthias, I don’t know where your artifacts come from, but I have some suggestions that might help you getting close to the source. - is it transient or do you find it throughout the whole recording? - do you find it in more than one recording? - does your choice of reference or filtering influence it? Not sure it will lead to something, but at least you will have a better understanding of this artifact. Best Anne > Am 28.11.2018 um 15:15 schrieb Matthias Möller : > > Dear all, > > my name is Matthias and I am relatively new to the field of EEG analysis. I'm currently carrying out a study on the effects of natural sounds on the quantitative EEG in patients with Parkinson's disease at the universities of Vanvouver and Marburg. > > Right now I'm experiencing these weird artifacts as seen in the screenshot. It's sharp spikes, looking similar to ECG artifacts, but in frequencies of 7-12Hz. > They are also showing up in the independent components after ICA as well. > > The recording was done at a sampling rate of 500 Hz, I've applied a Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz (recording took place in Canada). > > Has anyone seem similar artifacts before and maybe even knows how to get rid of them? > > Many thanks in advance and all the best, > > Matthias > <9 Hz Artefact.JPG>_______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From uwe.graichen at tu-ilmenau.de Thu Nov 29 10:08:29 2018 From: uwe.graichen at tu-ilmenau.de (Uwe Graichen) Date: Thu, 29 Nov 2018 10:08:29 +0100 Subject: [FieldTrip] =?utf-8?q?15_PhD_positions_in_Marie_Slodowskwa-Curie?= =?utf-8?q?_Innovative_Training_Network_=E2=80=9CINFANS=22?= In-Reply-To: <36f6a451-912e-ecfe-8796-7bde4fb9ff8e@tu-ilmenau.de> References: <36f6a451-912e-ecfe-8796-7bde4fb9ff8e@tu-ilmenau.de> Message-ID: As part of the Marie Skłodowska-Curie Innovative Training Network “INFANS - INtegrating Functional Assessment measures for Neonatal Safeguard" http://www.infansproject.eu , funded by the European Union’s Horizon 2020 Research and Innovation Programme, we advertise 15 PhD positions. The goal of INFANS is to develop a new neonatal brain monitoring system, designed to overcome the severe shortage of clinically viable means to high quality monitor the brain function in infancy, crucial to prevent later life neurological, cognitive and motor impairment. To accomplish this goal, INFANS established a structured European PhD training programme in biomedical engineering, signal processing and clinical procedures to train a new generation of creative and entrepreneurial young researchers. The individual research projects of the early stage researchers (ESR) encompass the topics: technological innovation, industrial development, clinical validation, identification of neonatal healthcare needs. As part of their research the INFANS ESRs will develop a novel platform for high quality, clinically-viable EEG-NIRS monitoring accessible worldwide. Well-targeted visits and secondments, soft skills and dynamic training activities, an Open Science strategy, extensive involvement of ESRs in the network events organization, extensive contacts with other research, training and industrial European networks, dissemination activities and the award of Double doctoral degrees are further assets offered to INFANS ESRs. Excellent science, industrial leadership and societal challenge are merged in the INFANS network. The INFANS consortium includes 6 academic and 4 non-academic partners from 6 EU countries, among which leading universities, companies and clinical institutions. The partners involved in INFANS share complementary expertise and facilities to provide international, interdisciplinary and intersectoral research training and mobility that will complement local doctoral training. The INFANS ESRs will become independent researchers with improved career prospects in both the academic and non-academic sectors, and will advance the EU capacity for innovation in biomedical engineering. The institution and the place where the different ESR projects will be carried out, the scientific supervisor(s), individual research project titles, and contact person for each available position can be found specified in the attached document. -------------- next part -------------- A non-text attachment was scrubbed... Name: ITN_INFANS Open_Position_20181129.pdf Type: application/pdf Size: 125155 bytes Desc: not available URL: From ablenkmann at gmail.com Thu Nov 29 15:30:27 2018 From: ablenkmann at gmail.com (Alejandro Blenkmann) Date: Thu, 29 Nov 2018 15:30:27 +0100 Subject: [FieldTrip] Fwd: FW: [all] 5 PhDs and 5 Postdoc fellowships (University of Oslo) In-Reply-To: References: Message-ID: Dear all, A new call for postdocs and PhDs is open at RITMO Center in Oslo. See information below, Best, Alejandro *From:* Alexander Refsum Jensenius *Sent:* Wednesday, November 21, 2018 10:01 PM *To:* all at ritmo.uio.no *Subject:* [all] 5 PhDs and 5 Postdoc fellowships (University of Oslo) Dear all, We are happy to announce a total of 10 recruit positions at RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion at the University of Oslo, Norway. 5 PhD fellowships in: · Rhythmic Robotics · Rhythm and Temporality in Audiovisual Narrative Media · Cognitive Neuroscience · Cross-modal Rhythms · Entrainment and Pleasure 5 postdoc fellowships in · Rhythmic Robotics · Eye Tracking and Motion Capture of rhythm-related behavior · Music, Time, and Consciousness · Electrophysiological basis of rhythm perception and production · fMRI RITMO is a Centre of Excellence funded by the Research Council of Norway, and focuses on rhythm as a structuring mechanism for temporal dimensions of human life. RITMO researchers work in a unique interdisciplinary constellation, with world-leading competence in musicology, psychology and informatics. The researchers have access to state-of-the-art facilities in sound/video recording, motion capture, eye tracking, physiological measurements, various types of brain imaging (EEG, fMRI), and rapid prototyping and robotics laboratories. · Application deadlines: 15 January / 15 March 2019 (check position) · Start-dates: August 2019 Please forward to relevant candidates. Apologies for cross-posting. *In addition: we are happy to host Marie Curie fellowship applications. Please get in touch if you are interested. * Best, -- Alexander Refsum Jensenius, Ph.D. Associate Professor, Department of Musicology, University of Oslo http://people.uio.no/alexanje/ Deputy Director, RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion http://www.uio.no/ritmo/ New book: "A NIME Reader" http://link.springer.com/book/10.1007/978-3-319-47214-0 New master's programme: " Music, Communication & Technology" http://www.uio.no/mct-master/ -- Alejandro Blenkmann, PhD Postdoctoral Fellow Front Neurolab Department of Psychology University of Oslo -- -------------- next part -------------- An HTML attachment was scrubbed... URL: From MatthiasCMoeller at gmx.de Thu Nov 29 16:55:26 2018 From: MatthiasCMoeller at gmx.de (=?UTF-8?Q?=22Matthias_M=C3=B6ller=22?=) Date: Thu, 29 Nov 2018 16:55:26 +0100 Subject: [FieldTrip] Spike artifacts 7-12Hz In-Reply-To: References: Message-ID: An HTML attachment was scrubbed... URL: From Hoang.Truong at colorado.edu Thu Nov 29 18:22:11 2018 From: Hoang.Truong at colorado.edu (Hoang Truong) Date: Thu, 29 Nov 2018 10:22:11 -0700 Subject: [FieldTrip] HELP: import simple data from txt file - non supported format, no header Message-ID: Dear community, This is Tony, from CS Department, University of Colorado Boulder. I am new to eeg analysis and fieldtrip. Currently I work on recording eeg and emg data using our own hardware with 2 eeg and 2 emg channels respectively. Since we use our custom hardware, the data are saved in a simple format of txt file with 7 columns (example data on Dropbox: link ): 3 first columns are data from environment sensors, the next 4 are eeg and emg data (250Hz sampling rate, 42s recording) I would like to ask what will be a proper way for me to import this data into fieldtrip. I checked the faq about importing custom data but I do not understand how to make custom readheader, readdata and readevent function. Any help would be appreciated (suggestions, code examples, etc). Thank you so much. Sincerely, Tony -------------- next part -------------- An HTML attachment was scrubbed... URL: From stephen.whitmarsh at gmail.com Thu Nov 29 19:37:11 2018 From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh) Date: Thu, 29 Nov 2018 19:37:11 +0100 Subject: [FieldTrip] HELP: import simple data from txt file - non supported format, no header In-Reply-To: References: Message-ID: Hi Tony, As long as you are able to load the data into MATLAB, you should be able to put it into a MATLAB structure that is in accordance with how FieldTrip expect data to be organized. See the second paragraph "Circumvent the FieldTrip reading functions" of the FAQ you referred to, and this FAQ on the data structures. It will be something like (syntax is probably wrong): % read from file mydatafromfile = tdfread('mydata.txt'); % check the help of the read function, not sure how this goes exactly % datastructure of raw data, i.e. not epoched mydata = []; mydata.label = {'env1','env2','env3','eeg1','eeg2','emg'}; % label for every column of data mydata.trial{1} = mydatafromfile; % data in columns, channels in rows mydata.time{1} = (1:size(mydatafromfile,1)) * 1/250; % create fake time-axis, should be same length as data If you are not able to program in MATLAB (yet), FieldTrip might not (yet) be for you. In that case you can take a look at other MATLAB based software such as e.g. EEGlab or Brainstorm , which use GUIs for importing data that might help (I'm not familiar with them though). Out of curiosity, what hardware are you using? Build it yourself? Good luck, Stephen On Thu, 29 Nov 2018 at 18:44, Hoang Truong wrote: > Dear community, > > This is Tony, from CS Department, University of Colorado Boulder. I am new > to eeg analysis and fieldtrip. Currently I work on recording eeg and emg > data using our own hardware with 2 eeg and 2 emg channels respectively. > Since we use our custom hardware, the data are saved in a simple format of > txt file with 7 columns (example data on Dropbox: link > ): 3 > first columns are data from environment sensors, the next 4 are eeg and emg > data (250Hz sampling rate, 42s recording) > I would like to ask what will be a proper way for me to import this data > into fieldtrip. I checked the faq about importing custom data but I do not > understand how to make custom readheader, readdata and readevent function. > Any help would be appreciated (suggestions, code examples, etc). > Thank you so much. > > Sincerely, > Tony > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Thu Nov 29 21:56:27 2018 From: christine.blume at sbg.ac.at (Blume Christine) Date: Thu, 29 Nov 2018 20:56:27 +0000 Subject: [FieldTrip] Spike artifacts 7-12Hz In-Reply-To: References: , Message-ID: Dear Matthias, I would guess that if the ICA nicely removes this component that might be the best approach, it is also commonly used to remove eye blinks or ECG artefacts from data. You can also try to design your own filter if the artefact is really centered around 9 Hz, although filtering is an issue on its own. Hope this helps! Christine ________________________________ Von: fieldtrip im Auftrag von "Matthias Möller" Gesendet: Donnerstag, 29. November 2018 16:55:26 An: fieldtrip at science.ru.nl Betreff: Re: [FieldTrip] Spike artifacts 7-12Hz Dear all, thanks a lot for your prompt answers, that is really supportive, thank you! Testing is already done, so I can't test for devices anymore whether they produce artifacts or not. To be honest I'm running out of ideas which devices might have been responsible. I used bluetooth headphones where the frequency is a lot higher, and in some sets I don't have the artifacts although subjects were stimulated using the same headphones. It's not in all recordings but in some. If it's in the recording, then it is throughout the whole one. It's not influenced by referencing or filtering. Deep brain stimulation seems to be to high in frequency as well. For now the only thing I can do is to remove the respective components indeed. Does anyone else maybe have an idea about how to filter out/get rid of those artifacts? Best, Matthias Gesendet: Donnerstag, 29. November 2018 um 10:08 Uhr Von: fieldtrip-request at science.ru.nl An: fieldtrip at science.ru.nl Betreff: fieldtrip Digest, Vol 96, Issue 22 Send fieldtrip mailing list submissions to fieldtrip at science.ru.nl To subscribe or unsubscribe via the World Wide Web, visit https://mailman.science.ru.nl/mailman/listinfo/fieldtrip or, via email, send a message with subject or body 'help' to fieldtrip-request at science.ru.nl You can reach the person managing the list at fieldtrip-owner at science.ru.nl When replying, please edit your Subject line so it is more specific than "Re: Contents of fieldtrip digest..." Today's Topics: 1. Re: Spike artifacts in EEG, 7-12Hz (Blume Christine) 2. Re: Spike artifacts in EEG, 7-12Hz (Marcin Koculak) 3. Re: Spike artifacts in EEG, 7-12Hz (anne Hauswald) 4. 15 PhD positions in Marie Slodowskwa-Curie Innovative Training Network “INFANS" (Uwe Graichen) ---------------------------------------------------------------------- Message: 1 Date: Wed, 28 Nov 2018 15:04:32 +0000 From: Blume Christine To: "fieldtrip at science.ru.nl" Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz Message-ID: <07979b73a31a4cb5bd02398211e36ec6 at sbg.ac.at> Content-Type: text/plain; charset="utf-8" Dear Matthias, Admittedly, I do not know what this could be. While the first step should of course be to find the source and eliminate this (any devices in the EEG lab, artefact from the acoustic stimulation/headphones, …), in case you are unable to find it, you could remove the component(s) that correspond to the artefact. But as I said, the first goal should always be to record clean data… Best, Christine Von: fieldtrip Im Auftrag von "Matthias Möller" Gesendet: Mittwoch, 28. November 2018 15:16 An: fieldtrip at science.ru.nl Betreff: [FieldTrip] Spike artifacts in EEG, 7-12Hz Dear all, my name is Matthias and I am relatively new to the field of EEG analysis. I'm currently carrying out a study on the effects of natural sounds on the quantitative EEG in patients with Parkinson's disease at the universities of Vanvouver and Marburg. Right now I'm experiencing these weird artifacts as seen in the screenshot. It's sharp spikes, looking similar to ECG artifacts, but in frequencies of 7-12Hz. They are also showing up in the independent components after ICA as well. The recording was done at a sampling rate of 500 Hz, I've applied a Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz (recording took place in Canada). Has anyone seem similar artifacts before and maybe even knows how to get rid of them? Many thanks in advance and all the best, Matthias -------------- next part -------------- An HTML attachment was scrubbed... URL: ------------------------------ Message: 2 Date: Thu, 29 Nov 2018 00:19:13 +0100 From: Marcin Koculak To: fieldtrip at science.ru.nl Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz Message-ID: Content-Type: text/plain; charset="utf-8" Dear Matthias, I have never seen such artifacts, but if you are working with patients with Parkinson's, have you checked if they have deep brain stimulation devices? Maybe that is causing the artifacts in the data? best, Marcin śr., 28 lis 2018 o 16:11 Blume Christine napisał(a): > Dear Matthias, > > > > Admittedly, I do not know what this could be. While the first step should > of course be to find the source and eliminate this (any devices in the EEG > lab, artefact from the acoustic stimulation/headphones, …), in case you are > unable to find it, you could remove the component(s) that correspond to the > artefact. But as I said, the first goal should always be to record clean > data… > > > > Best, > > Christine > > > > > > *Von:* fieldtrip *Im Auftrag von *"Matthias > Möller" > *Gesendet:* Mittwoch, 28. November 2018 15:16 > *An:* fieldtrip at science.ru.nl > *Betreff:* [FieldTrip] Spike artifacts in EEG, 7-12Hz > > > > Dear all, > > > > my name is Matthias and I am relatively new to the field of EEG analysis. > I'm currently carrying out a study on the effects of natural sounds on the > quantitative EEG in patients with Parkinson's disease at the universities > of Vanvouver and Marburg. > > > > Right now I'm experiencing these weird artifacts as seen in the > screenshot. It's sharp spikes, looking similar to ECG artifacts, but in > frequencies of 7-12Hz. > > They are also showing up in the independent components after ICA as well. > > > > The recording was done at a sampling rate of 500 Hz, I've applied a > Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz > (recording took place in Canada). > > > > Has anyone seem similar artifacts before and maybe even knows how to get > rid of them? > > > > Many thanks in advance and all the best, > > > > Matthias > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: ------------------------------ Message: 3 Date: Thu, 29 Nov 2018 09:26:20 +0100 From: anne Hauswald To: FieldTrip discussion list Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz Message-ID: Content-Type: text/plain; charset="utf-8" Dear Matthias, I don’t know where your artifacts come from, but I have some suggestions that might help you getting close to the source. - is it transient or do you find it throughout the whole recording? - do you find it in more than one recording? - does your choice of reference or filtering influence it? Not sure it will lead to something, but at least you will have a better understanding of this artifact. Best Anne > Am 28.11.2018 um 15:15 schrieb Matthias Möller : > > Dear all, > > my name is Matthias and I am relatively new to the field of EEG analysis. I'm currently carrying out a study on the effects of natural sounds on the quantitative EEG in patients with Parkinson's disease at the universities of Vanvouver and Marburg. > > Right now I'm experiencing these weird artifacts as seen in the screenshot. It's sharp spikes, looking similar to ECG artifacts, but in frequencies of 7-12Hz. > They are also showing up in the independent components after ICA as well. > > The recording was done at a sampling rate of 500 Hz, I've applied a Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz (recording took place in Canada). > > Has anyone seem similar artifacts before and maybe even knows how to get rid of them? > > Many thanks in advance and all the best, > > Matthias > <9 Hz Artefact.JPG>_______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: ------------------------------ Message: 4 Date: Thu, 29 Nov 2018 10:08:29 +0100 From: Uwe Graichen To: fieldtrip at science.ru.nl Subject: [FieldTrip] 15 PhD positions in Marie Slodowskwa-Curie Innovative Training Network “INFANS" Message-ID: Content-Type: text/plain; charset="utf-8" As part of the Marie Skłodowska-Curie Innovative Training Network “INFANS - INtegrating Functional Assessment measures for Neonatal Safeguard" http://www.infansproject.eu , funded by the European Union’s Horizon 2020 Research and Innovation Programme, we advertise 15 PhD positions. The goal of INFANS is to develop a new neonatal brain monitoring system, designed to overcome the severe shortage of clinically viable means to high quality monitor the brain function in infancy, crucial to prevent later life neurological, cognitive and motor impairment. To accomplish this goal, INFANS established a structured European PhD training programme in biomedical engineering, signal processing and clinical procedures to train a new generation of creative and entrepreneurial young researchers. The individual research projects of the early stage researchers (ESR) encompass the topics: technological innovation, industrial development, clinical validation, identification of neonatal healthcare needs. As part of their research the INFANS ESRs will develop a novel platform for high quality, clinically-viable EEG-NIRS monitoring accessible worldwide. Well-targeted visits and secondments, soft skills and dynamic training activities, an Open Science strategy, extensive involvement of ESRs in the network events organization, extensive contacts with other research, training and industrial European networks, dissemination activities and the award of Double doctoral degrees are further assets offered to INFANS ESRs. Excellent science, industrial leadership and societal challenge are merged in the INFANS network. The INFANS consortium includes 6 academic and 4 non-academic partners from 6 EU countries, among which leading universities, companies and clinical institutions. The partners involved in INFANS share complementary expertise and facilities to provide international, interdisciplinary and intersectoral research training and mobility that will complement local doctoral training. The INFANS ESRs will become independent researchers with improved career prospects in both the academic and non-academic sectors, and will advance the EU capacity for innovation in biomedical engineering. The institution and the place where the different ESR projects will be carried out, the scientific supervisor(s), individual research project titles, and contact person for each available position can be found specified in the attached document. -------------- next part -------------- A non-text attachment was scrubbed... Name: ITN_INFANS Open_Position_20181129.pdf Type: application/pdf Size: 125155 bytes Desc: not available URL: ------------------------------ Subject: Digest Footer _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 ------------------------------ End of fieldtrip Digest, Vol 96, Issue 22 ***************************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: From rmontefusco at med.uchile.cl Thu Nov 29 22:31:40 2018 From: rmontefusco at med.uchile.cl (Rodrigo Montefusco) Date: Thu, 29 Nov 2018 18:31:40 -0300 Subject: [FieldTrip] Spike artifacts 7-12Hz In-Reply-To: References: Message-ID: Dear Matthias, I use to have something like that. Have you noticed if that happened at some particular time of the day? In my case it was solved after turning some equipment off in a neighbor lab, or by doing the recordings early in the morning, late in the night or during weekends. Good luck! Rodrigo On Thu, Nov 29, 2018 at 5:56 PM Blume Christine wrote: > Dear Matthias, > > > I would guess that if the ICA nicely removes this component that might be > the best approach, it is also commonly used to remove eye blinks or ECG > artefacts from data. You can also try to design your own filter if the > artefact is really centered around 9 Hz, although filtering is an issue on > its own. > > > Hope this helps! > > Christine > > > ------------------------------ > *Von:* fieldtrip im Auftrag von > "Matthias Möller" > *Gesendet:* Donnerstag, 29. November 2018 16:55:26 > *An:* fieldtrip at science.ru.nl > *Betreff:* Re: [FieldTrip] Spike artifacts 7-12Hz > > Dear all, > > thanks a lot for your prompt answers, that is really supportive, thank > you! > > > Testing is already done, so I can't test for devices anymore whether they > produce artifacts or not. > > To be honest I'm running out of ideas which devices might have been > responsible. I used bluetooth headphones where the frequency is a lot > higher, and in some sets I don't have the artifacts although subjects were > stimulated using the same headphones. It's not in all recordings but in > some. If it's in the recording, then it is throughout the whole one. > It's not influenced by referencing or filtering. > > Deep brain stimulation seems to be to high in frequency as well. > > For now the only thing I can do is to remove the respective components > indeed. > Does anyone else maybe have an idea about how to filter out/get rid of > those artifacts? > > Best, > > Matthias > > *Gesendet:* Donnerstag, 29. November 2018 um 10:08 Uhr > *Von:* fieldtrip-request at science.ru.nl > *An:* fieldtrip at science.ru.nl > *Betreff:* fieldtrip Digest, Vol 96, Issue 22 > Send fieldtrip mailing list submissions to > fieldtrip at science.ru.nl > > To subscribe or unsubscribe via the World Wide Web, visit > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > or, via email, send a message with subject or body 'help' to > fieldtrip-request at science.ru.nl > > You can reach the person managing the list at > fieldtrip-owner at science.ru.nl > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of fieldtrip digest..." > > > Today's Topics: > > 1. Re: Spike artifacts in EEG, 7-12Hz (Blume Christine) > 2. Re: Spike artifacts in EEG, 7-12Hz (Marcin Koculak) > 3. Re: Spike artifacts in EEG, 7-12Hz (anne Hauswald) > 4. 15 PhD positions in Marie Slodowskwa-Curie Innovative > Training Network “INFANS" (Uwe Graichen) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Wed, 28 Nov 2018 15:04:32 +0000 > From: Blume Christine > To: "fieldtrip at science.ru.nl" > Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz > Message-ID: <07979b73a31a4cb5bd02398211e36ec6 at sbg.ac.at> > Content-Type: text/plain; charset="utf-8" > > Dear Matthias, > > Admittedly, I do not know what this could be. While the first step should > of course be to find the source and eliminate this (any devices in the EEG > lab, artefact from the acoustic stimulation/headphones, …), in case you are > unable to find it, you could remove the component(s) that correspond to the > artefact. But as I said, the first goal should always be to record clean > data… > > Best, > Christine > > > Von: fieldtrip Im Auftrag von "Matthias > Möller" > Gesendet: Mittwoch, 28. November 2018 15:16 > An: fieldtrip at science.ru.nl > Betreff: [FieldTrip] Spike artifacts in EEG, 7-12Hz > > Dear all, > > my name is Matthias and I am relatively new to the field of EEG analysis. > I'm currently carrying out a study on the effects of natural sounds on the > quantitative EEG in patients with Parkinson's disease at the universities > of Vanvouver and Marburg. > > Right now I'm experiencing these weird artifacts as seen in the > screenshot. It's sharp spikes, looking similar to ECG artifacts, but in > frequencies of 7-12Hz. > They are also showing up in the independent components after ICA as well. > > The recording was done at a sampling rate of 500 Hz, I've applied a > Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz > (recording took place in Canada). > > Has anyone seem similar artifacts before and maybe even knows how to get > rid of them? > > Many thanks in advance and all the best, > > Matthias > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: < > http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20181128/ccb3622e/attachment-0001.html > > > > ------------------------------ > > Message: 2 > Date: Thu, 29 Nov 2018 00:19:13 +0100 > From: Marcin Koculak > To: fieldtrip at science.ru.nl > Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz > Message-ID: > > Content-Type: text/plain; charset="utf-8" > > Dear Matthias, > I have never seen such artifacts, but if you are working with patients with > Parkinson's, have you checked if they have deep brain stimulation devices? > Maybe that is causing the artifacts in the data? > best, > Marcin > > śr., 28 lis 2018 o 16:11 Blume Christine > napisał(a): > > > Dear Matthias, > > > > > > > > Admittedly, I do not know what this could be. While the first step should > > of course be to find the source and eliminate this (any devices in the > EEG > > lab, artefact from the acoustic stimulation/headphones, …), in case you > are > > unable to find it, you could remove the component(s) that correspond to > the > > artefact. But as I said, the first goal should always be to record clean > > data… > > > > > > > > Best, > > > > Christine > > > > > > > > > > > > *Von:* fieldtrip *Im Auftrag von > *"Matthias > > Möller" > > *Gesendet:* Mittwoch, 28. November 2018 15:16 > > *An:* fieldtrip at science.ru.nl > > *Betreff:* [FieldTrip] Spike artifacts in EEG, 7-12Hz > > > > > > > > Dear all, > > > > > > > > my name is Matthias and I am relatively new to the field of EEG analysis. > > I'm currently carrying out a study on the effects of natural sounds on > the > > quantitative EEG in patients with Parkinson's disease at the universities > > of Vanvouver and Marburg. > > > > > > > > Right now I'm experiencing these weird artifacts as seen in the > > screenshot. It's sharp spikes, looking similar to ECG artifacts, but in > > frequencies of 7-12Hz. > > > > They are also showing up in the independent components after ICA as well. > > > > > > > > The recording was done at a sampling rate of 500 Hz, I've applied a > > Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz > > (recording took place in Canada). > > > > > > > > Has anyone seem similar artifacts before and maybe even knows how to get > > rid of them? > > > > > > > > Many thanks in advance and all the best, > > > > > > > > Matthias > > _______________________________________________ > > fieldtrip mailing list > > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > > https://doi.org/10.1371/journal.pcbi.1002202 > > > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: < > http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20181129/ee2745dc/attachment-0001.html > > > > ------------------------------ > > Message: 3 > Date: Thu, 29 Nov 2018 09:26:20 +0100 > From: anne Hauswald > To: FieldTrip discussion list > Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz > Message-ID: > Content-Type: text/plain; charset="utf-8" > > Dear Matthias, > > I don’t know where your artifacts come from, but I have some suggestions > that might help you getting close to the source. > - is it transient or do you find it throughout the whole recording? > - do you find it in more than one recording? > - does your choice of reference or filtering influence it? > > Not sure it will lead to something, but at least you will have a better > understanding of this artifact. > > Best Anne > > > > Am 28.11.2018 um 15:15 schrieb Matthias Möller >: > > > > Dear all, > > > > my name is Matthias and I am relatively new to the field of EEG > analysis. I'm currently carrying out a study on the effects of natural > sounds on the quantitative EEG in patients with Parkinson's disease at the > universities of Vanvouver and Marburg. > > > > Right now I'm experiencing these weird artifacts as seen in the > screenshot. It's sharp spikes, looking similar to ECG artifacts, but in > frequencies of 7-12Hz. > > They are also showing up in the independent components after ICA as well. > > > > The recording was done at a sampling rate of 500 Hz, I've applied a > Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz > (recording took place in Canada). > > > > Has anyone seem similar artifacts before and maybe even knows how to get > rid of them? > > > > Many thanks in advance and all the best, > > > > Matthias > > <9 Hz Artefact.JPG>_______________________________________________ > > fieldtrip mailing list > > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > > https://doi.org/10.1371/journal.pcbi.1002202 > > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: < > http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20181129/87d8fe88/attachment-0001.html > > > > ------------------------------ > > Message: 4 > Date: Thu, 29 Nov 2018 10:08:29 +0100 > From: Uwe Graichen > To: fieldtrip at science.ru.nl > Subject: [FieldTrip] 15 PhD positions in Marie Slodowskwa-Curie > Innovative Training Network “INFANS" > Message-ID: > Content-Type: text/plain; charset="utf-8" > > As part of the Marie Skłodowska-Curie Innovative Training Network > “INFANS - INtegrating Functional Assessment measures for Neonatal > Safeguard" http://www.infansproject.eu , funded by the European Union’s > Horizon 2020 Research and Innovation Programme, we advertise 15 PhD > positions. > > The goal of INFANS is to develop a new neonatal brain monitoring system, > designed to overcome the severe shortage of clinically viable means to > high quality monitor the brain function in infancy, crucial to prevent > later life neurological, cognitive and motor impairment. To accomplish > this goal, INFANS established a structured European PhD training > programme in biomedical engineering, signal processing and clinical > procedures to train a new generation of creative and entrepreneurial > young researchers. > > The individual research projects of the early stage researchers (ESR) > encompass the topics: technological innovation, industrial development, > clinical validation, identification of neonatal healthcare needs. As > part of their research the INFANS ESRs will develop a novel platform for > high quality, clinically-viable EEG-NIRS monitoring accessible > worldwide. Well-targeted visits and secondments, soft skills and dynamic > training activities, an Open Science strategy, extensive involvement of > ESRs in the network events organization, extensive contacts with other > research, training and industrial European networks, dissemination > activities and the award of Double doctoral degrees are further assets > offered to INFANS ESRs. > > Excellent science, industrial leadership and societal challenge are > merged in the INFANS network. The INFANS consortium includes 6 academic > and 4 non-academic partners from 6 EU countries, among which leading > universities, companies and clinical institutions. The partners involved > in INFANS share complementary expertise and facilities to provide > international, interdisciplinary and intersectoral research training and > mobility that will complement local doctoral training. The INFANS ESRs > will become independent researchers with improved career prospects in > both the academic and non-academic sectors, and will advance the EU > capacity for innovation in biomedical engineering. > > The institution and the place where the different ESR projects will be > carried out, the scientific supervisor(s), individual research project > titles, and contact person for each available position can be found > specified in the attached document. > > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: ITN_INFANS Open_Position_20181129.pdf > Type: application/pdf > Size: 125155 bytes > Desc: not available > URL: < > http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20181129/928f2253/attachment.pdf > > > > ------------------------------ > > Subject: Digest Footer > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > ------------------------------ > > End of fieldtrip Digest, Vol 96, Issue 22 > ***************************************** > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From Pranish.Kantak at UTSouthwestern.edu Thu Nov 29 22:47:42 2018 From: Pranish.Kantak at UTSouthwestern.edu (Pranish Kantak) Date: Thu, 29 Nov 2018 21:47:42 +0000 Subject: [FieldTrip] Remove from list serve Message-ID: <6D018139-18FD-44FB-808B-09268E09BE99@UTSouthwestern.edu> Hi! Could you please remove me from the field trip list serve? Thank you! Sent from my iPhone ________________________________ UT Southwestern Medical Center The future of medicine, today. From jason.taylor at manchester.ac.uk Fri Nov 30 02:05:05 2018 From: jason.taylor at manchester.ac.uk (Jason Taylor) Date: Fri, 30 Nov 2018 01:05:05 +0000 Subject: [FieldTrip] automatic IC rejection In-Reply-To: <9b277ef4-067a-9a61-a11b-da7d504b9717@uni-konstanz.de> References: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> <9b277ef4-067a-9a61-a11b-da7d504b9717@uni-konstanz.de> Message-ID: <48E6E95C4AB29E4BAD7908051F5C17EF016E1E31BC@MBXP07.ds.man.ac.uk> Hi Aitor, If you have an 'objective' measure of the artefact you're trying to remove (e.g., VEOG for blinks), a relatively straightforward method is to run a temporal correlation between each IC's activation time-course and the artefact channel's time-course. You can then reject any IC with a correlation higher than some threshold, or with a Z-score (r value relative to the distribution of IC r values) above some threshold. This tends to work very well for identifying blinks, and fairly well for eye-movements (*EOG), and can work for pulse artefact if you have recorded ECG. To avoid spurious correlations due to high-frequency noise, you can filter (e.g., 1 to 30 Hz) the component and artefact signals before correlating them (but obviously go back to the original unfiltered signals to continue with your analysis). Best wishes, Jason -----Original Message----- From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of David Schubring Sent: 28 November 2018 12:47 To: fieldtrip at science.ru.nl; aitor.martinezegurcegui at uzh.ch Subject: Re: [FieldTrip] automatic IC rejection Dear Aitor, the closest thing I know of for a data-driven approach of selecting independent components is COMPASS, quote: "COMPASS is a MATLAB and EEGLAB based algorithm with the purpose of providing the user with a convenient technique for automatic Independent Component (IC) selection with respect to the contributions of the ICs to a certain ERP." Link to the toolbox: http://53450283.de.strato-hosting.eu/jrw/lab/e_compass.htm Paper: Wessel, J. R., & Ullsperger, M. (2011). Selection of independent components representing event-related brain potentials: a data-driven approach for greater objectivity. Neuroimage, 54(3), 2105-2115. https://doi.org/10.1016/j.neuroimage.2010.10.033 I have only theoretical experience with the toolbox as I only learned about it in a workshop and did not yet have the time to test and implement it in my personal FieldTrip workflow (even though it is on my ever growing to-do list). So far it looked like a useful thing to try out to me, especially as code can better be reproduced than "personal judgement". Best, David Am 28.11.2018 um 10:49 schrieb Aitor Egurtzegi: > Dear researchers at Fieldtrip, > > > In order to make my work more reproducible, I would like to > automatically reject ICs instead of doing visual inspection and > rejection of the components. Unfortunately, I haven't found any > documentation for such thing. Is there a way to do it in Fieldtrip? > > Best, > Aitor > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -- Dr. David Schubring General & Biological Psychology University of Konstanz Room C524 P.O. Box 36 78457 Konstanz Phone: +49-(0)7531-88-5350 Homepage: https://gpbp.uni-konstanz.de/people-page/david-schubring _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 From y.visser at hotmail.com Fri Nov 30 10:47:03 2018 From: y.visser at hotmail.com (Yvonne Visser) Date: Fri, 30 Nov 2018 09:47:03 +0000 Subject: [FieldTrip] Cluster based permutation test interpretation Message-ID: Dear all, Thank you for welcoming me to the discussion list, my name is Yvonne Visser and I currently work as a research assistant with dr. Aaron Schurger at Neurospin. During my masters program I learned about cluster based permutation tests for electrophysiological data and distinctly remember how from this type of test one can not conclude that a particular cluster is significant (in line with what is said on the fieldtrip website here, http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test) We are currently using the cluster based permutation test in the analysis of our experiment, but we are a bit confused on how to interpret the results from our test. To give you a short introduction to our experiment: we are looking for a relationship between a behavioural variable and our collected EEG data. So we computed the grand average time frequency spectrum in a single channel of the time bins of interest. Then, we correlated each time/frequency point in this 2d matrix with the behavioural variable in that trial. This resulted in a correlation matrix like you can see in attachment1_correlationmatrix. As you can see, we also computed clusters of time/frequency points with p<0.05. After computing the permutations, we found that the biggest "real" cluster is bigger than any of the permuted clusters. Now, we would like to conclude something from this result about which frequency band at what time is correlated to our behavioural variable. We found a fieldtrip function called ft_clusterplot that does seem to suggest that you can highlight a specific cluster it if it survives the test, but isn't that exactly what my lectures and the webpage say we should not do? Can we say that activity in the alpha band around -0.75 to 0 (where the biggest cluster is located) is correlated to the size of the movement? Or should we not conclude something about which cluster is significant and can we only say that some time frequency power is correlated to our behavioural variable? If the second is true, do you have any advice for us to make the interpretation more specific? Thank you so much in advance, and please let us know if anything is unclear. Kind regards, Yvonne & Aaron. -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: attachment1_correlationmatrix.jpg Type: image/jpeg Size: 61990 bytes Desc: attachment1_correlationmatrix.jpg URL: From aitor.martinezegurcegui at uzh.ch Fri Nov 30 14:42:39 2018 From: aitor.martinezegurcegui at uzh.ch (Aitor Egurtzegi) Date: Fri, 30 Nov 2018 14:42:39 +0100 Subject: [FieldTrip] automatic IC rejection In-Reply-To: <48E6E95C4AB29E4BAD7908051F5C17EF016E1E31BC@MBXP07.ds.man.ac.uk> References: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> <9b277ef4-067a-9a61-a11b-da7d504b9717@uni-konstanz.de> <48E6E95C4AB29E4BAD7908051F5C17EF016E1E31BC@MBXP07.ds.man.ac.uk> Message-ID: <6630afcc-003d-3ecf-2d53-d0174a75ea09@uzh.ch> Dear Jason, Thanks a lot for your reply. Is there a Fieldtrip method already implemented to run such temporal correlation? or would I have to do the implementation in raw Matlab? Thanks in advance, Aitor On 11/30/18 2:05 AM, Jason Taylor wrote: > Hi Aitor, > > If you have an 'objective' measure of the artefact you're trying to remove (e.g., VEOG for blinks), a relatively straightforward method is to run a temporal correlation between each IC's activation time-course and the artefact channel's time-course. You can then reject any IC with a correlation higher than some threshold, or with a Z-score (r value relative to the distribution of IC r values) above some threshold. This tends to work very well for identifying blinks, and fairly well for eye-movements (*EOG), and can work for pulse artefact if you have recorded ECG. To avoid spurious correlations due to high-frequency noise, you can filter (e.g., 1 to 30 Hz) the component and artefact signals before correlating them (but obviously go back to the original unfiltered signals to continue with your analysis). > > Best wishes, > Jason > > > -----Original Message----- > From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of David Schubring > Sent: 28 November 2018 12:47 > To: fieldtrip at science.ru.nl; aitor.martinezegurcegui at uzh.ch > Subject: Re: [FieldTrip] automatic IC rejection > > Dear Aitor, > > the closest thing I know of for a data-driven approach of selecting > independent components is COMPASS, quote: > > "COMPASS is a MATLAB and EEGLAB based algorithm with the purpose of > providing the user with a convenient technique for automatic Independent > Component (IC) selection with respect to the contributions of the ICs to > a certain ERP." > > Link to the toolbox: > > http://53450283.de.strato-hosting.eu/jrw/lab/e_compass.htm > > Paper: > > Wessel, J. R., & Ullsperger, M. (2011). Selection of independent > components representing event-related brain potentials: a data-driven > approach for greater objectivity. Neuroimage, 54(3), 2105-2115. > https://doi.org/10.1016/j.neuroimage.2010.10.033 > > I have only theoretical experience with the toolbox as I only learned > about it in a workshop and did not yet have the time to test and > implement it in my personal FieldTrip workflow (even though it is on my > ever growing to-do list). So far it looked like a useful thing to try > out to me, especially as code can better be reproduced than "personal > judgement". > > Best, > David > > Am 28.11.2018 um 10:49 schrieb Aitor Egurtzegi: >> Dear researchers at Fieldtrip, >> >> >> In order to make my work more reproducible, I would like to >> automatically reject ICs instead of doing visual inspection and >> rejection of the components. Unfortunately, I haven't found any >> documentation for such thing. Is there a way to do it in Fieldtrip? >> >> Best, >> Aitor >> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 From jason.taylor at manchester.ac.uk Fri Nov 30 18:08:22 2018 From: jason.taylor at manchester.ac.uk (Jason Taylor) Date: Fri, 30 Nov 2018 17:08:22 +0000 Subject: [FieldTrip] automatic IC rejection In-Reply-To: <6630afcc-003d-3ecf-2d53-d0174a75ea09@uzh.ch> References: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> <9b277ef4-067a-9a61-a11b-da7d504b9717@uni-konstanz.de> <48E6E95C4AB29E4BAD7908051F5C17EF016E1E31BC@MBXP07.ds.man.ac.uk> <6630afcc-003d-3ecf-2d53-d0174a75ea09@uzh.ch> Message-ID: <48E6E95C4AB29E4BAD7908051F5C17EF016E1E3CBD@MBXP07.ds.man.ac.uk> Hi Aitor, No, not that I know of. I generally use a hacky combination of SPM, fieldtrip, and EEGLAB functions, but if you've already run ICA, you could accomplish what I suggested with some standard matlab functions. Best wishes, Jason -----Original Message----- From: Aitor Egurtzegi [mailto:aitor.martinezegurcegui at uzh.ch] Sent: 30 November 2018 13:43 To: Jason Taylor; FieldTrip discussion list Subject: Re: [FieldTrip] automatic IC rejection Dear Jason, Thanks a lot for your reply. Is there a Fieldtrip method already implemented to run such temporal correlation? or would I have to do the implementation in raw Matlab? Thanks in advance, Aitor On 11/30/18 2:05 AM, Jason Taylor wrote: > Hi Aitor, > > If you have an 'objective' measure of the artefact you're trying to remove (e.g., VEOG for blinks), a relatively straightforward method is to run a temporal correlation between each IC's activation time-course and the artefact channel's time-course. You can then reject any IC with a correlation higher than some threshold, or with a Z-score (r value relative to the distribution of IC r values) above some threshold. This tends to work very well for identifying blinks, and fairly well for eye-movements (*EOG), and can work for pulse artefact if you have recorded ECG. To avoid spurious correlations due to high-frequency noise, you can filter (e.g., 1 to 30 Hz) the component and artefact signals before correlating them (but obviously go back to the original unfiltered signals to continue with your analysis). > > Best wishes, > Jason > > > -----Original Message----- > From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of David Schubring > Sent: 28 November 2018 12:47 > To: fieldtrip at science.ru.nl; aitor.martinezegurcegui at uzh.ch > Subject: Re: [FieldTrip] automatic IC rejection > > Dear Aitor, > > the closest thing I know of for a data-driven approach of selecting > independent components is COMPASS, quote: > > "COMPASS is a MATLAB and EEGLAB based algorithm with the purpose of > providing the user with a convenient technique for automatic Independent > Component (IC) selection with respect to the contributions of the ICs to > a certain ERP." > > Link to the toolbox: > > http://53450283.de.strato-hosting.eu/jrw/lab/e_compass.htm > > Paper: > > Wessel, J. R., & Ullsperger, M. (2011). Selection of independent > components representing event-related brain potentials: a data-driven > approach for greater objectivity. Neuroimage, 54(3), 2105-2115. > https://doi.org/10.1016/j.neuroimage.2010.10.033 > > I have only theoretical experience with the toolbox as I only learned > about it in a workshop and did not yet have the time to test and > implement it in my personal FieldTrip workflow (even though it is on my > ever growing to-do list). So far it looked like a useful thing to try > out to me, especially as code can better be reproduced than "personal > judgement". > > Best, > David > > Am 28.11.2018 um 10:49 schrieb Aitor Egurtzegi: >> Dear researchers at Fieldtrip, >> >> >> In order to make my work more reproducible, I would like to >> automatically reject ICs instead of doing visual inspection and >> rejection of the components. Unfortunately, I haven't found any >> documentation for such thing. Is there a way to do it in Fieldtrip? >> >> Best, >> Aitor >> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 From abela.eugenio at gmail.com Fri Nov 30 18:25:51 2018 From: abela.eugenio at gmail.com (Eugenio Abela) Date: Fri, 30 Nov 2018 17:25:51 +0000 Subject: [FieldTrip] HELP: import simple data from txt file - non supported format, no header In-Reply-To: References: Message-ID: <666ECAD5-0675-42B3-AF4F-205F1747A735@gmail.com> Hi Tony Here’s a pretty quick and dirty fix - have a careful look to make sure it does what you want. You need FieldTrip installed and in the path; it works on my MATLAB2016a. Others on the list might have better ideas, or spot obvious errors. Good luck eugenio % Import you data D = importdata('Pressure_3_6.txt',' '); % Fill in FieldTrip data structure (check out ft_datatype_raw) % I assume columns 4, 5 are EEG, 6,7 EMG. % Channels go in rows, time in columns. data = struct(); data.label = {'EEG-1';'EEG-2';'EMG-1';'EMG-2'}; data.fsample = 250; data.time = {1:length(D)}; data.trial = {D(:,4:7)’}; % Check out how it looks cfg = []; cfg.viemode = 'vertical'; ft_databrowser(cfg,data); On 29 Nov 2018, at 17:22, Hoang Truong wrote: Dear community, This is Tony, from CS Department, University of Colorado Boulder. I am new to eeg analysis and fieldtrip. Currently I work on recording eeg and emg data using our own hardware with 2 eeg and 2 emg channels respectively. Since we use our custom hardware, the data are saved in a simple format of txt file with 7 columns (example data on Dropbox: link ): 3 first columns are data from environment sensors, the next 4 are eeg and emg data (250Hz sampling rate, 42s recording) I would like to ask what will be a proper way for me to import this data into fieldtrip. I checked the faq about importing custom data but I do not understand how to make custom readheader, readdata and readevent function. Any help would be appreciated (suggestions, code examples, etc). Thank you so much. Sincerely, Tony _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From Hoang.Truong at colorado.edu Fri Nov 30 18:47:10 2018 From: Hoang.Truong at colorado.edu (Hoang Truong) Date: Fri, 30 Nov 2018 10:47:10 -0700 Subject: [FieldTrip] HELP: import simple data from txt file - non supported format, no header In-Reply-To: <666ECAD5-0675-42B3-AF4F-205F1747A735@gmail.com> References: <666ECAD5-0675-42B3-AF4F-205F1747A735@gmail.com> Message-ID: Thanks for your prompt help, Eugenio and Stephen !!! I can view the data with Eugenio's code w MATLAB2018a. I'll continue to work from there. @Stephen: I build a small hardware piece that can capture multimodal biosignal and integrate this into some wearable prototypes. Sincerely, Tony On Fri, Nov 30, 2018 at 10:25 AM Eugenio Abela wrote: > Hi Tony > > Here’s a pretty quick and dirty fix - have a careful look to make sure it > does what you want. You need FieldTrip installed and in the path; it works > on my MATLAB2016a. Others on the list might have better ideas, or spot > obvious errors. > > Good luck > eugenio > > % Import you data > D = importdata('Pressure_3_6.txt',' '); > > > % Fill in FieldTrip data structure (check out ft_datatype_raw) > % I assume columns 4, 5 are EEG, 6,7 EMG. > % Channels go in rows, time in columns. > data = struct(); > data.label = {'EEG-1';'EEG-2';'EMG-1';'EMG-2'}; > data.fsample = 250; > data.time = {1:length(D)}; > data.trial = {D(:,4:7)’}; > > > % Check out how it looks > cfg = []; > cfg.viemode = 'vertical'; > ft_databrowser(cfg,data); > > > > On 29 Nov 2018, at 17:22, Hoang Truong wrote: > > Dear community, > > This is Tony, from CS Department, University of Colorado Boulder. I am new > to eeg analysis and fieldtrip. Currently I work on recording eeg and emg > data using our own hardware with 2 eeg and 2 emg channels respectively. > Since we use our custom hardware, the data are saved in a simple format of > txt file with 7 columns (example data on Dropbox: link > ): 3 > first columns are data from environment sensors, the next 4 are eeg and emg > data (250Hz sampling rate, 42s recording) > I would like to ask what will be a proper way for me to import this data > into fieldtrip. I checked the faq about importing custom data but I do not > understand how to make custom readheader, readdata and readevent function. > Any help would be appreciated (suggestions, code examples, etc). > Thank you so much. > > Sincerely, > Tony > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From ignasi.sols at nyu.edu Thu Nov 1 04:06:54 2018 From: ignasi.sols at nyu.edu (Ignasi Sols) Date: Wed, 31 Oct 2018 23:06:54 -0400 Subject: [FieldTrip] Brain-shift compensation Error In-Reply-To: References: Message-ID: Many thanks, Arjen. I could solve the problem following your suggestion. It was an issue with the format of the channel information, that was not recognized by* ft_channelselection.* Best, Ignasi On Wed, Oct 31, 2018 at 2:26 AM Arjen Stolk wrote: > Hi Ignasi, > > One can only guess based on that error msg alone. You might want to put a > debug marker at line 156, check whether coord0 is truly empty, and then try > to trace back to what's causing it to be empty (e.g., an empty elecpos > field in your elec structure?). > > Arjen > > On Tue, Oct 30, 2018 at 12:04 PM Ignasi Sols wrote: > >> Dear all, >> I'm following the method developed by Stolk et al (2018) to localize the >> electrodes of ECoG data. >> I'm getting this error on step 23 (Project the electrode grids to the >> surface hull of the implanted hemisphere) and I can't solve it. Could >> anyone help me with this? >> >> Thanks, >> Ignasi >> >> >> *using electrodes specified in the configuration* >> *using warp algorithm described in Dykstra et al. 2012 Neuroimage PMID: >> 22155045* >> *creating electrode pairs based on electrode positions* >> *Error using fmincon (line 241)* >> *You must provide a non-empty starting* >> *point.* >> *Error in warp_dykstra2012 (line 156)* >> *coord_snapped = fmincon(efun, coord0,* >> *[], [], [], [], [], [], cfun,* >> *options);* >> *Error in ft_electroderealign (line* >> *406)* >> * norm.elecpos =* >> warp_dykstra2012(cfg, elec, >> headshape); >> >> -- >> Ignasi Sols >> Postdoctoral Fellow >> Department of Psychology >> New York University >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> >> https://doi.org/10.1371/journal.pcbi.1002202 >> >> > _______________________________________________ > fieldtrip mailing list > > https://urldefense.proofpoint.com/v2/url?u=https-3A__mailman.science.ru.nl_mailman_listinfo_fieldtrip&d=DwIGaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=MfcAKxrNJUDchBMH7cGf8A7flR7i0Mg4wa1BIfRAxTM&m=ogxF67dx9cUcJr32WF9DDK-sz4FQ20js2rkptNpxMBA&s=Jx9wYcv0XnbORxGzfpEH0gqB1QyHZMaw-4hs4evzORE&e= > > https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1371_journal.pcbi.1002202&d=DwIGaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=MfcAKxrNJUDchBMH7cGf8A7flR7i0Mg4wa1BIfRAxTM&m=ogxF67dx9cUcJr32WF9DDK-sz4FQ20js2rkptNpxMBA&s=9nWIVPM4Xf2SQkk3fMSCzXvE82mHFH-pnn67Fhygfyk&e= > -- Ignasi Sols Postdoctoral Fellow Department of Psychology New York University -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Thu Nov 1 08:51:05 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 1 Nov 2018 07:51:05 +0000 Subject: [FieldTrip] Phase Information For PCC Beamformer In-Reply-To: <74e003128d5a0219ae6febed2c6ff119@inserm.fr> References: <74e003128d5a0219ae6febed2c6ff119@inserm.fr> Message-ID: Hi Hesham, If memory serves me well, but you may want to verify this in the code of ft_specest_mtmfft, the phase estimate is expressed relative to t=0 (considering the epoch’s time axis that is passed on to ft_specest_mtmfft). Best wishes, Jan-Mathijs > On 30 Oct 2018, at 17:05, Hesham ElShafei wrote: > > Hello Fieldtrippers! > > So I am trying to do some whole brain connectivity analysis according to this tutorial: http://www.fieldtriptoolbox.org/tutorial/networkanalysis > > All is going fine :) I just would like to understand how the phase information is obtained using these options in ft_freqanalysis > > cfg.method = 'mtmfft'; > cfg.output = 'fourier'; > > In other words , how is time incorporated in the computed phase values? > In other other words, these phase values represent the signal at which time point? > > hope I was clear enough > > Cheers! > Hesham > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From jan.schoffelen at donders.ru.nl Thu Nov 1 09:13:34 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 1 Nov 2018 08:13:34 +0000 Subject: [FieldTrip] Problem using ft_sensrorealign with Yokogawa MEG In-Reply-To: References: Message-ID: <684D7E73-1417-44EB-A75B-DE1A83864556@donders.ru.nl> Hi Victor, You don’t mention which fieldtrip version you are currently using, but ft_sensorrealign has been moved to compat/obsolete about a year ago. This means that this particular function is not actively maintained and supported anymore. At the moment, I don’t remember the reason for this, but it would mean that there is functionality in the main fieldtrip functions that got rid of the raison d’etre of ft_sensorrealign. As is mentioned in the README of the compat/obsolete directory, you should move ft_sensorrealign up a few directories. The reason for this is that the function might rely on low-level functions that are located in fieldtrip/private, which are only visible from function that are located in the directory that contains the private folder. ‘fixpos’ might be one of them. Best wishes, Jan-Mathijs J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands On 30 Oct 2018, at 10:43, Victor RG > wrote: HI Fieldtrip experts! I'm trying to align a headmodel (the one from example "Subject1") with my MEG sensors (Yokogawa system). I'm trying to do that interactively, in order to construct a Leadfied matrix as accurate as possible. To do that, I am testing this code: cfg = []; cfg.method = 'interactive'; cfg.headshape = vol.bnd(1); cfg.senstype = 'meggrad'; grad_aligned = ft_sensorrealign(cfg, grad); % Im using ft_sensorrealign cause I think is the MEG version of ft_electroderealign The variables employed consist of: >> grad = struct with fields: * balance: [1×1 struct] * chanori: [160×3 double] * chanpos: [160×3 double] * chantype: {160×1 cell} * chanunit: {160×1 cell} * coilori: [320×3 double] * coilpos: [320×3 double] * label: {160×1 cell} * tra: [160×320 double] * type: 'yokogawa160' * unit: 'cm' * fid: [1×1 struct] >> vol.bnd(1) = struct with fields: * pos: [1000×3 double] * tri: [1996×3 double] * coordsys: 'ctf' I have looked at the tutorial for EEG sensors realignment, and I have copied the procedure, since there is not a specific tutorial for MEG sensors. I thought it would be the same, but when executing it I obtain the following errors: >> Undefined function 'fixpos' for input arguments of type 'struct'. Error in ft_sensorrealign (line 255) headshape = fixpos(cfg.headshape); Error in generating_leadfield (line 63) grad_aligned = ft_sensorrealign(cfg, grad); Does anybody know how to do that, or how to do an interactive realignment with MEG sensors? Thanks in advance. Víctor. Víctor Rodríguez González Grupo de Ingeniería Biomédica, ETSIT. Universidad de Valladolid, España. _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Thu Nov 1 09:23:11 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 1 Nov 2018 08:23:11 +0000 Subject: [FieldTrip] ft_mergealgin: high residual variance? In-Reply-To: References: Message-ID: <863F96C2-45E4-4441-B330-6C0292880D72@donders.ru.nl> Hi Maria, Are you sure about the units in your headmodel (and gradiometers)? Using the value 1.0 as an inwardshift parameter suggests ‘cm’. Not sure what will happen when by accident the hs_file is in ‘m’ (which, as far as I know, is usually the case). Best wishes, Jan-Mathijs J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands On 29 Oct 2018, at 13:33, Maria Hakonen > wrote: Dear FieldTrip experts, I have run ft_mergealign across subjects to align the head positions. However, the residual variance between the original and the realigned data seems to be high: original -> template RV 21232.46 % original -> original RV 36.96 % original -> template -> original RV 9579.95 % Could someone please let me know what would be the largest acceptable change in the residual variance, and what should I do if the residual variance is too high? Does the increase in residual variance mean that there is a large shift in the head position? I have used ft_mergealign as follows: template = list of subjects (i.e. I want to calculate an average head position over the subjects) grad = data.grad; hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); vol = ft_headmodel_localspheres(hs,grad); cfg = []; cfg.template = template; cfg.inwardshift = 1.0; cfg.vol = vol; data_aligned = ft_megrealign(cfg, data); Best, Maria _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From C.Mazzetti at donders.ru.nl Thu Nov 1 12:04:23 2018 From: C.Mazzetti at donders.ru.nl (Mazzetti, C. (Cecilia)) Date: Thu, 1 Nov 2018 11:04:23 +0000 Subject: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage Message-ID: <1541070268890.93169@donders.ru.nl> Dear everyone, I have some stimulus locked data which I have to baseline with respect to the same data but cue locked. I was wondering whether there is a setting in fieldtrip which allows to do that (i.e. set the baseline timw window according to a different dataset, the cue-locked data) and applying it to the stimulus locked data. Thanks in advance! Best, Cecilia Cecilia Mazzetti - Ph.D. Candidate Donders Centre for Cognitive Neuroimaging, room 0.068 Kapittelweg 29 | 6525 EN Nijmegen -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Thu Nov 1 21:07:08 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Thu, 1 Nov 2018 16:07:08 -0400 Subject: [FieldTrip] Post hoc test following significant interaction Message-ID: Dear FieldTrip community, I have a 2 x 2 mixed study design (within factor being pre post, and between factor being group 1 and group 2) in which I am hoping to use an ANOVA to analyze. Since I have a 2 x 2 factorial design, I have been following the tutorial: -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Thu Nov 1 21:21:38 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Thu, 1 Nov 2018 16:21:38 -0400 Subject: [FieldTrip] Post hoc test following significant interaction Message-ID: Dear FieldTrip community, I have a 2 x 2 mixed study design (within factor being pre post, and between factor being group 1 and group 2) in which I am hoping to use an ANOVA in the cluster based permutation framework to analyze. Since I have a 2 x 2 factorial design, I have been following the tutorial: http://www.fieldtriptoolbox.org/faq/how_can_i_test_an_interaction_effect_using_cluster-based_permutation_tests as well as various previous responses on the mailing list. In the context that I have an interaction in certain electrodes at certain time points, my questions are: 1. How would I go about testing the simple effects to disentangle the interaction. Would it simply be a case of skipping the main effects and running the appropriate indep/dep t-test between each 4 of the possible contrasts for simple effects? (i.e. group 1 vs group 2 at pre, group 1 vs group 2 at post, group 1 at pre vs group 1 at post, group 2 at pre vs group 2 at post). 2). If an interaction is initially found, should the post-hoc (e.g. simple main effects) be limited to the electrodes and time in which the interactions are present, or would the simple effect contrasts be run across all electrodes and time points as were run in the initial interaction test. Any insight would be greatly appreciated. Thank you, Paul -------------- next part -------------- An HTML attachment was scrubbed... URL: From pascualm at key.uzh.ch Fri Nov 2 02:36:01 2018 From: pascualm at key.uzh.ch (pascualm at key.uzh.ch) Date: Fri, 2 Nov 2018 10:36:01 +0900 Subject: [FieldTrip] Comparison of measures of electrophysiological connectivity Message-ID: Dear Colleagues, The preprint entitled: "A comparison of bivariate frequency domain measures of electrophysiological connectivity" at: https://doi.org/10.1101/459503 might be of interest to those performing research related to electrophysiological connectivity inference. The abstract can be found below. Cordially, Roberto ... Roberto D. Pascual-Marqui, PhD, PD The KEY Institute for Brain-Mind Research, University of Zurich Visiting Professor at Neuropsychiatry, Kansai Medical University, Osaka [https://www.uzh.ch/keyinst/loreta] [scholar.google.com/citations?user=pascualmarqui] ... Abstract: The problem of interest here concerns electrophysiological signals from two cortical sites, acquired as invasive intracranial recordings, or from non-invasive estimates of cortical electric neuronal activity computed from EEG or MEG recordings (see e.g. https://doi.org/10.1101/269753). In the absence of other sources, these measured signals consist of an instantaneous linear mixture of the true, actual, unobserved local signals, due to low spatial resolution and volume conduction. A connectivity measure is unreliable as a true indicator of electrophysiological connectivity if it is not invariant to mixing, or if it reports a significant connection for a mixture of independent signals. In (Vinck et al 2011 Neuroimage 55:1548) it was shown that coherence, imaginary coherence, and phase locking value are not invariant to mixing, while the phase lag index (PLI) and the weighted version (wPLI) are invariant to mixing. Here we show that the lagged coherence (LagCoh) measure (2007, https://arxiv.org/abs/0711.1455), not studied in Vinck et al, is invariant to mixing. Additionally, we present here a new mixture-invariant connectivity statistic: the "standardized imaginary covariance" (sImCov). We also include in the comparisons the directed PLI (dPLI) by Stam et al (2012 Neuroimage 62:1415). Fourier coefficients for "N" trials are generated from a linear unidirectional causal time domain model with electrophysiological delay "k" and regression coefficient "b". 1000 random data sets of "N" trials are simulated, and for each one, and for each connectivity measure, non-parametric randomization tests are performed. The "true positive detection rate" is calculated as the fraction of 1000 cases that have significant connectivity at p<0.05, 0.1, and 0.2. The connectivity methods were compared in terms of detection rates, under non-mixed and mixed conditions, for small and large sample sizes "N", with and without jitter, and for different values of signal to noise. Under mixing, the results show that LagCoh outperforms wPLI, PLI, dPLI, and sImCov. Without mixing, LagCoh and sImCov outperform wPLI, PLI, and dPLI. Finally, it is shown that dPLI is an invalid estimator of flow direction, i.e. it reverses and "goes against the flow" by merely changing the sign of one of the time series, a fact that violates the basic definition of Granger causality. From maria.hakonen at gmail.com Fri Nov 2 07:37:44 2018 From: maria.hakonen at gmail.com (Maria Hakonen) Date: Fri, 2 Nov 2018 08:37:44 +0200 Subject: [FieldTrip] ft_mergealgin: high residual variance? In-Reply-To: <863F96C2-45E4-4441-B330-6C0292880D72@donders.ru.nl> References: <863F96C2-45E4-4441-B330-6C0292880D72@donders.ru.nl> Message-ID: Hi Jan-Mathias! Thank you for the answer! I changed the units of gradiometers and head model to cm. This clearly decreased the residual variances. Also, singleshell seems to work better than localspheres. However, the transformation seems to still increase the residual variance a lot. Here are some examples: original -> template RV 407.64 % original -> original RV 9.85 % original -> template -> original RV 10.75 % realigning trial 706 original -> template RV 393.13 % original -> original RV 9.00 % original -> template -> original RV 9.90 % realigning trial 707 original -> template RV 362.10 % original -> original RV 8.33 % original -> template -> original RV 9.18 % realigning trial 708 original -> template RV 377.15 % original -> original RV 9.43 % original -> template -> original RV 10.33 % The code is now as follows: load([data_path nameList{subj} '.mat']); grad = datafinal.grad; hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); cfg = []; cfg.method = 'singlesphere'; cfg.geom = hs; cfg.grad = grad; cfg.feedback = true; vol = ft_prepare_headmodel(cfg); vol = ft_convert_units(vol,'cm'); grad = ft_convert_units(grad,'cm'); cfg = []; cfg.template = template; cfg.inwardshift = 2.5; cfg.feedback ='no'; cfg.vol = vol; data = ft_megrealign(cfg, data); Best, Maria to 1. marrask. 2018 klo 10.35 Schoffelen, J.M. (Jan Mathijs) ( jan.schoffelen at donders.ru.nl) kirjoitti: > Hi Maria, > > Are you sure about the units in your headmodel (and gradiometers)? Using > the value 1.0 as an inwardshift parameter suggests ‘cm’. Not sure what will > happen when by accident the hs_file is in ‘m’ (which, as far as I know, is > usually the case). > > Best wishes, > > Jan-Mathijs > > J.M.Schoffelen, MD PhD > Senior Researcher, VIDI-fellow - PI, language in interaction > Telephone: +31-24-3614793 > Physical location: room 00.028 > Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands > > > > > On 29 Oct 2018, at 13:33, Maria Hakonen wrote: > > Dear FieldTrip experts, > > I have run ft_mergealign across subjects to align the head positions. > However, the residual variance between the original and the realigned data > seems to be high: > > original -> template RV 21232.46 % > original -> original RV 36.96 % > original -> template -> original RV 9579.95 % > > Could someone please let me know what would be the largest acceptable > change in the residual variance, and what should I do if the residual > variance is too high? Does the increase in residual variance mean that > there is a large shift in the head position? > > I have used ft_mergealign as follows: > > template = list of subjects (i.e. I want to calculate an average head > position over the subjects) > > grad = data.grad; > hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); > vol = ft_headmodel_localspheres(hs,grad); > > cfg = []; > cfg.template = template; > cfg.inwardshift = 1.0; > cfg.vol = vol; > data_aligned = ft_megrealign(cfg, data); > > Best, > Maria > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From tom.marshall at psy.ox.ac.uk Fri Nov 2 23:29:10 2018 From: tom.marshall at psy.ox.ac.uk (Tom Marshall) Date: Fri, 2 Nov 2018 22:29:10 +0000 Subject: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage In-Reply-To: <1541070268890.93169@donders.ru.nl> References: <1541070268890.93169@donders.ru.nl> Message-ID: Hey Cecilia, I guess you could do that with a combination of 'ft_selectdata', and 'ft_math'. Let's say you have datasets called 'stimlocked' and 'cuelocked' You'd do something like: % cut out baseline from cuelocked cfg = []; cfg.latency = [-0.5 0]; % or whatever your baseline window is baseline = ft_selectdata(cfg, cuelocked); % subtract baseline from stimlocked cfg = []; cfg.operation = 'subtract'; baseline_corrected_stimlocked = ft_math(cfg, stimlocked, baseline); I think that ft_math allows you to input algebraic expressions too. So if you wanted to do, for example, a 'relchange' baseline correction you should substitute (something like) cfg.operation = '(x1-x2)/x2'; Best, Tom ________________________________ From: fieldtrip on behalf of Mazzetti, C. (Cecilia) Sent: 01 November 2018 11:04:23 To: fieldtrip at science.ru.nl Subject: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage Dear everyone, I have some stimulus locked data which I have to baseline with respect to the same data but cue locked. I was wondering whether there is a setting in fieldtrip which allows to do that (i.e. set the baseline timw window according to a different dataset, the cue-locked data) and applying it to the stimulus locked data. Thanks in advance! Best, Cecilia Cecilia Mazzetti - Ph.D. Candidate Donders Centre for Cognitive Neuroimaging, room 0.068 Kapittelweg 29 | 6525 EN Nijmegen -------------- next part -------------- An HTML attachment was scrubbed... URL: From silvana.silva at upf.edu Mon Nov 5 16:14:35 2018 From: silvana.silva at upf.edu (SILVA PEREIRA, SILVANA) Date: Mon, 5 Nov 2018 16:14:35 +0100 Subject: [FieldTrip] ICA inspection: Error using ft_icabrowser.m Message-ID: Dear all, I'm trying to use the funtion ft_icabrowser.m, but I get the following error message: Error using topoplot_common (line 523) labels in data and labels in layout do not match Error in ft_topoplotIC (line 184) [cfg] = topoplot_common(cfg, comp); Error in ft_icabrowser (line 151) ft_topoplotIC(cfgtopo, comp); I understand that the error is due to a mismatch between the labels of the cfg struct and the layout I'm using. I modified the entries of the struct in acticap-64ch-standard2.mat, where I removed four of the channels, since we have in addition RM, LM, Heog and Veog. Adding these four labels to the original label list does not solve the problem. Any idea to work around this issue? Thank you! Silvana *Silvana Silva Pereira* /Postdoctoral Researcher/ Center for Brain and Cognition [image: Universitat Pompeu Fabra, Barcelona] -------------- next part -------------- An HTML attachment was scrubbed... URL: From dlozanosoldevilla at gmail.com Mon Nov 5 16:54:24 2018 From: dlozanosoldevilla at gmail.com (Diego Lozano-Soldevilla) Date: Mon, 5 Nov 2018 16:54:24 +0100 Subject: [FieldTrip] ICA inspection: Error using ft_icabrowser.m In-Reply-To: References: Message-ID: Hi Silvana, Could you try the component viewmode option? cfg = []; cfg.layout = 'acticap-64ch-standard'; cfg.viewmode = 'component'; % Mode specifically suited to browse through ICA data ft_databrowser(cfg, comp); Best, Diego On Mon, Nov 5, 2018, 16:38 SILVA PEREIRA, SILVANA Dear all, > > I'm trying to use the funtion ft_icabrowser.m, but I get the following > error message: > > Error using topoplot_common (line 523) > labels in data and labels in layout do not match > > Error in ft_topoplotIC (line 184) > [cfg] = topoplot_common(cfg, comp); > > Error in ft_icabrowser (line 151) > ft_topoplotIC(cfgtopo, comp); > > I understand that the error is due to a mismatch between the labels of the > cfg struct and the layout I'm using. I modified the entries of the struct > in acticap-64ch-standard2.mat, where I removed four of the channels, since > we have in addition RM, LM, Heog and Veog. Adding these four labels to the > original label list does not solve the problem. Any idea to work around > this issue? > > Thank you! > Silvana > > > *Silvana Silva Pereira* > /Postdoctoral Researcher/ > Center for Brain and Cognition > [image: Universitat Pompeu Fabra, Barcelona] > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Mon Nov 5 17:21:05 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Mon, 5 Nov 2018 17:21:05 +0100 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra Message-ID: Fellow FieldTrippers, I have identified two points of interest in the brain, between which I want to compute connectivity metrics. One is in early visual cortex, the other is in right temporal lobe, somewhat medial (compatible with hippocampus, but exact interpretation not relevant right now). I reconstructed activity for these grid points using LCMV beamformer (on MEG data) and computed source-level Fourier spectra (taper = 'dpss', tapsmofrq = 3) after applying a band-stop filter around 50 Hz and harmonics. Using ft_connectivityanalysis, I computed both debiased wPLI and Granger causality between the two points of interest. In both spectra, I see a clear peak at 60 Hz in the grand average across 36 subjects, which is also there in the majority of individual subjects (though very strong only in 2/36). Plots are attached. Now this would be exciting news if indeed it turns out to be a true highly band-limited gamma effect! But of course I suspect that this peak could very well be artifactual. I can't think of any artifact source I may have missed that would cause this, though. The projector refresh rate during the experiment was set to 120 Hz, and not 60. (Also note this is European data so AC frequency is 50 Hz and not 60, hence the band stop I mentioned earlier.) Does anyone have any idea what might be causing this sharp peak? (A genuine effect after all?) Thanks, Eelke -------------- next part -------------- A non-text attachment was scrubbed... Name: granger-spectrum.png Type: image/png Size: 14990 bytes Desc: not available URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: wpli-spectrum.png Type: image/png Size: 11765 bytes Desc: not available URL: From jan.schoffelen at donders.ru.nl Mon Nov 5 18:13:05 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Mon, 5 Nov 2018 17:13:05 +0000 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra In-Reply-To: References: Message-ID: This looks like an artifact to me. In addition, the zigzag in the Granger spectra is suggestive of convergence issues in the non-parametric spectral matrix factorization in at least a few of your subjects. This is often caused by strong discontinuities in the spectra (spectral lines!) or poor behaviour of the time domain data at the edges of your epochs, causing spectral leakage. JM > On 5 Nov 2018, at 17:21, Eelke Spaak wrote: > > Fellow FieldTrippers, > > I have identified two points of interest in the brain, between which I > want to compute connectivity metrics. One is in early visual cortex, > the other is in right temporal lobe, somewhat medial (compatible with > hippocampus, but exact interpretation not relevant right now). I > reconstructed activity for these grid points using LCMV beamformer (on > MEG data) and computed source-level Fourier spectra (taper = 'dpss', > tapsmofrq = 3) after applying a band-stop filter around 50 Hz and > harmonics. Using ft_connectivityanalysis, I computed both debiased > wPLI and Granger causality between the two points of interest. > > In both spectra, I see a clear peak at 60 Hz in the grand average > across 36 subjects, which is also there in the majority of individual > subjects (though very strong only in 2/36). Plots are attached. Now > this would be exciting news if indeed it turns out to be a true highly > band-limited gamma effect! > > But of course I suspect that this peak could very well be artifactual. > I can't think of any artifact source I may have missed that would > cause this, though. The projector refresh rate during the experiment > was set to 120 Hz, and not 60. (Also note this is European data so AC > frequency is 50 Hz and not 60, hence the band stop I mentioned > earlier.) > > Does anyone have any idea what might be causing this sharp peak? (A > genuine effect after all?) > > Thanks, > Eelke > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From silvana.silva at upf.edu Mon Nov 5 18:23:06 2018 From: silvana.silva at upf.edu (SILVA PEREIRA, SILVANA) Date: Mon, 5 Nov 2018 18:23:06 +0100 Subject: [FieldTrip] ICA inspection: Error using ft_icabrowser.m In-Reply-To: References: Message-ID: Hi Diego, Your suggestion works fine for ft_databrowser, but not for ft_icabrowser! best regards, Silvana El lun., 5 nov. 2018 a las 16:58, Diego Lozano-Soldevilla (< dlozanosoldevilla at gmail.com>) escribió: > Hi Silvana, > Could you try the component viewmode option? > > cfg = []; > cfg.layout = 'acticap-64ch-standard'; > cfg.viewmode = 'component'; % Mode specifically suited to browse through ICA data > ft_databrowser(cfg, comp); > > Best, > Diego > > > On Mon, Nov 5, 2018, 16:38 SILVA PEREIRA, SILVANA wrote: > >> Dear all, >> >> I'm trying to use the funtion ft_icabrowser.m, but I get the following >> error message: >> >> Error using topoplot_common (line 523) >> labels in data and labels in layout do not match >> >> Error in ft_topoplotIC (line 184) >> [cfg] = topoplot_common(cfg, comp); >> >> Error in ft_icabrowser (line 151) >> ft_topoplotIC(cfgtopo, comp); >> >> I understand that the error is due to a mismatch between the labels of >> the cfg struct and the layout I'm using. I modified the entries of the >> struct in acticap-64ch-standard2.mat, where I removed four of the channels, >> since we have in addition RM, LM, Heog and Veog. Adding these four labels >> to the original label list does not solve the problem. Any idea to work >> around this issue? >> >> Thank you! >> Silvana >> >> >> *Silvana Silva Pereira* >> /Postdoctoral Researcher/ >> Center for Brain and Cognition >> >> [image: Universitat Pompeu Fabra, Barcelona] >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From rdm146 at newark.rutgers.edu Mon Nov 5 18:26:35 2018 From: rdm146 at newark.rutgers.edu (Ravi Mill) Date: Mon, 5 Nov 2018 17:26:35 +0000 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> References: , <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> Message-ID: Hi Carsten and Johannes Many thanks for responding, and for developing these great tools! I'm in the process of acquiring a large diffusion MR dataset from which I can hopefully create an 'averaged' atlas. From your responses I think I have a sense of how to integrate the conductivity tensors derived from this atlas with the Fieldtrip FEM pipeline. But I was wondering if you had any advice on how to compute these conductivity tensors in the first place? From the paper that Carsten sent, It seems like the FDT program within FSL is what I need to compute diffusion tensors from the raw diffusion images (steps 1-6 from the FDT user guide https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Processing_pipeline)? Seemingly, these diffusion tensors need to then be converted to conductivity tensors - any advice on how to do this (or if you could point me to some example code) would be greatly appreciated. Thanks Ravi FDT/UserGuide - FslWiki - University of Oxford fsl.fmrib.ox.ac.uk Eddy Current Correction. Eddy currents in the gradient coils induce (approximate) stretches and shears in the diffusion weighted images. These distortions are different for different gradient directions. Thanks again Ravi ________________________________ From: fieldtrip on behalf of Johannes Vorwerk Sent: Monday, October 29, 2018 10:14:16 AM To: FieldTrip discussion list Subject: Re: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio Dear Ravi, as Carsten already said, calculating FEM with anisotropic conductivities is not directly supported by the FieldTrip-SimBio implementation. However, if you are willing to invest a bit of time it is possible to work around this. The „only“ thing that needs to be changed is the calculation of the FEM stiffness matrix, which is performed by the routine „calc_stiffness_matrix_val“ in the function sb_calc_stiff (usually called from ft_prepare_headmodel). The problem is that FieldTrip does not support anisotropic conductivities, so that you would have to call calc_stiffness_matrix_val directly. You can see the correct call in sb_calc_stiff. For anisotropic conductivities you have to replace the input „cond“ by a #elements x 6 matrix containing your anisotropic conductivities in the format "xx yy zz xy yz zx“. If you now follow the normal FieldTrip-SimBio workflow using the resulting stiffness matrix, you will get results for anisotropic conductivities. Best, Johannes Am 29.10.2018 um 12:31 schrieb Carsten Wolters >: Dear Ravi, 1) You can use the pure SimBio-code from https://www.mrt.uni-jena.de/simbio/index.php/Main_Page to treat WM anisotropy. While it would in principle also be possible to use anisotropic conductivities with FieldTrip-SimBio, this is currently not implemented using ft_prepare_headmodel. Johannes (in CC), who implemented Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: "Depending on your matlab skills and your available time, I could help you to give it a try though. It should be possible with using some direct function calls instead of the high-level fieldtrip-functions." 2) We recommend http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf on individual data. I could imagine that an atlas does a reasonable job w.r.t. the main bigger fiber tracts such as corpus callosum or pyramidal tracts, but that the finer details in the cortices are individual. We always measure T1, T2 and DTI from each subject and I personally do not have experience with such a group-level anisotropy compared to the individual one. Might be interesting to hear from others what they think!? BR Carsten Am 25.10.18 um 23:05 schrieb Ravi Mill: Dear Fieldtrippers I have applied the FEM simbio head modeling pipeline implemented in Fieldtrip to my EEG data. My understanding is that this pipeline assumes isotropic conductivities for 5 head compartments (as specified by cfg.conductivity in ft_prepare_headmodel). After reading some papers (e.g. Vorwerk et al 2014 https://doi.org/10.1016/j.neuroimage.2014.06.040), it seems like incorporating white matter conductivity anisotropy has a relatively small albeit significant effect on the source solution. I am interested in comparing FEM results when treating white matter as anisotropic. My questions are as follows: 1. Is there a way to implement the FEM simbio head model whilst treating WM as anisotropic within Fieldtrip? If so, how would one do this (or are there any resources available that demonstrate this)? 2. From previous papers and some simbio documentation (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples) it seems like diffusion MRI data is required to calculate the WM conductivity for each individual subject. I only have T1 and T2 scans for my subjects. So would it be possible to use WM anisotropic information obtained from some kind of diffusion MRI group average/atlas instead (accepting some loss in subject-level precision)? If so, does such a group average/atlas exist? Any help would be greatly appreciated! Thanks Ravi _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -- Prof. Dr.rer.nat. Carsten H. Wolters University of Münster Institute for Biomagnetism and Biosignalanalysis Malmedyweg 15 48149 Münster, Germany Phone: +49 (0)251 83 56904 +49 (0)251 83 56865 (secr.) Fax: +49 (0)251 83 56874 Email: carsten.wolters at uni-muenster.de Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From ekenaykut at gmail.com Mon Nov 5 18:41:35 2018 From: ekenaykut at gmail.com (Aykut Eken) Date: Mon, 5 Nov 2018 18:41:35 +0100 Subject: [FieldTrip] ICA inspection: Error using ft_icabrowser.m In-Reply-To: References: Message-ID: Hi Silvana, Can you check the match_str function? [seldat, sellay] = match_str(label, cfg.layout.label); if isempty(seldat) ft_error('labels in data and labels in layout do not match'); end Best Aykut > On 5 Nov 2018, at 18:23, SILVA PEREIRA, SILVANA wrote: > > > Hi Diego, > > Your suggestion works fine for ft_databrowser, but not for ft_icabrowser! > > best regards, > Silvana > > > El lun., 5 nov. 2018 a las 16:58, Diego Lozano-Soldevilla (>) escribió: > Hi Silvana, > Could you try the component viewmode option? > > cfg = []; > cfg.layout = 'acticap-64ch-standard'; > cfg.viewmode = 'component'; % Mode specifically suited to browse through ICA data > ft_databrowser(cfg, comp); > Best, > Diego > > > On Mon, Nov 5, 2018, 16:38 SILVA PEREIRA, SILVANA wrote: > Dear all, > > I'm trying to use the funtion ft_icabrowser.m, but I get the following error message: > > Error using topoplot_common (line 523) > labels in data and labels in layout do not match > > Error in ft_topoplotIC (line 184) > [cfg] = topoplot_common(cfg, comp); > > Error in ft_icabrowser (line 151) > ft_topoplotIC(cfgtopo, comp); > > I understand that the error is due to a mismatch between the labels of the cfg struct and the layout I'm using. I modified the entries of the struct in acticap-64ch-standard2.mat, where I removed four of the channels, since we have in addition RM, LM, Heog and Veog. Adding these four labels to the original label list does not solve the problem. Any idea to work around this issue? > > Thank you! > Silvana > > > Silvana Silva Pereira > /Postdoctoral Researcher/ > Center for Brain and Cognition > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From carsten.wolters at uni-muenster.de Tue Nov 6 12:10:36 2018 From: carsten.wolters at uni-muenster.de (Carsten Wolters) Date: Tue, 6 Nov 2018 12:10:36 +0100 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: References: <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> Message-ID: Hi Ravi, Marios (in CC) promised to send you the short Matlab-script that we use to transform the diffusion tensors to conductivity tensors using Tuch's effective medium approach. For this approach, please check e.g. the subsection "Calibrated Finite Element Head Model and Forward Solution" in https://link.springer.com/article/10.1007/s10548-017-0568-9 BR    Carsten Am 05.11.18 um 18:26 schrieb Ravi Mill: > > Hi Carsten and Johannes > > > Many thanks for responding, and for developing these great tools! > > > I'm in the process of acquiring a large diffusion MR dataset from > which I can hopefully create an 'averaged' atlas. From your > responses I think I have a sense of how to integrate the conductivity > tensors derived from this atlas with the Fieldtrip FEM pipeline. > > > But I was wondering if you had any advice on how to compute > these conductivity tensors in the first place? From the paper that > Carsten sent, It seems like the FDT program within FSL is what I need > to compute diffusion tensors from the raw diffusion images (steps 1-6 > from the FDT user guide > https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Processing_pipeline)? > Seemingly, these diffusion tensors need to then be converted to > conductivity tensors - any advice on how to do this (or if you could > point me to some example code) would be greatly appreciated. > > > Thanks > > Ravi > > > FDT/UserGuide - FslWiki - University of Oxford > > fsl.fmrib.ox.ac.uk > Eddy Current Correction. Eddy currents in the gradient coils induce > (approximate) stretches and shears in the diffusion weighted images. > These distortions are different for different gradient directions. > > > > Thanks again > Ravi > > > ------------------------------------------------------------------------ > *From:* fieldtrip on behalf of > Johannes Vorwerk > *Sent:* Monday, October 29, 2018 10:14:16 AM > *To:* FieldTrip discussion list > *Subject:* Re: [FieldTrip] Incorporating White Matter conductivity > anisotropy into FEM simbio > Dear Ravi, > > as Carsten already said, calculating FEM with anisotropic > conductivities is not directly supported by the FieldTrip-SimBio > implementation. However, if you are willing to invest a bit of time it > is possible to work around this. > > The „only“ thing that needs to be changed is the calculation of the > FEM stiffness matrix, which is performed by the routine > „calc_stiffness_matrix_val“ in the function sb_calc_stiff (usually > called from ft_prepare_headmodel). The problem is that FieldTrip does > not support anisotropic conductivities, so that you would have to call > calc_stiffness_matrix_val directly. You can see the correct call in > sb_calc_stiff. For anisotropic conductivities you have to replace the > input „cond“ by a #elements x 6 matrix containing your anisotropic > conductivities in the format "xx yy zz xy yz zx“. If you now follow > the normal FieldTrip-SimBio workflow using the resulting stiffness > matrix, you will get results for anisotropic conductivities. > > Best, > Johannes > >> Am 29.10.2018 um 12:31 schrieb Carsten Wolters >> > >: >> >> Dear Ravi, >> >> 1) You can use the pure SimBio-code from >> https://www.mrt.uni-jena.de/simbio/index.php/Main_Page >> >> to treat WM anisotropy. >> While it would in principle also be possible to use anisotropic >> conductivities with FieldTrip-SimBio, >> this is currently not implemented using ft_prepare_headmodel. >> Johannes (in CC), who implemented >> Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: >> "Depending on your matlab skills and your available time, I could >> help you to give it a >> try though. It should be possible with using some direct function >> calls instead of the high-level fieldtrip-functions." >> >> 2) We recommend >> http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf >> >> on individual data. I could imagine that an atlas does a reasonable >> job w.r.t. the main >> bigger fiber tracts such as corpus callosum or pyramidal tracts, but >> that the finer details >> in the cortices are individual. We always measure T1, T2 and DTI from >> each subject >> and I personally do not have experience with such a group-level >> anisotropy compared >> to the individual one. Might be interesting to hear from others what >> they think!? >> >> BR >>    Carsten >> >> >> >> Am 25.10.18 um 23:05 schrieb Ravi Mill: >>> Dear Fieldtrippers >>> >>> I have applied the FEM simbio head modeling pipeline implemented >>> in Fieldtrip to my EEG data. My understanding is that this pipeline >>> assumes isotropic conductivities for 5 head compartments (as >>> specified by cfg.conductivity in ft_prepare_headmodel). After >>> reading some papers (e.g. Vorwerk et al 2014 >>> https://doi.org/10.1016/j.neuroimage.2014.06.040 >>> ), >>> it seems like incorporating white matter conductivity anisotropy has >>> a relatively small albeit significant effect on the source solution. >>> I am interested in comparing FEM results when treating white matter >>> as anisotropic. My questions are as follows: >>> >>> 1. Is there a way to implement the FEM simbio head model whilst >>> treating WM as anisotropic within Fieldtrip? If so, how would >>> one do this (or are there any resources available that >>> demonstrate this)? >>> 2. From previous papers and some simbio documentation >>> (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples >>> ) >>> it seems like diffusion MRI data is required to calculate the WM >>> conductivity for each individual subject. I only have T1 and T2 >>> scans for my subjects. So would it be possible to use WM >>> anisotropic information obtained from some kind of diffusion >>> MRI group average/atlas instead (accepting some loss in >>> subject-level precision)? If so, does such a group average/atlas >>> exist? >>> >>> >>> Any help would be greatly appreciated! >>> >>> Thanks >>> Ravi >>> >>> >>> _______________________________________________ >>> fieldtrip mailing list >>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >>> https://doi.org/10.1371/journal.pcbi.1002202 >> >> >> -- >> Prof. Dr.rer.nat. Carsten H. Wolters >> University of Münster >> Institute for Biomagnetism and Biosignalanalysis >> Malmedyweg 15 >> 48149 Münster, Germany >> >> Phone: >> +49 (0)251 83 56904 >> +49 (0)251 83 56865 (secr.) >> >> Fax: >> +49 (0)251 83 56874 >> >> Email:carsten.wolters at uni-muenster.de >> Web:https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -- Prof. Dr.rer.nat. Carsten H. Wolters University of Münster Institute for Biomagnetism and Biosignalanalysis Malmedyweg 15 48149 Münster, Germany Phone: +49 (0)251 83 56904 +49 (0)251 83 56865 (secr.) Fax: +49 (0)251 83 56874 Email: carsten.wolters at uni-muenster.de Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From antoine.ducorps at orange.fr Tue Nov 6 12:44:17 2018 From: antoine.ducorps at orange.fr (Antoine Ducorps) Date: Tue, 6 Nov 2018 12:44:17 +0100 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger(fieldtrip Digest, Vol 96, Issue 4) In-Reply-To: References: Message-ID: <1D61E3DF-EEDF-473C-BF56-EA9A1E1772BC@orange.fr> Dear Eelke, I agree that a sharp 60 Hz peak is certainly an artefact. You mention that your projector is set to 120 Hz, but unless you use a very sophisticated one, the internal rate for most (if not all) commercial devices is still 60 Hz, and any input rate will be interpolated to that fundamental. In your case, every second frame would be displayed, or an average light value of two frames, I am not sure. You can check that on your technical manual, or alternating black/white frames and measuring the light output with a photodiode. If it really happens that your projector runs internally at 60 Hz, then you should also set your computer output at the same value, otherwise you don’t know what the interpolation or down sampling will do, most probably not documented by the vendor. Then, either you have a shielding problem with your projector electronics, or the 60 Hz rate is a real light effect in your stimulus, reflected in the visual cortex. Best, Antoine. > ------------------------------ > > Message: 3 > Date: Mon, 5 Nov 2018 17:21:05 +0100 > From: Eelke Spaak > To: FieldTrip discussion list > Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger > spectra > Message-ID: > > Content-Type: text/plain; charset="utf-8" > > Fellow FieldTrippers, > > I have identified two points of interest in the brain, between which I > want to compute connectivity metrics. One is in early visual cortex, > the other is in right temporal lobe, somewhat medial (compatible with > hippocampus, but exact interpretation not relevant right now). I > reconstructed activity for these grid points using LCMV beamformer (on > MEG data) and computed source-level Fourier spectra (taper = 'dpss', > tapsmofrq = 3) after applying a band-stop filter around 50 Hz and > harmonics. Using ft_connectivityanalysis, I computed both debiased > wPLI and Granger causality between the two points of interest. > > In both spectra, I see a clear peak at 60 Hz in the grand average > across 36 subjects, which is also there in the majority of individual > subjects (though very strong only in 2/36). Plots are attached. Now > this would be exciting news if indeed it turns out to be a true highly > band-limited gamma effect! > > But of course I suspect that this peak could very well be artifactual. > I can't think of any artifact source I may have missed that would > cause this, though. The projector refresh rate during the experiment > was set to 120 Hz, and not 60. (Also note this is European data so AC > frequency is 50 Hz and not 60, hence the band stop I mentioned > earlier.) > > Does anyone have any idea what might be causing this sharp peak? (A > genuine effect after all?) > > Thanks, > Eelke > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: granger-spectrum.png > Type: image/png > Size: 14990 bytes > Desc: not available > URL: > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: wpli-spectrum.png > Type: image/png > Size: 11765 bytes > Desc: not available > URL: > > ------------------------------ > > Message: 4 > Date: Mon, 5 Nov 2018 17:13:05 +0000 > From: "Schoffelen, J.M. (Jan Mathijs)" > To: FieldTrip discussion list > Subject: Re: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and > Granger spectra > Message-ID: > Content-Type: text/plain; charset="us-ascii" > > This looks like an artifact to me. > > In addition, the zigzag in the Granger spectra is suggestive of convergence issues in the non-parametric spectral matrix factorization in at least a few of your subjects. This is often caused by strong discontinuities in the spectra (spectral lines!) or poor behaviour of the time domain data at the edges of your epochs, causing spectral leakage. > > JM > > From antonakakismar at gmail.com Tue Nov 6 14:21:56 2018 From: antonakakismar at gmail.com (Marios Antonakakis) Date: Tue, 6 Nov 2018 14:21:56 +0100 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: References: <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> Message-ID: Hi Ravi, assuming that you work under Linux and you have already the DTI data registered to MRI, you need then to run the below script. *%%1, calculate conductivity tensor for every voxel* % mri_segmented_final, V1, ... L1 variables have ft MRI structure %skin,skull compacta, skull spongiosa, CSF, GM, WM conductivity = [0.4300, 0.0024, 0.0086, 1.7900, 0.3300, 0.1400]; cfg =[]; cfg.compartments = mri_segmented_final.anatomylabel; % anatomical labels, skin: 0, skull spongiosa 2 and show on... cfg.conductivity = conductivity; [condcell, s, fail] = sb_calcTensorCond_tuch(cfg,mri_segmented_final,V1,V2,V3,L1,L2,L3); *%% 2. assign the conductivity tensor with the hex mesh * [mesh.nodes,mesh.elements,mesh.labels]= read_vista_mesh('foo_mesh.v); mask = mri_segmented_final; mask.anatomy((mask.anatomy~=5) & (mask.anatomy~=6))=0; mask.anatomy((mask.anatomy==5) | (mask.anatomy==6))=1; tensors = sb_assiTensorCond(mask,mesh.nodes,mesh.elements,mesh.labels,condcell); write_vista_mesh('foo_mesh_aniso.v',mesh.nodes,mesh.elements,mesh.labels,tensors'); Do not hesitate to ask further questions. **Note that write_vista_mesh and read_vista_mesh are private ft functions.* Best regards, Marios ᐧ Στις Τρί, 6 Νοε 2018 στις 12:10 μ.μ., ο/η Carsten Wolters < carsten.wolters at uni-muenster.de> έγραψε: > Hi Ravi, > > Marios (in CC) promised to send you the short Matlab-script that we use to > transform the diffusion tensors > to conductivity tensors using Tuch's effective medium approach. > > For this approach, please check e.g. the subsection > "Calibrated Finite Element Head Model and Forward Solution" > in > https://link.springer.com/article/10.1007/s10548-017-0568-9 > > BR > Carsten > > Am 05.11.18 um 18:26 schrieb Ravi Mill: > > Hi Carsten and Johannes > > > Many thanks for responding, and for developing these great tools! > > > I'm in the process of acquiring a large diffusion MR dataset from which I > can hopefully create an 'averaged' atlas. From your responses I think I > have a sense of how to integrate the conductivity tensors derived from this > atlas with the Fieldtrip FEM pipeline. > > > But I was wondering if you had any advice on how to compute > these conductivity tensors in the first place? From the paper that Carsten > sent, It seems like the FDT program within FSL is what I need to compute > diffusion tensors from the raw diffusion images (steps 1-6 from the FDT > user guide > https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Processing_pipeline)? > Seemingly, these diffusion tensors need to then be converted to > conductivity tensors - any advice on how to do this (or if you could point > me to some example code) would be greatly appreciated. > > > Thanks > > Ravi > > > FDT/UserGuide - FslWiki - University of Oxford > > fsl.fmrib.ox.ac.uk > Eddy Current Correction. Eddy currents in the gradient coils induce > (approximate) stretches and shears in the diffusion weighted images. These > distortions are different for different gradient directions. > > > Thanks again > Ravi > > > ------------------------------ > *From:* fieldtrip > on behalf of Johannes Vorwerk > > *Sent:* Monday, October 29, 2018 10:14:16 AM > *To:* FieldTrip discussion list > *Subject:* Re: [FieldTrip] Incorporating White Matter conductivity > anisotropy into FEM simbio > > Dear Ravi, > > as Carsten already said, calculating FEM with anisotropic conductivities > is not directly supported by the FieldTrip-SimBio implementation. However, > if you are willing to invest a bit of time it is possible to work around > this. > > The „only“ thing that needs to be changed is the calculation of the FEM > stiffness matrix, which is performed by the routine > „calc_stiffness_matrix_val“ in the function sb_calc_stiff (usually called > from ft_prepare_headmodel). The problem is that FieldTrip does not support > anisotropic conductivities, so that you would have to call > calc_stiffness_matrix_val directly. You can see the correct call in > sb_calc_stiff. For anisotropic conductivities you have to replace the input > „cond“ by a #elements x 6 matrix containing your anisotropic conductivities > in the format "xx yy zz xy yz zx“. If you now follow the normal > FieldTrip-SimBio workflow using the resulting stiffness matrix, you will > get results for anisotropic conductivities. > > Best, > Johannes > > Am 29.10.2018 um 12:31 schrieb Carsten Wolters < > carsten.wolters at uni-muenster.de>: > > Dear Ravi, > > 1) You can use the pure SimBio-code from > https://www.mrt.uni-jena.de/simbio/index.php/Main_Page > > to treat WM anisotropy. > While it would in principle also be possible to use anisotropic > conductivities with FieldTrip-SimBio, > this is currently not implemented using ft_prepare_headmodel. Johannes (in > CC), who implemented > Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: > "Depending on your matlab skills and your available time, I could help you > to give it a > try though. It should be possible with using some direct function calls > instead of the high-level fieldtrip-functions." > > 2) We recommend > > http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf > > on individual data. I could imagine that an atlas does a reasonable job > w.r.t. the main > bigger fiber tracts such as corpus callosum or pyramidal tracts, but that > the finer details > in the cortices are individual. We always measure T1, T2 and DTI from each > subject > and I personally do not have experience with such a group-level anisotropy > compared > to the individual one. Might be interesting to hear from others what they > think!? > > BR > Carsten > > > > Am 25.10.18 um 23:05 schrieb Ravi Mill: > > Dear Fieldtrippers > > I have applied the FEM simbio head modeling pipeline implemented > in Fieldtrip to my EEG data. My understanding is that this pipeline > assumes isotropic conductivities for 5 head compartments (as specified by > cfg.conductivity in ft_prepare_headmodel). After reading some papers > (e.g. Vorwerk et al 2014 https://doi.org/10.1016/j.neuroimage.2014.06.040 > ), > it seems like incorporating white matter conductivity anisotropy has a > relatively small albeit significant effect on the source solution. I am > interested in comparing FEM results when treating white matter as > anisotropic. My questions are as follows: > > > 1. Is there a way to implement the FEM simbio head model whilst > treating WM as anisotropic within Fieldtrip? If so, how would one do this > (or are there any resources available that demonstrate this)? > 2. From previous papers and some simbio documentation ( > https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples > ) > it seems like diffusion MRI data is required to calculate the WM > conductivity for each individual subject. I only have T1 and T2 scans for > my subjects. So would it be possible to use WM anisotropic information > obtained from some kind of diffusion MRI group average/atlas instead > (accepting some loss in subject-level precision)? If so, does such a group > average/atlas exist? > > > Any help would be greatly appreciated! > > Thanks > Ravi > > > > _______________________________________________ > fieldtrip mailing listhttps://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 > > > > -- > Prof. Dr.rer.nat. Carsten H. Wolters > University of Münster > Institute for Biomagnetism and Biosignalanalysis > Malmedyweg 15 > 48149 Münster, Germany > > Phone: > +49 (0)251 83 56904 > +49 (0)251 83 56865 (secr.) > > Fax: > +49 (0)251 83 56874 > > Email: carsten.wolters at uni-muenster.de > Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ > > > > _______________________________________________ > fieldtrip mailing listhttps://mailman.science.ru.nl/mailman/listinfo/fieldtriphttps://doi.org/10.1371/journal.pcbi.1002202 > > > > -- > Prof. Dr.rer.nat. Carsten H. Wolters > University of Münster > Institute for Biomagnetism and Biosignalanalysis > Malmedyweg 15 > 48149 Münster, Germany > > Phone: > +49 (0)251 83 56904 > +49 (0)251 83 56865 (secr.) > > Fax: > +49 (0)251 83 56874 > > Email: carsten.wolters at uni-muenster.de > Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ > > -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- function condtensor = sb_assiTensorCond(mask,nodes,elem,labels,condcell) %% condtensor = zeros(9,size(elem,1)); count = 0; for i = 1 : size(elem) pos = nodes(elem(i,7),:); pos = round(pos); if (~(mask.anatomy(pos(1),pos(2),pos(3)) == 0)) if labels(i) ~= 5 && labels(i) ~= 6 labels(i) disp('not 5 or 6') end count = count+1; for j = 1 : 3 for k = 1 : 3 condtensor((j-1)*3 + k,i) = condcell{pos(1),pos(2),pos(3)}(j,k); end end end end count end -------------- next part -------------- function [condtensor, s, fail] = sb_calcTensorCond_tuch(cfg,mask,V1,V2,V3,L1,L2,L3) check1 = isequal(V1.dim,V2.dim,V3.dim); check2 = isequal(V1.dim,size(V1.anatomy),size(V2.anatomy),size(V3.anatomy)); check3 = isequal(L1.dim,L2.dim,L3.dim); check4 = isequal(L1.dim,size(L1.anatomy),size(L2.anatomy),size(L3.anatomy)); check5 = isequal(V1.dim(1:3),L1.dim,mask.dim); check6 = ~(isempty(cfg)||isempty(cfg.conductivity)); check7 = ~(length(cfg.conductivity)<6); if (check1 && check2 && check3 && check4 && check5 && check6 && check7) fail = 0; failnan = 0; condtensor = cell(mask.dim); N = zeros(1,3); vol = zeros(1,3); for i = 1 : V1.dim(1) for j = 1 : V1.dim(2) for k = 1 : V1.dim(3) if (((mask.anatomy(i,j,k) == 5)|(mask.anatomy(i,j,k) == 6))&(L1.anatomy(i,j,k)>10^-4)&(L2.anatomy(i,j,k)>10^-4)&(L3.anatomy(i,j,k)>10^-5)) S = zeros(3); S(:,1) = V1.anatomy(i,j,k,:); S(:,2) = V2.anatomy(i,j,k,:); S(:,3) = V3.anatomy(i,j,k,:); if(norm(S'*S-diag([1,1,1]),2)>10e-7) S(:,1) = S(:,1) / norm(S(:,1)); S(:,2) = S(:,2) - (S(:,1)'*S(:,2))*S(:,1); S(:,2) = S(:,2) / norm(S(:,2)); S(:,3) = S(:,3) - (S(:,1)'*S(:,3))*S(:,1) - (S(:,2)'*S(:,3))*S(:,2); S(:,3) = S(:,3) / norm(S(:,3)); failnan = failnan + 1; end if(sum(sum(isnan(S),1),2)>0) condtensor{i,j,k} = cfg.conductivity(mask.anatomy(i,j,k))*diag([1,1,1]); else D = diag([L1.anatomy(i,j,k),L2.anatomy(i,j,k),L3.anatomy(i,j,k)]); T = S * D * S'; %T = hier skalieren condtensor{i,j,k} = T; if (mask.anatomy(i,j,k) == 5) N(1) = N(1) + 1; vol(1) = vol(1) + L1.anatomy(i,j,k)*L2.anatomy(i,j,k)*L3.anatomy(i,j,k); elseif (mask.anatomy(i,j,k) == 6) N(2) = N(2) + 1; vol(2) = vol(2) + L1.anatomy(i,j,k)*L2.anatomy(i,j,k)*L3.anatomy(i,j,k); % elseif (mask.anatomy(i,j,k) == 3) % N(3) = N(3) + 1; % vol(3) = vol(3) + L1.anatomy(i,j,k)*L2.anatomy(i,j,k)*L3.anatomy(i,j,k); end end elseif(((mask.anatomy(i,j,k) == 5)|(mask.anatomy(i,j,k) == 6))) condtensor{i,j,k} = cfg.conductivity(mask.anatomy(i,j,k))*diag([1,1,1]); if(((mask.anatomy(i,j,k) == 5)|(mask.anatomy(i,j,k) == 6))&(~((L1.anatomy(i,j,k)>10^-4)&(L2.anatomy(i,j,k)>10^-4)&(L3.anatomy(i,j,k)>10^-5)))) fail = fail + 1; end else condtensor{i,j,k} = zeros(3); end end end end d(1) = vol(1) / N(1); d(1) = d(1)^(1/3); d(2) = vol(2) / N(2); d(2) = d(2)^(1/3); % d(3) = vol(3) / N(3); % d(3) = d(3)^(1/3); s = d(1)*cfg.conductivity(5)+d(2)*cfg.conductivity(6); s = s / (d(1)^2 + d(2)^2); fprintf('s*d = %.6f\n',s*(d(1)+d(2))) fprintf('s = %.6f\n',s) % failper = fail / (N(1)+N(2)); mx = -100000; mn = 100000; for i = 1 : V1.dim(1) for j = 1 : V1.dim(2) for k = 1 : V1.dim(3) if (((mask.anatomy(i,j,k) == 5)||(mask.anatomy(i,j,k) == 6))&(L2.anatomy(i,j,k)>10^-4)&(L3.anatomy(i,j,k)>10^-5)) condtensor{i,j,k} = s * condtensor{i,j,k}; %keep outliers wma bigger from the highest cond around cond of wm if max(condtensor{i,j,k}(:)) > cfg.conductivity(4) condtensor{i,j,k}(1:1+size(condtensor{i,j,k},1):end) = cfg.conductivity(6); end if mx < max(max(condtensor{i,j,k})) mx = max(max(condtensor{i,j,k})); fprintf('mx %d, %d, %d\n',i,j,k) end if mn > min(min(condtensor{i,j,k})) mn = min(min(condtensor{i,j,k})); fprintf('mn %d, %d, %d\n',i,j,k) end end end end end mn mx end end From silvana.silva at upf.edu Tue Nov 6 16:01:27 2018 From: silvana.silva at upf.edu (SILVA PEREIRA, SILVANA) Date: Tue, 6 Nov 2018 16:01:27 +0100 Subject: [FieldTrip] ICA inspection: Error using ft_icabrowser.m In-Reply-To: References: Message-ID: Ok, I finally found a solution, just modifying line 53 in ft_icabrowser from: lay = ft_prepare_layout(cfglay, comp); to: lay = ft_prepare_layout(cfglay); since the layout somehow gets modified if I add the ica data structure (comp) in the input parameters. Now it works! Best regards, Silvana El lun., 5 nov. 2018 a las 18:44, Aykut Eken () escribió: > Hi Silvana, > > Can you check the match_str function? > > [seldat, sellay] = match_str(label, cfg.layout.label); > if isempty(seldat) > ft_error('labels in data and labels in layout do not match'); > end > > Best > > Aykut > > On 5 Nov 2018, at 18:23, SILVA PEREIRA, SILVANA > wrote: > > > Hi Diego, > > Your suggestion works fine for ft_databrowser, but not for ft_icabrowser! > > best regards, > Silvana > > > El lun., 5 nov. 2018 a las 16:58, Diego Lozano-Soldevilla (< > dlozanosoldevilla at gmail.com>) escribió: > >> Hi Silvana, >> Could you try the component viewmode option? >> >> cfg = []; >> cfg.layout = 'acticap-64ch-standard'; >> cfg.viewmode = 'component'; % Mode specifically suited to browse through ICA data >> ft_databrowser(cfg, comp); >> >> Best, >> Diego >> >> >> On Mon, Nov 5, 2018, 16:38 SILVA PEREIRA, SILVANA > wrote: >> >>> Dear all, >>> >>> I'm trying to use the funtion ft_icabrowser.m, but I get the following >>> error message: >>> >>> Error using topoplot_common (line 523) >>> labels in data and labels in layout do not match >>> >>> Error in ft_topoplotIC (line 184) >>> [cfg] = topoplot_common(cfg, comp); >>> >>> Error in ft_icabrowser (line 151) >>> ft_topoplotIC(cfgtopo, comp); >>> >>> I understand that the error is due to a mismatch between the labels of >>> the cfg struct and the layout I'm using. I modified the entries of the >>> struct in acticap-64ch-standard2.mat, where I removed four of the channels, >>> since we have in addition RM, LM, Heog and Veog. Adding these four labels >>> to the original label list does not solve the problem. Any idea to work >>> around this issue? >>> >>> Thank you! >>> Silvana >>> >>> >>> *Silvana Silva Pereira* >>> /Postdoctoral Researcher/ >>> Center for Brain and Cognition >>> >>> [image: Universitat Pompeu Fabra, Barcelona] >>> _______________________________________________ >>> fieldtrip mailing list >>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >>> https://doi.org/10.1371/journal.pcbi.1002202 >>> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From tineke.snijders at donders.ru.nl Wed Nov 7 13:28:46 2018 From: tineke.snijders at donders.ru.nl (Snijders, T.M. (Tineke)) Date: Wed, 7 Nov 2018 12:28:46 +0000 Subject: [FieldTrip] ft_rejectvisual reduces trial length Message-ID: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl> Hi, I am using trials of different lengths, which seems to give a problem in ft_rejectvisual. It wasn't a problem before (I think in 2016) when I did the same analysis. When using ft_rejectvisual, in the current fieldtrip version in the output data all trial lengths are reduced to the shortest trial-length, cutting away the remainder of the trial. In the command window it gives a warning of 'inconsistent sampling info'. Is there a workaround to still get the full trial-lengths for the artifact rejected data? I do want to keep the complete trials at this point. Thanks, Tineke -- Dr T.M. Snijders Research Staff Max Planck Institute for Psycholinguistics, Nijmegen www.ru.nl/people/donders/snijders-t http://www.mpi.nl/departments/language-development MPI | Wundtlaan 1 | Room 246 | tel 024 35 21246 | PO Box 310 | 6500 AH Nijmegen -------------- next part -------------- An HTML attachment was scrubbed... URL: From vincent.fontanier at inserm.fr Wed Nov 7 13:53:52 2018 From: vincent.fontanier at inserm.fr (vincent.fontanier at inserm.fr) Date: Wed, 07 Nov 2018 13:53:52 +0100 Subject: [FieldTrip] Spike-field analysis (combine freq and spike ; using ft_spiketriggeredspectrum) Message-ID: Hi everybody! I want to do some spike-field analysis on my dataset and have some questions regarding how fieldtrip handle such data and about the use of some of the functions related to this topic. 1. I have not found a fieldtrip way to combine freq structure (typically output from ft_freqanalysis) and spike structures. If I got it right, the fieldtrip pipeline to do spike-field is as follow: assuming filt_trials is the epoched LFP and spike is a fieldtrip spike structure · EPOCH the spike data like the LFP % spike to trials based on the epoched raw signal cfg = []; cfg.hdr = filt_trials.hdr; % contains information for conversion of samples to timestamps cfg.trlunit = 'samples'; cfg.trl = filt_trials.cfg.trl; % now in samples spikeTrials = ft_spike_maketrials(cfg,spike); · Compute the spike triggered spectrum cfg = []; cfg.method = 'mtmfft'; %'mtmconvol' is more powerful with many neurons and great firing rate cfg.foilim = [0 40]; % cfg.timwin determines spacing cfg.taper = 'hanning'; cfg.timwin = [-0.1 0.1]; %time around each spike stsConvol = ft_spiketriggeredspectrum(cfg, filt_trials, spikeTrials); · Make some analysis on the spike triggered spectrum cfg = []; cfg.method = 'ppc0'; % compute the Pairwise Phase Consistency cfg.avgoverchan = 'unweighted'; % weight spike-LFP phases irrespective of LFP power cfg.timwin = 'all'; % compute over all available spikes in the window cfg.latency = [-1 3]; statSts = ft_spiketriggeredspectrum_stat(cfg, stsConvol ); However having already performed time-frequency decomposition of all my LFP data I find this inefficient having to compute them again. Furthermore the methods of TF decomposition implemented in ft_spiketriggeredspectrum are much more limited than the one in ft_freqanalysis. So is there a way to combine the two together? A workaround is to realign the two together taking the sample of each spike in spikeTrials.timestamp{1}; and the start and end sample of each trial from the freq structure (freq.cfg.previous.trl. But this does not keep the fieldtrip way of formatting the data. Moreover this would require adjustments for further fieldtrip computations such as pairwise-phase consistency analysis using ft_spiketriggeredspectrum_stat. 2. (Useless if there is a solution to 1.) In ft_spiketriggeredspectrum you can provide a time window around each spike in the input to compute the spectrum. However the output spectrum is not time-resolved. Basically the output is just the average spectrum during the provided time window. Thus it is impossible to reconstruct the spike triggered time-frequency representation of the data. It is possible to run many iteration of ft_spiketriggeredspectrum for each timebin and store the output in a {chan}_spike_lfpchan_freq_time cell but it sounds like a very inefficient way to go. Therefore I am wondering if there is one way to do that more efficiently, for example an option that I missed in ft_spiketriggeredspectrum? Additionally could this time-resolved spiketriggeredspectrum output be used as an input to ft_spiketriggeredspectrum_stat in order to have a time-resolved output of the analysis? Many thanks! -- Vincent Fontanier Inserm U1208 (ex-U846) Stem Cell and Brain Research Institute Team Neurobiology of Executive Functions https://www.labex-cortex.com/en/team/neurobiology-executive-functions 18 av Du Doyen Lepine 69675 Bron CEDEX, (Lyon) FRANCE From tineke.snijders at donders.ru.nl Wed Nov 7 14:15:30 2018 From: tineke.snijders at donders.ru.nl (Snijders, T.M. (Tineke)) Date: Wed, 7 Nov 2018 13:15:30 +0000 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger(fieldtrip Digest, Vol 96, Issue 4) In-Reply-To: <1D61E3DF-EEDF-473C-BF56-EA9A1E1772BC@orange.fr> References: , <1D61E3DF-EEDF-473C-BF56-EA9A1E1772BC@orange.fr> Message-ID: <1541596532255.87599@donders.ru.nl> Hi Eelke, Yes I would also interpret this as artefact due to the projector (or: real visual entrainment to the projected visual stimuli). Your effect reminds me of a very clear 60 Hz response I had in my data, which appeared to be related to the refresh rate of the screen. We ran a few subjects with a refresh rate of 75 Hz and then the frequency mostly shifted to 75 Hz. See Snijders et al 2013, https://doi.org/10.1016/j.nicl.2013.06.015 Best, Tineke ________________________________________ From: fieldtrip on behalf of Antoine Ducorps Sent: Tuesday, November 6, 2018 12:44 PM To: fieldtrip at science.ru.nl Subject: Re: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger(fieldtrip Digest, Vol 96, Issue 4) Dear Eelke, I agree that a sharp 60 Hz peak is certainly an artefact. You mention that your projector is set to 120 Hz, but unless you use a very sophisticated one, the internal rate for most (if not all) commercial devices is still 60 Hz, and any input rate will be interpolated to that fundamental. In your case, every second frame would be displayed, or an average light value of two frames, I am not sure. You can check that on your technical manual, or alternating black/white frames and measuring the light output with a photodiode. If it really happens that your projector runs internally at 60 Hz, then you should also set your computer output at the same value, otherwise you don’t know what the interpolation or down sampling will do, most probably not documented by the vendor. Then, either you have a shielding problem with your projector electronics, or the 60 Hz rate is a real light effect in your stimulus, reflected in the visual cortex. Best, Antoine. > ------------------------------ > > Message: 3 > Date: Mon, 5 Nov 2018 17:21:05 +0100 > From: Eelke Spaak > To: FieldTrip discussion list > Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger > spectra > Message-ID: > > Content-Type: text/plain; charset="utf-8" > > Fellow FieldTrippers, > > I have identified two points of interest in the brain, between which I > want to compute connectivity metrics. One is in early visual cortex, > the other is in right temporal lobe, somewhat medial (compatible with > hippocampus, but exact interpretation not relevant right now). I > reconstructed activity for these grid points using LCMV beamformer (on > MEG data) and computed source-level Fourier spectra (taper = 'dpss', > tapsmofrq = 3) after applying a band-stop filter around 50 Hz and > harmonics. Using ft_connectivityanalysis, I computed both debiased > wPLI and Granger causality between the two points of interest. > > In both spectra, I see a clear peak at 60 Hz in the grand average > across 36 subjects, which is also there in the majority of individual > subjects (though very strong only in 2/36). Plots are attached. Now > this would be exciting news if indeed it turns out to be a true highly > band-limited gamma effect! > > But of course I suspect that this peak could very well be artifactual. > I can't think of any artifact source I may have missed that would > cause this, though. The projector refresh rate during the experiment > was set to 120 Hz, and not 60. (Also note this is European data so AC > frequency is 50 Hz and not 60, hence the band stop I mentioned > earlier.) > > Does anyone have any idea what might be causing this sharp peak? (A > genuine effect after all?) > > Thanks, > Eelke > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: granger-spectrum.png > Type: image/png > Size: 14990 bytes > Desc: not available > URL: > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: wpli-spectrum.png > Type: image/png > Size: 11765 bytes > Desc: not available > URL: > > ------------------------------ > > Message: 4 > Date: Mon, 5 Nov 2018 17:13:05 +0000 > From: "Schoffelen, J.M. (Jan Mathijs)" > To: FieldTrip discussion list > Subject: Re: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and > Granger spectra > Message-ID: > Content-Type: text/plain; charset="us-ascii" > > This looks like an artifact to me. > > In addition, the zigzag in the Granger spectra is suggestive of convergence issues in the non-parametric spectral matrix factorization in at least a few of your subjects. This is often caused by strong discontinuities in the spectra (spectral lines!) or poor behaviour of the time domain data at the edges of your epochs, causing spectral leakage. > > JM > > _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 From M.vanEs at donders.ru.nl Wed Nov 7 17:23:54 2018 From: M.vanEs at donders.ru.nl (Es, M.W.J. van (Mats)) Date: Wed, 7 Nov 2018 16:23:54 +0000 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra In-Reply-To: References: Message-ID: <3bda2dd4a5374570998b2f1d951a525d@EXPRD06.hosting.ru.nl> Hi Eelke, I actually had the exact same issue with one of my pilot datasets, recorded in February this year. It was recorded from the same MEG system in Nijmegen. I only found out a while later, so it wasn't possible to check if anything in the setup had changed. I assumed someone changed the refresh rate to 60 Hz and forgot to set it to the default 120 Hz afterwards, but I can't be certain. I do remember thinking whether it could be caused by the power shortages in Eastern Europe that were going on around that time, having heard that this affected internal circuitry of electrical devices. Not sure if it could cause this though. When were your data recorded? In the end I worked around this by putting a dft filter on 60 Hz, which worked reasonably well. Hope this mystery gets solved at some point! Cheers, Mats -----Original Message----- From: Eelke Spaak Sent: maandag 5 november 2018 17:21 To: FieldTrip discussion list Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra Fellow FieldTrippers, I have identified two points of interest in the brain, between which I want to compute connectivity metrics. One is in early visual cortex, the other is in right temporal lobe, somewhat medial (compatible with hippocampus, but exact interpretation not relevant right now). I reconstructed activity for these grid points using LCMV beamformer (on MEG data) and computed source-level Fourier spectra (taper = 'dpss', tapsmofrq = 3) after applying a band-stop filter around 50 Hz and harmonics. Using ft_connectivityanalysis, I computed both debiased wPLI and Granger causality between the two points of interest. In both spectra, I see a clear peak at 60 Hz in the grand average across 36 subjects, which is also there in the majority of individual subjects (though very strong only in 2/36). Plots are attached. Now this would be exciting news if indeed it turns out to be a true highly band-limited gamma effect! But of course I suspect that this peak could very well be artifactual. I can't think of any artifact source I may have missed that would cause this, though. The projector refresh rate during the experiment was set to 120 Hz, and not 60. (Also note this is European data so AC frequency is 50 Hz and not 60, hence the band stop I mentioned earlier.) Does anyone have any idea what might be causing this sharp peak? (A genuine effect after all?) Thanks, Eelke From maria.hakonen at gmail.com Thu Nov 8 08:35:20 2018 From: maria.hakonen at gmail.com (Maria Hakonen) Date: Thu, 8 Nov 2018 09:35:20 +0200 Subject: [FieldTrip] ft_mergealgin: high residual variance? In-Reply-To: References: <863F96C2-45E4-4441-B330-6C0292880D72@donders.ru.nl> Message-ID: Dear FieldTrip experts, Could the reason for the increased variance be that I have magnetometer data? However, the ft_megrealign and ft_prepare_headmodel don't have an option to specify whether I have magnetometers or gradiometers. Could you please let me know whether co-registration is needed before ft_megrealign (it is not needed with maxfilter)? Thank you already in advance! Best, Maria pe 2. marrask. 2018 klo 8.37 Maria Hakonen (maria.hakonen at gmail.com) kirjoitti: > Hi Jan-Mathias! > > Thank you for the answer! > I changed the units of gradiometers and head model to cm. > This clearly decreased the residual variances. > Also, singleshell seems to work better than localspheres. > > However, the transformation seems to still increase the residual variance > a lot. > > Here are some examples: > original -> template RV 407.64 % > original -> original RV 9.85 % > original -> template -> original RV 10.75 % > realigning trial 706 > original -> template RV 393.13 % > original -> original RV 9.00 % > original -> template -> original RV 9.90 % > realigning trial 707 > original -> template RV 362.10 % > original -> original RV 8.33 % > original -> template -> original RV 9.18 % > realigning trial 708 > original -> template RV 377.15 % > original -> original RV 9.43 % > original -> template -> original RV 10.33 % > > The code is now as follows: > load([data_path nameList{subj} '.mat']); > grad = datafinal.grad; > hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); > > cfg = []; > cfg.method = 'singlesphere'; > cfg.geom = hs; > cfg.grad = grad; > cfg.feedback = true; > vol = ft_prepare_headmodel(cfg); > > vol = ft_convert_units(vol,'cm'); > grad = ft_convert_units(grad,'cm'); > cfg = []; > cfg.template = template; > cfg.inwardshift = 2.5; > cfg.feedback ='no'; > cfg.vol = vol; > data = ft_megrealign(cfg, data); > > Best, > Maria > > to 1. marrask. 2018 klo 10.35 Schoffelen, J.M. (Jan Mathijs) ( > jan.schoffelen at donders.ru.nl) kirjoitti: > >> Hi Maria, >> >> Are you sure about the units in your headmodel (and gradiometers)? Using >> the value 1.0 as an inwardshift parameter suggests ‘cm’. Not sure what will >> happen when by accident the hs_file is in ‘m’ (which, as far as I know, is >> usually the case). >> >> Best wishes, >> >> Jan-Mathijs >> >> J.M.Schoffelen, MD PhD >> Senior Researcher, VIDI-fellow - PI, language in interaction >> Telephone: +31-24-3614793 >> Physical location: room 00.028 >> Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands >> >> >> >> >> On 29 Oct 2018, at 13:33, Maria Hakonen wrote: >> >> Dear FieldTrip experts, >> >> I have run ft_mergealign across subjects to align the head positions. >> However, the residual variance between the original and the realigned data >> seems to be high: >> >> original -> template RV 21232.46 % >> original -> original RV 36.96 % >> original -> template -> original RV 9579.95 % >> >> Could someone please let me know what would be the largest acceptable >> change in the residual variance, and what should I do if the residual >> variance is too high? Does the increase in residual variance mean that >> there is a large shift in the head position? >> >> I have used ft_mergealign as follows: >> >> template = list of subjects (i.e. I want to calculate an average head >> position over the subjects) >> >> grad = data.grad; >> hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); >> vol = ft_headmodel_localspheres(hs,grad); >> >> cfg = []; >> cfg.template = template; >> cfg.inwardshift = 1.0; >> cfg.vol = vol; >> data_aligned = ft_megrealign(cfg, data); >> >> Best, >> Maria >> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> >> >> >> >> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> > -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Thu Nov 8 09:33:17 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Thu, 8 Nov 2018 09:33:17 +0100 Subject: [FieldTrip] ft_rejectvisual reduces trial length In-Reply-To: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl> References: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl> Message-ID: Hi Tineke, That sounds like a bug to me. You could consider filing it: https://github.com/fieldtrip/fieldtrip/issues Meanwhile, as a workaround, I usually ensure that one of the columns of my data.trialinfo contains unique trial identifiers (e.g. data.trialinfo(:,end+1) = 1:numel(data.trial);). Then, after datclean = ft_rejectvisual(cfg, data), the datclean.trialinfo(:,end) will contain those trial IDs that are kept. Store those IDs somewhere and then you can select the appropriate trials from the original data using ft_selectdata. Hope that helps, Eelke On Wed, 7 Nov 2018 at 13:28, Snijders, T.M. (Tineke) wrote: > > Hi, > > > > I am using trials of different lengths, which seems to give a problem in ft_rejectvisual. It wasn’t a problem before (I think in 2016) when I did the same analysis. > > > > When using ft_rejectvisual, in the current fieldtrip version in the output data all trial lengths are reduced to the shortest trial-length, cutting away the remainder of the trial. In the command window it gives a warning of ‘inconsistent sampling info’. > > > > Is there a workaround to still get the full trial-lengths for the artifact rejected data? I do want to keep the complete trials at this point. > > > > Thanks, > > > > Tineke > > > > -- > Dr T.M. Snijders > Research Staff > Max Planck Institute for Psycholinguistics, Nijmegen > > www.ru.nl/people/donders/snijders-t > > http://www.mpi.nl/departments/language-development > > MPI | Wundtlaan 1 | Room 246 | tel 024 35 21246 | PO Box 310 | 6500 AH Nijmegen > > > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From e.spaak at donders.ru.nl Thu Nov 8 09:51:51 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Thu, 8 Nov 2018 09:51:51 +0100 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra In-Reply-To: <3bda2dd4a5374570998b2f1d951a525d@EXPRD06.hosting.ru.nl> References: <3bda2dd4a5374570998b2f1d951a525d@EXPRD06.hosting.ru.nl> Message-ID: Thanks a lot JM, Lasha, Antoine, Tineke, & Mats for all the fast responses! Just to be clear: I agree that it's extremely unlikely that the peak reflects an endogenous rhythm. @Antoine: the projector is supposed to be very high-end (vPixx Propixx) and I've used it in the past at 1440 Hz, which worked well. So I would assume it's not a matter of mismatch between the GPU refresh rate and the projector's. But this is worth checking with a photodiode. Also I guess it's possible that I forgot to check the computer's refresh rate on some of the recording sessions and that it was actually 60 Hz instead of 120. For now I think I will deal with this with a notch filter. @Mats: the data were recorded between April and June of this year (2018). Cheers, Eelke On Wed, 7 Nov 2018 at 17:23, Es, M.W.J. van (Mats) wrote: > > Hi Eelke, > > I actually had the exact same issue with one of my pilot datasets, recorded in February this year. It was recorded from the same MEG system in Nijmegen. I only found out a while later, so it wasn't possible to check if anything in the setup had changed. I assumed someone changed the refresh rate to 60 Hz and forgot to set it to the default 120 Hz afterwards, but I can't be certain. I do remember thinking whether it could be caused by the power shortages in Eastern Europe that were going on around that time, having heard that this affected internal circuitry of electrical devices. Not sure if it could cause this though. When were your data recorded? > In the end I worked around this by putting a dft filter on 60 Hz, which worked reasonably well. > > Hope this mystery gets solved at some point! > Cheers, > Mats > > -----Original Message----- > From: Eelke Spaak > Sent: maandag 5 november 2018 17:21 > To: FieldTrip discussion list > Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra > > Fellow FieldTrippers, > > I have identified two points of interest in the brain, between which I want to compute connectivity metrics. One is in early visual cortex, the other is in right temporal lobe, somewhat medial (compatible with hippocampus, but exact interpretation not relevant right now). I reconstructed activity for these grid points using LCMV beamformer (on MEG data) and computed source-level Fourier spectra (taper = 'dpss', tapsmofrq = 3) after applying a band-stop filter around 50 Hz and harmonics. Using ft_connectivityanalysis, I computed both debiased wPLI and Granger causality between the two points of interest. > > In both spectra, I see a clear peak at 60 Hz in the grand average across 36 subjects, which is also there in the majority of individual subjects (though very strong only in 2/36). Plots are attached. Now this would be exciting news if indeed it turns out to be a true highly band-limited gamma effect! > > But of course I suspect that this peak could very well be artifactual. > I can't think of any artifact source I may have missed that would cause this, though. The projector refresh rate during the experiment was set to 120 Hz, and not 60. (Also note this is European data so AC frequency is 50 Hz and not 60, hence the band stop I mentioned > earlier.) > > Does anyone have any idea what might be causing this sharp peak? (A genuine effect after all?) > > Thanks, > Eelke > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From Silvia.Formica at UGent.be Thu Nov 8 11:40:06 2018 From: Silvia.Formica at UGent.be (Silvia Formica) Date: Thu, 8 Nov 2018 10:40:06 +0000 Subject: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage In-Reply-To: References: <1541070268890.93169@donders.ru.nl>, Message-ID: <1541673605780.63773@UGent.be> Dear Cecilia and Tom, I am in a similar situation to the one described by Cecilia, so I thought of asking you for a suggestion. I have a dataset locked to the onset of the cue, and I would like to use the same baseline I used in this dataset to baseline-correct also the same dataset locked to the onset of the target. I tried the solution Tom suggested, but it is not working for me. The reason is that I preprocessed the cue-locked and target-locked datasets separately, therefore they end up having a slightly different number of trials. Do you have any idea of how this could be solved? Would it make sense to baseline-correct at the grandaverage level? Another option I have been thinking about is to use ft_redefinetrial. In this case I have other problems, though. If I try to use the ft_redefinetrial function after preprocessing and cleaning my cue-locked dataset, it will output all the trials in the raw data (therefore not accounting for the trial rejection I performed on the cue-locked dataset). Is this the right way to use this function or am I missing something? Thanks in advance for any input and sorry if my question is not very clear (still a newbie!) Best, Silvia ________________________________ From: fieldtrip on behalf of Tom Marshall Sent: 02 November 2018 23:29 To: FieldTrip discussion list Subject: Re: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage Hey Cecilia, I guess you could do that with a combination of 'ft_selectdata', and 'ft_math'. Let's say you have datasets called 'stimlocked' and 'cuelocked' You'd do something like: % cut out baseline from cuelocked cfg = []; cfg.latency = [-0.5 0]; % or whatever your baseline window is baseline = ft_selectdata(cfg, cuelocked); % subtract baseline from stimlocked cfg = []; cfg.operation = 'subtract'; baseline_corrected_stimlocked = ft_math(cfg, stimlocked, baseline); I think that ft_math allows you to input algebraic expressions too. So if you wanted to do, for example, a 'relchange' baseline correction you should substitute (something like) cfg.operation = '(x1-x2)/x2'; Best, Tom ________________________________ From: fieldtrip on behalf of Mazzetti, C. (Cecilia) Sent: 01 November 2018 11:04:23 To: fieldtrip at science.ru.nl Subject: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage Dear everyone, I have some stimulus locked data which I have to baseline with respect to the same data but cue locked. I was wondering whether there is a setting in fieldtrip which allows to do that (i.e. set the baseline timw window according to a different dataset, the cue-locked data) and applying it to the stimulus locked data. Thanks in advance! Best, Cecilia Cecilia Mazzetti - Ph.D. Candidate Donders Centre for Cognitive Neuroimaging, room 0.068 Kapittelweg 29 | 6525 EN Nijmegen -------------- next part -------------- An HTML attachment was scrubbed... URL: From afsamani at hst.aau.dk Thu Nov 8 12:29:29 2018 From: afsamani at hst.aau.dk (Afshin Samani) Date: Thu, 8 Nov 2018 11:29:29 +0000 Subject: [FieldTrip] explore cluster statistics Message-ID: <2E4AC5E2A691FE4B89DA44B0E70DF70A014154A5E6@AD-EXCHMBX3-3.aau.dk> Hi, I have never used fieldtrip before but I was trying to get familiar with its statistical analysis tools. I tried to run the online example from: http://www.fieldtriptoolbox.org/example/use_simulated_erps_to_explore_cluster_statistics I am using MATLAB Version: 9.4.0.813654 (R2018a) and I downloaded that latest version of fieldtrip on 5 nov 2018 I face two errors: first at ft_math function: Error using ft_math (line 151) the requested parameter is not present in the data Error in Nonparametric_statistical_testingEEGandMEG_data (line 45) difference = ft_math(cfg, timelock1, timelock2); % line 137 of ft_datatype_timelock removes the fields and if I comment that line out, I face another error at ft_timelcokstatistics Error using chol Matrix must be square. Error in montecarlo (line 13) R = chol(Sigma); % Y = R^{-T}X has covariance I Error in ft_timelockstatistics (line 185) [stat] = statmethod(cfg, dat, design); Error in Nonparametric_statistical_testingEEGandMEG_data (line 63) stat = ft_timelockstatistics(cfg, timelock1, timelock2); Any comment? Best regards, Associate Prof. Afshin Samani, PhD Sport Sciences Dept. of Health Science and Technology Aalborg University, Aalborg Denmark [Description: Beskrivelse: AAU_LINE_blue_rgb] -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image001.png Type: image/png Size: 4637 bytes Desc: image001.png URL: From hesham.elshafei at inserm.fr Thu Nov 8 16:40:42 2018 From: hesham.elshafei at inserm.fr (Hesham ElShafei) Date: Thu, 08 Nov 2018 16:40:42 +0100 Subject: [FieldTrip] Software Developer Opportunity in Lyon ! Message-ID: Hello fieldtrippers !! Our team is looking for a software developer to participate in the development of signal processing and visualization (topographies, time-frequency plots) tools for EEG and MEG signals. This project is in continuity with the ELAN software that has been developed and updated in our laboratory for more than 20 years (https://www.ncbi.nlm.nih.gov/pubmed/21687568) and will be in close collaboration with the MNE-Python development team (https://martinos.org/mne/stable/index.html) You will be responsible for developing a new graphic interface to the already available visualization tools. Regular interactions with team members will be organized to better sense the future users' needs. Moreover, different data sets (EEG, MEG) corresponding to various experimental conditions will be available to test the developed tools. You should have a master's degree computer science. Experience in (C/C++, Python programming) and knowledge of (Qt and signal processing). Knowledge of human electrophysiology (EEG/MEG) would be a plus. You should have strong organizational skills, be able to work independently, and have excellent interpersonal communication skills. You should be able to work in a dynamic, collaborative and international environment. Intended starting data is January 1st, 2019. Initial contract will be for 12 months with possibility of an extension According to education level and work experience salary will range between 1800 and 2500 euros net/month. For more information please contact : Pierre-Emmanuel Aguera : pe.aguera at inserm.fr Aurélie Bidet-Caulet : aurelie.bidet-caulet at inserm.fr Anne Caclin : anne.caclin at inserm.fr Or visit our website(s) ! https://crnl.univ-lyon1.fr/index.php/fr http://dycog.lyon.inserm.fr/ Cheers Hesham ps. maybe I'm not smart enough to figure out how to reply to answers to my questions , but thanks a lot for answering them !! :) -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Fri Nov 9 11:18:26 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Fri, 9 Nov 2018 10:18:26 +0000 Subject: [FieldTrip] ft_rejectvisual reduces trial length In-Reply-To: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl> References: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl> Message-ID: <17306DD6-842B-4153-A0D1-CFA9DA13CD90@donders.ru.nl> Hi Tineke, This is indeed inappropriate behaviour of the function, which has been tracked down to something going wrong in ft_selectdata. I have submitted a PR https://github.com/fieldtrip/fieldtrip/pull/873 on the github-repo which fixes the issue https://github.com/fieldtrip/fieldtrip/issues/871 I am pretty sure that this fix does not come with unwanted side effects, but since ft_selectdata is used almost everywhere I’d appreciate some moral support before merging. Let me know what you think. Jan-Mathijs PS: for those who are around: please do join Tineke and me this coming Sunday Nov 11, when we will commemorate the centenary of the 1918 armistice with a choral concert at the church of St. Anthony of Padua in Nijmegen. It starts at 15.30, see http://www.kamerkoormnemosyne.nl J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands On 7 Nov 2018, at 13:28, Snijders, T.M. (Tineke) > wrote: Hi, I am using trials of different lengths, which seems to give a problem in ft_rejectvisual. It wasn’t a problem before (I think in 2016) when I did the same analysis. When using ft_rejectvisual, in the current fieldtrip version in the output data all trial lengths are reduced to the shortest trial-length, cutting away the remainder of the trial. In the command window it gives a warning of ‘inconsistent sampling info’. Is there a workaround to still get the full trial-lengths for the artifact rejected data? I do want to keep the complete trials at this point. Thanks, Tineke -- Dr T.M. Snijders Research Staff Max Planck Institute for Psycholinguistics, Nijmegen www.ru.nl/people/donders/snijders-t http://www.mpi.nl/departments/language-development MPI | Wundtlaan 1 | Room 246 | tel 024 35 21246 | PO Box 310 | 6500 AH Nijmegen _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Fri Nov 9 15:05:33 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Fri, 9 Nov 2018 14:05:33 +0000 Subject: [FieldTrip] explore cluster statistics In-Reply-To: <2E4AC5E2A691FE4B89DA44B0E70DF70A014154A5E6@AD-EXCHMBX3-3.aau.dk> References: <2E4AC5E2A691FE4B89DA44B0E70DF70A014154A5E6@AD-EXCHMBX3-3.aau.dk> Message-ID: <2263141F-A069-45C9-8FDA-0C7D24149CCC@donders.ru.nl> Afshin, I face two errors: first at ft_math function: Error using ft_math (line 151) the requested parameter is not present in the data As the error indicates, the requested parameter ‘avg’ is apparently not in the data. In other words, the timelock1/2 structures shouldn't have an ‘avg’ field, causing ft_math to complain. The reason for this is that the example script pre-dates some changes to the fieldtrip code base, which caused the ‘avg’ field to disappear if ft_timelockanalysis is called with cfg.keeptrials = ‘yes’. Probably, if you use cfg.parameter = ‘trial’, rather than ‘avg’ in your call to ft_math, it’ll work. Error in Nonparametric_statistical_testingEEGandMEG_data (line 45) difference = ft_math(cfg, timelock1, timelock2); % line 137 of ft_datatype_timelock removes the fields and if I comment that line out, I face another error at ft_timelcokstatistics Error using chol Matrix must be square. Error in montecarlo (line 13) R = chol(Sigma); % Y = R^{-T}X has covariance I For some reason, you end up in a function called ‘montecarlo’, which is incorrect, since you should have ended up in ft_statistics_montecarlo. The cause of all this is probably that the folder that has the ‘montecarlo’ function is higher on the matlab search path than the fieldtrip folder. Jan-Mathijs J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands Error in ft_timelockstatistics (line 185) [stat] = statmethod(cfg, dat, design); Error in Nonparametric_statistical_testingEEGandMEG_data (line 63) stat = ft_timelockstatistics(cfg, timelock1, timelock2); Any comment? Best regards, Associate Prof. Afshin Samani, PhD Sport Sciences Dept. of Health Science and Technology Aalborg University, Aalborg Denmark _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From twater14 at student.aau.dk Sun Nov 11 21:40:19 2018 From: twater14 at student.aau.dk (Toby Steven Waterstone) Date: Sun, 11 Nov 2018 20:40:19 +0000 Subject: [FieldTrip] Comparing spatio-temporal data with FieldTrip toolbox Message-ID: <63EFD5634C86B743A236791097CE83866D363D17@AD-EXCHMBX3-2.aau.dk> Hi, I am analyzing high density surface electromyography (HD-sEMG) data, where I am calculating the similarities between each channel in the sensor grid. This is done in two conditions to see if there are any effect from a specific intervention, from 12 subjects, where pre and post recordings has been performed for both a control and intervention group. For the similarity measure I have calculated normalised mutual information (NMI), where I have two different outcome measures from my calculations. NMI heatmaps (12x5 matrices) representing the aggregated NMI over the HD-sEMG sensor grid, and connectivity maps (60x60 matrices) representing the similarity between each electrode pair. I have attached examples of the data, so you get the idea. I am trying to find a way to compare this data, which has a spatio-temporal pattern. The FieldTrip toolbox has a lot of functions to analyse EEG/MEG data and as my data (HD-sEMG) has many of the same characteristics as EEG data, this toolbox might be useful to analyse my data. But in my case, I have already analysed the EMG signals by calculating NMI, and only need to do the statistical comparison now. FieldTrip is provided with a couple of statistical functions, but I'm unsure about how to use these on my data and if it is possible with this toolbox. The approach I'm looking into is FieldTrip cluster-based permutation tests, but it cannot directly be transferred to my case, because I am comparing the already analysed data. So my data kind of already have the data structure of the output from the analysis of this tutorial (the plots). So my question is: How can I use the statistical functions of the FieldTrip toolbox for comparison of NMI (spatio-temporal data)? If this is possible? I want to compare the heatmaps (12x5 vs 12x5 data structures) to each other and connectivity maps (60x60 vs 60x60 data structures) to each other. Thank you Best regards Toby S. Waterstone -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Heatmap.png Type: image/png Size: 162770 bytes Desc: Heatmap.png URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Connectivity_map.png Type: image/png Size: 306140 bytes Desc: Connectivity_map.png URL: From rdm146 at newark.rutgers.edu Mon Nov 12 17:36:29 2018 From: rdm146 at newark.rutgers.edu (Ravi Mill) Date: Mon, 12 Nov 2018 16:36:29 +0000 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: References: <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> , Message-ID: Many thanks Marios and Carsten - I will try out the scripts you sent me and let you know if I have any issues. Best wishes Ravi ________________________________ From: Marios Antonakakis Sent: Tuesday, November 6, 2018 8:21:56 AM To: Carsten Wolters Cc: fieldtrip at science.ru.nl; Ravi Mill Subject: Re: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio Hi Ravi, assuming that you work under Linux and you have already the DTI data registered to MRI, you need then to run the below script. %%1, calculate conductivity tensor for every voxel % mri_segmented_final, V1, ... L1 variables have ft MRI structure %skin,skull compacta, skull spongiosa, CSF, GM, WM conductivity = [0.4300, 0.0024, 0.0086, 1.7900, 0.3300, 0.1400]; cfg =[]; cfg.compartments = mri_segmented_final.anatomylabel; % anatomical labels, skin: 0, skull spongiosa 2 and show on... cfg.conductivity = conductivity; [condcell, s, fail] = sb_calcTensorCond_tuch(cfg,mri_segmented_final,V1,V2,V3,L1,L2,L3); %% 2. assign the conductivity tensor with the hex mesh [mesh.nodes,mesh.elements,mesh.labels]= read_vista_mesh('foo_mesh.v); mask = mri_segmented_final; mask.anatomy((mask.anatomy~=5) & (mask.anatomy~=6))=0; mask.anatomy((mask.anatomy==5) | (mask.anatomy==6))=1; tensors = sb_assiTensorCond(mask,mesh.nodes,mesh.elements,mesh.labels,condcell); write_vista_mesh('foo_mesh_aniso.v',mesh.nodes,mesh.elements,mesh.labels,tensors'); Do not hesitate to ask further questions. *Note that write_vista_mesh and read_vista_mesh are private ft functions. Best regards, Marios [https://mailfoogae.appspot.com/t?sender=aYW50b25ha2FraXNtYXJAZ21haWwuY29t&type=zerocontent&guid=420ef8db-499e-48d0-b63b-37a4d6551822]ᐧ Στις Τρί, 6 Νοε 2018 στις 12:10 μ.μ., ο/η Carsten Wolters > έγραψε: Hi Ravi, Marios (in CC) promised to send you the short Matlab-script that we use to transform the diffusion tensors to conductivity tensors using Tuch's effective medium approach. For this approach, please check e.g. the subsection "Calibrated Finite Element Head Model and Forward Solution" in https://link.springer.com/article/10.1007/s10548-017-0568-9 BR Carsten Am 05.11.18 um 18:26 schrieb Ravi Mill: Hi Carsten and Johannes Many thanks for responding, and for developing these great tools! I'm in the process of acquiring a large diffusion MR dataset from which I can hopefully create an 'averaged' atlas. From your responses I think I have a sense of how to integrate the conductivity tensors derived from this atlas with the Fieldtrip FEM pipeline. But I was wondering if you had any advice on how to compute these conductivity tensors in the first place? From the paper that Carsten sent, It seems like the FDT program within FSL is what I need to compute diffusion tensors from the raw diffusion images (steps 1-6 from the FDT user guide https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Processing_pipeline)? Seemingly, these diffusion tensors need to then be converted to conductivity tensors - any advice on how to do this (or if you could point me to some example code) would be greatly appreciated. Thanks Ravi FDT/UserGuide - FslWiki - University of Oxford fsl.fmrib.ox.ac.uk Eddy Current Correction. Eddy currents in the gradient coils induce (approximate) stretches and shears in the diffusion weighted images. These distortions are different for different gradient directions. Thanks again Ravi ________________________________ From: fieldtrip on behalf of Johannes Vorwerk Sent: Monday, October 29, 2018 10:14:16 AM To: FieldTrip discussion list Subject: Re: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio Dear Ravi, as Carsten already said, calculating FEM with anisotropic conductivities is not directly supported by the FieldTrip-SimBio implementation. However, if you are willing to invest a bit of time it is possible to work around this. The „only“ thing that needs to be changed is the calculation of the FEM stiffness matrix, which is performed by the routine „calc_stiffness_matrix_val“ in the function sb_calc_stiff (usually called from ft_prepare_headmodel). The problem is that FieldTrip does not support anisotropic conductivities, so that you would have to call calc_stiffness_matrix_val directly. You can see the correct call in sb_calc_stiff. For anisotropic conductivities you have to replace the input „cond“ by a #elements x 6 matrix containing your anisotropic conductivities in the format "xx yy zz xy yz zx“. If you now follow the normal FieldTrip-SimBio workflow using the resulting stiffness matrix, you will get results for anisotropic conductivities. Best, Johannes Am 29.10.2018 um 12:31 schrieb Carsten Wolters >: Dear Ravi, 1) You can use the pure SimBio-code from https://www.mrt.uni-jena.de/simbio/index.php/Main_Page to treat WM anisotropy. While it would in principle also be possible to use anisotropic conductivities with FieldTrip-SimBio, this is currently not implemented using ft_prepare_headmodel. Johannes (in CC), who implemented Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: "Depending on your matlab skills and your available time, I could help you to give it a try though. It should be possible with using some direct function calls instead of the high-level fieldtrip-functions." 2) We recommend http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf on individual data. I could imagine that an atlas does a reasonable job w.r.t. the main bigger fiber tracts such as corpus callosum or pyramidal tracts, but that the finer details in the cortices are individual. We always measure T1, T2 and DTI from each subject and I personally do not have experience with such a group-level anisotropy compared to the individual one. Might be interesting to hear from others what they think!? BR Carsten Am 25.10.18 um 23:05 schrieb Ravi Mill: Dear Fieldtrippers I have applied the FEM simbio head modeling pipeline implemented in Fieldtrip to my EEG data. My understanding is that this pipeline assumes isotropic conductivities for 5 head compartments (as specified by cfg.conductivity in ft_prepare_headmodel). After reading some papers (e.g. Vorwerk et al 2014 https://doi.org/10.1016/j.neuroimage.2014.06.040), it seems like incorporating white matter conductivity anisotropy has a relatively small albeit significant effect on the source solution. I am interested in comparing FEM results when treating white matter as anisotropic. My questions are as follows: 1. Is there a way to implement the FEM simbio head model whilst treating WM as anisotropic within Fieldtrip? If so, how would one do this (or are there any resources available that demonstrate this)? 2. From previous papers and some simbio documentation (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples) it seems like diffusion MRI data is required to calculate the WM conductivity for each individual subject. I only have T1 and T2 scans for my subjects. So would it be possible to use WM anisotropic information obtained from some kind of diffusion MRI group average/atlas instead (accepting some loss in subject-level precision)? If so, does such a group average/atlas exist? Any help would be greatly appreciated! Thanks Ravi _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -- Prof. Dr.rer.nat. Carsten H. Wolters University of Münster Institute for Biomagnetism and Biosignalanalysis Malmedyweg 15 48149 Münster, Germany Phone: +49 (0)251 83 56904 +49 (0)251 83 56865 (secr.) Fax: +49 (0)251 83 56874 Email: carsten.wolters at uni-muenster.de Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -- Prof. Dr.rer.nat. Carsten H. Wolters University of Münster Institute for Biomagnetism and Biosignalanalysis Malmedyweg 15 48149 Münster, Germany Phone: +49 (0)251 83 56904 +49 (0)251 83 56865 (secr.) Fax: +49 (0)251 83 56874 Email: carsten.wolters at uni-muenster.de Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Wed Nov 14 12:28:37 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Wed, 14 Nov 2018 11:28:37 +0000 Subject: [FieldTrip] Comparing spatio-temporal data with FieldTrip toolbox In-Reply-To: <63EFD5634C86B743A236791097CE83866D363D17@AD-EXCHMBX3-2.aau.dk> References: <63EFD5634C86B743A236791097CE83866D363D17@AD-EXCHMBX3-2.aau.dk> Message-ID: <230E33B4-CF3E-448C-A6C9-AB2D94688863@donders.ru.nl> Dear Toby, I think that this is not altogether too difficult. What you would need to do is to put your numeric data into a structure that FieldTrip can work with. Specifically, if you manage to create a so-called ’timelock’ or ‘freq’ representation of your data, you can use ft_timelockstatistics or ft_freqstatistics for the statistical inference. (as a side note, I think it’s up to you to think whether it makes sense to use the spatial clustering heuristic for family-wise error control when comparing the connectivity matrices; yet, you can still do a permutation test to test the null hypothesis of exchangeability between groups). Long story short for the 12x5 NMI data, I’d create 2 data structures, let’s call them freq1 (intervention group) and freq2 (controls), with the following fields freq1.label = {‘thenameofthisisnotrelevant’}; freq1.freq = 1:12 freq1.time = 1:5 freq1.dimord = ‘rpt_chan_freq_time’; freq1.powspctrm = zeros(number-of-subjects, 1, 12, 5); for i = 1:nsubj freq1.powspctrm(i,1,:,:) = nmi; % this should yield a 1 x 12 x 5 matrix end and the same thing for freq2. Then you can use ft_freqstatistics for statistical inference, with optional clustering for multiple comparison correction. In this case, the clustering will take place across the ‘freq’, and ‘time’ dimensions, which in your case boils down to spatial clustering across adjacent electrodes in the x and y directions, respectively. For the connectivity matrices, I’d convert the single subject matrices into a vector (using the lower triangular part only), but I’d say: first things first. Best wishes, Jan-Mathijs On 11 Nov 2018, at 21:40, Toby Steven Waterstone > wrote: Hi, I am analyzing high density surface electromyography (HD-sEMG) data, where I am calculating the similarities between each channel in the sensor grid. This is done in two conditions to see if there are any effect from a specific intervention, from 12 subjects, where pre and post recordings has been performed for both a control and intervention group. For the similarity measure I have calculated normalised mutual information (NMI), where I have two different outcome measures from my calculations. NMI heatmaps (12x5 matrices) representing the aggregated NMI over the HD-sEMG sensor grid, and connectivity maps (60x60 matrices) representing the similarity between each electrode pair. I have attached examples of the data, so you get the idea. I am trying to find a way to compare this data, which has a spatio-temporal pattern. The FieldTrip toolbox has a lot of functions to analyse EEG/MEG data and as my data (HD-sEMG) has many of the same characteristics as EEG data, this toolbox might be useful to analyse my data. But in my case, I have already analysed the EMG signals by calculating NMI, and only need to do the statistical comparison now. FieldTrip is provided with a couple of statistical functions, but I'm unsure about how to use these on my data and if it is possible with this toolbox. The approach I'm looking into is FieldTrip cluster-based permutation tests, but it cannot directly be transferred to my case, because I am comparing the already analysed data. So my data kind of already have the data structure of the output from the analysis of this tutorial (the plots). So my question is: How can I use the statistical functions of the FieldTrip toolbox for comparison of NMI (spatio-temporal data)? If this is possible? I want to compare the heatmaps (12x5 vs 12x5 data structures) to each other and connectivity maps (60x60 vs 60x60 data structures) to each other. Thank you Best regards Toby S. Waterstone _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From stanabe at wisc.edu Wed Nov 14 21:26:33 2018 From: stanabe at wisc.edu (SEAN TANABE) Date: Wed, 14 Nov 2018 20:26:33 +0000 Subject: [FieldTrip] Concerning powcorr_orth method in ft_connectivityanalysis Message-ID: Dear all, I am trying to use the powcorr_orth method in ft_connectivityanalysis and noticed the results always produce a random connectivity pattern. A similar issue has been raised a year ago here; https://mailman.science.ru.nl/pipermail/fieldtrip/2017-July/011695.html I found after editing a line from ft_connectivityanalysis the results look more reasonable, and wanted to confirm if this editing is correct with the fieldtrip developers. In fieldtrip-20170820 ft_connectivityanalysis I noticed a line which reshapes the fourierspctrm before inputting to ft_connectivity_powcorr_orth (line 852 in fieldtrip-20181113). dat = reshape(data.fourierspctrm(:,:,i)', nrpttap, nchan).'; datout{i} = ft_connectivity_powcorr_ortho(dat, optarg{:}); This seems to not fit the description of ft_connectivity_powcorr_orth, which requires "dat" to have nchan rows. I changed this to dat = data.fourierspctrm(:,:,i)'; datout{i} = ft_connectivity_powcorr_ortho(dat, optarg{:}); The above gave natural looking blobs of connectivity in the topology. Thank you. Sean -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Thu Nov 15 09:25:39 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 15 Nov 2018 08:25:39 +0000 Subject: [FieldTrip] Concerning powcorr_orth method in ft_connectivityanalysis In-Reply-To: References: Message-ID: <3E50C67C-9EF0-4C04-8988-AAA879C87134@donders.ru.nl> Dear Sean, Thanks for catching this. The original line 852 does not make sense at all, indeed. May I suggest to replace this with data.fourierspctrm(:,:,i).’ (note the ‘.’)? Can you please submit this as a pull request on github? http://www.fieldtriptoolbox.org/development/git Many thanks, Jan-Mathijs On 14 Nov 2018, at 21:26, SEAN TANABE > wrote: Dear all, I am trying to use the powcorr_orth method in ft_connectivityanalysis and noticed the results always produce a random connectivity pattern. A similar issue has been raised a year ago here; https://mailman.science.ru.nl/pipermail/fieldtrip/2017-July/011695.html I found after editing a line from ft_connectivityanalysis the results look more reasonable, and wanted to confirm if this editing is correct with the fieldtrip developers. In fieldtrip-20170820 ft_connectivityanalysis I noticed a line which reshapes the fourierspctrm before inputting to ft_connectivity_powcorr_orth (line 852 in fieldtrip-20181113). dat = reshape(data.fourierspctrm(:,:,i)', nrpttap, nchan).'; datout{i} = ft_connectivity_powcorr_ortho(dat, optarg{:}); This seems to not fit the description of ft_connectivity_powcorr_orth, which requires "dat" to have nchan rows. I changed this to dat = data.fourierspctrm(:,:,i)'; datout{i} = ft_connectivity_powcorr_ortho(dat, optarg{:}); The above gave natural looking blobs of connectivity in the topology. Thank you. Sean _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From tineke.snijders at donders.ru.nl Thu Nov 15 12:27:26 2018 From: tineke.snijders at donders.ru.nl (Snijders, T.M. (Tineke)) Date: Thu, 15 Nov 2018 11:27:26 +0000 Subject: [FieldTrip] ft_rejectvisual reduces trial length In-Reply-To: <17306DD6-842B-4153-A0D1-CFA9DA13CD90@donders.ru.nl> References: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl>, <17306DD6-842B-4153-A0D1-CFA9DA13CD90@donders.ru.nl> Message-ID: <1542281246035.19816@donders.ru.nl> Thanks for fixing this Jan-Mathijs, it works beautifully now! Tineke ________________________________ From: fieldtrip on behalf of Schoffelen, J.M. (Jan Mathijs) Sent: Friday, November 9, 2018 11:18 AM To: FieldTrip discussion list Subject: Re: [FieldTrip] ft_rejectvisual reduces trial length Hi Tineke, This is indeed inappropriate behaviour of the function, which has been tracked down to something going wrong in ft_selectdata. I have submitted a PR https://github.com/fieldtrip/fieldtrip/pull/873 on the github-repo which fixes the issue https://github.com/fieldtrip/fieldtrip/issues/871 I am pretty sure that this fix does not come with unwanted side effects, but since ft_selectdata is used almost everywhere I'd appreciate some moral support before merging. Let me know what you think. Jan-Mathijs PS: for those who are around: please do join Tineke and me this coming Sunday Nov 11, when we will commemorate the centenary of the 1918 armistice with a choral concert at the church of St. Anthony of Padua in Nijmegen. It starts at 15.30, see http://www.kamerkoormnemosyne.nl J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands On 7 Nov 2018, at 13:28, Snijders, T.M. (Tineke) > wrote: Hi, I am using trials of different lengths, which seems to give a problem in ft_rejectvisual. It wasn't a problem before (I think in 2016) when I did the same analysis. When using ft_rejectvisual, in the current fieldtrip version in the output data all trial lengths are reduced to the shortest trial-length, cutting away the remainder of the trial. In the command window it gives a warning of 'inconsistent sampling info'. Is there a workaround to still get the full trial-lengths for the artifact rejected data? I do want to keep the complete trials at this point. Thanks, Tineke -- Dr T.M. Snijders Research Staff Max Planck Institute for Psycholinguistics, Nijmegen www.ru.nl/people/donders/snijders-t http://www.mpi.nl/departments/language-development MPI | Wundtlaan 1 | Room 246 | tel 024 35 21246 | PO Box 310 | 6500 AH Nijmegen _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From kai.hwang at gmail.com Fri Nov 16 17:47:38 2018 From: kai.hwang at gmail.com (Kai Hwang) Date: Fri, 16 Nov 2018 10:47:38 -0600 Subject: [FieldTrip] Postdoc Position | Cognitive Neuroscience | University of Iowa Message-ID: The Hwang lab for Neurocognitive Dynamics in the Psychological and Brain Sciences Department at the University of Iowa has a fully funded postdoc position available. Our lab focuses on brain network mechanisms, cognitive control, and their developmental processes with a strong emphasis on the human thalamocortical system and neural oscillations. Our research utilizes multimodal neuroimaging (EEG and fMRI), TMS, and lesion studies in combination with network neuroscience approaches. For more info, please see: https://kaihwang.github.io/ The lab is affiliated with the DeLTA Center and the Iowa Neuroscience Institute, which offers a collaborative research environment with access to research dedicated 3T and 7T MRI systems, TMS, EEG, neurosurgery patients, and a large lesion patient registry. Postdoc Candidate Qualifications - Ph.D. in Psychology, Neuroscience, or other related disciplines. - Experience with neuroimaging (fMRI, EEG/MEG). - Strong Programming skills (Matlab or Python) The postdoc position is opened immediately until filled. To apply, please send a cover letter and CV to: kai-hwang at uiowa.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From Eliana.Klier at uth.tmc.edu Fri Nov 16 19:33:21 2018 From: Eliana.Klier at uth.tmc.edu (Klier, Eliana M) Date: Fri, 16 Nov 2018 18:33:21 +0000 Subject: [FieldTrip] Postdoc Job Posting Message-ID: <0e5edb17c01c46b8a4516640461d11b4@uth.tmc.edu> Hello Fieldtrip, Is it possible for the Tandon Lab to post the attached postdoctoral ad to your mailing list? Sincerely, Eliana Eliana M Klier, Ph.D. Senior Program Manager - Research McGovern Medical School part of UTHealth | The University of Texas Health Science Center at Houston Department of Neurosurgery 6431 Fannin St | Rm G.550G | Houston, TX 77030 Phone: 713-500-5442 Email: Eliana.Klier at uth.tmc.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Tandon Lab Post Doc.docx Type: application/vnd.openxmlformats-officedocument.wordprocessingml.document Size: 159409 bytes Desc: Tandon Lab Post Doc.docx URL: From Oscar.Woolnough at uth.tmc.edu Fri Nov 16 17:39:54 2018 From: Oscar.Woolnough at uth.tmc.edu (Woolnough, Oscar) Date: Fri, 16 Nov 2018 16:39:54 +0000 Subject: [FieldTrip] Multiple Postdocs available in the Neurobiology of Language Message-ID: <064DCE91-3B14-4E59-91E5-F1AD8A0EE289@uth.tmc.edu> POSTDOCTORAL RESEARCH POSITIONS Multiple Postdoctoral research positions are available in the Tandon Lab at The University of Texas in Houston as part of the newly formed Texas Epilepsy Neurotechnologies and Neuroinformatics (TENN) Institute. Positions are funded either via multi year Institute funding or by NIH funds (an R01 and a U01). The lab uses multimodal approaches – fMRI, lesional analysis following epilepsy surgery, intracranial recordings and direct stimulation to create validate network level representations of language. Lab Collaborators include Greg Hickok (UCI), Stanislas Dehaene (NeuroSpin), Nathan Crone (JHU), Simon Fisher Baum (Rice) and Xaq Pitkow (Rice-Baylor); the post-doc will benefit from a close interaction with these experts in the fields of reading, semantics, speech production and computational neuroscience. The selected individual must have a Ph.D. in one or more of the following: neuroscience, psychology, cognitive science, mathematics, electrical engineering or computer science. Previous experience in neural time series data analysis, functional imaging studies of language, or studies of speech production are desirable – but not crucial. They must possess the ability to independently code in any or all of the following: MATLAB, R or python. They are expected to be highly motivated, team players with a passion to study cognitive processes using any or all of the various modalities available in the lab - imaging, direct recordings and closed-loop cortical stimulation in humans. Given the multiple unpredictable variables and privacy issues around data collection in human patients, the individual must possess high ethical and professional standards and be adaptable. A strong publication record and excellent academic credentials are highly desirable. CONTACT: Nitin.Tandon at uth.tmc.edu Eliana.Klier at uth.tmc.edu More information @ www.tandonlab.org -------------- next part -------------- An HTML attachment was scrubbed... URL: From p.gaur at ulster.ac.uk Mon Nov 19 19:19:55 2018 From: p.gaur at ulster.ac.uk (Gaur, Pramod) Date: Mon, 19 Nov 2018 18:19:55 +0000 Subject: [FieldTrip] Combine multiple blocks Message-ID: Dear Team, I have a quick concern, I have a cognitive task and recorded three blocks. Is there any way in which I can combine all the three blocks before the preprocessing. I have alternate to preprocess the blocks separately and then combining them. In this case, I lose the data_MEG_filt .grad.chanpos location. cfg = []; cfg.dataset = filename; % % cfg = ft_definetrial(cfg); cfg.trialdef.eventtype = 'STI101'; %cfg.trialdef.eventtype = 'gui'; cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own cfg.trialdef.prestim = prestim; cfg.trialdef.poststim = poststim; % cfg.length = 1; cfg = ft_definetrial(cfg); % read in the data from the magnetometer % cfg.channel = {'MEGMAG','STI101'}; cfg.channel = {'MEG'};%,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};% % cfg.lpfilter = 'yes'; % cfg.lpfreq = 40; % cfg.hpfilter = 'yes'; % cfg.hpfreq = 1; cfg.continuous = 'yes'; cfg.detrend = 'no'; cfg.demean = 'yes'; cfg.dftfilter = 'yes'; % cfg.dftfreq =[50 100 150];% power line noise cfg.bpfreq = [1 48]; cfg.metric = 'zvalue'; cfg.layout = 'neuromag306all.lay'; cfg.baselinewindow = [-0.5 0]; data_MEG_filt = ft_preprocessing(cfg); if isempty(data_MEG_filt) data_MEG_filt=data; grad = data.grad; else cfg = []; data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data); end Any advice will be highly appreciated. [Ulster University] Dr Pramod Gaur Research Assistant in Neuro-Imaging Technology School of Computing, Engineering and Intelligent Systems Magee Campus E: p.gaur at ulster.ac.uk W: www.ulster.ac.uk Social: Twitter: @SceisUni Facebook: @UlsterUniComputingEngineeringIntelligentSystems [cid:image004.jpg at 01D3EDDB.BF0D58A0] This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image001.jpg Type: image/jpeg Size: 37467 bytes Desc: image001.jpg URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image003.jpg Type: image/jpeg Size: 3389 bytes Desc: image003.jpg URL: From jan.schoffelen at donders.ru.nl Mon Nov 19 20:50:58 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Mon, 19 Nov 2018 19:50:58 +0000 Subject: [FieldTrip] Combine multiple blocks In-Reply-To: References: Message-ID: Pramod, I don’t understand how ‘combining all the three blocks before the preprocessing’ would help you to preserve the channel positions. Apparently, these are different for each of the blocks in your case. It’s also not clear to me, why this is causes problem in your specific case. Anyway, ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs. If you still want to obtain some kind of ‘average’ representation of the channel position, you can use ft_average_sens. If the positions are not altogether too different, it might be OK to average the positions across blocks, although technically it’s of course incorrect to do so. Yet, if for each of the blocks you have applied block-specific spatial transformations (e.g. maxfilter) then it’s a different story altogether, and things will become hairy very rapidly. Best wishes, Jan-Mathijs On 19 Nov 2018, at 19:19, Gaur, Pramod > wrote: Dear Team, I have a quick concern, I have a cognitive task and recorded three blocks. Is there any way in which I can combine all the three blocks before the preprocessing. I have alternate to preprocess the blocks separately and then combining them. In this case, I lose the data_MEG_filt .grad.chanpos location. cfg = []; cfg.dataset = filename; % % cfg = ft_definetrial(cfg); cfg.trialdef.eventtype = 'STI101'; %cfg.trialdef.eventtype = 'gui'; cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own cfg.trialdef.prestim = prestim; cfg.trialdef.poststim = poststim; % cfg.length = 1; cfg = ft_definetrial(cfg); % read in the data from the magnetometer % cfg.channel = {'MEGMAG','STI101'}; cfg.channel = {'MEG'};%,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};% % cfg.lpfilter = 'yes'; % cfg.lpfreq = 40; % cfg.hpfilter = 'yes'; % cfg.hpfreq = 1; cfg.continuous = 'yes'; cfg.detrend = 'no'; cfg.demean = 'yes'; cfg.dftfilter = 'yes'; % cfg.dftfreq =[50 100 150];% power line noise cfg.bpfreq = [1 48]; cfg.metric = 'zvalue'; cfg.layout = 'neuromag306all.lay'; cfg.baselinewindow = [-0.5 0]; data_MEG_filt = ft_preprocessing(cfg); if isempty(data_MEG_filt) data_MEG_filt=data; grad = data.grad; else cfg = []; data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data); end Any advice will be highly appreciated. Dr Pramod Gaur Research Assistant in Neuro-Imaging Technology School of Computing, Engineering and Intelligent Systems Magee Campus E: p.gaur at ulster.ac.uk W: www.ulster.ac.uk Social: Twitter: @SceisUni Facebook: @UlsterUniComputingEngineeringIntelligentSystems This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From p.gaur at ulster.ac.uk Mon Nov 19 23:04:29 2018 From: p.gaur at ulster.ac.uk (Gaur, Pramod) Date: Mon, 19 Nov 2018 22:04:29 +0000 Subject: [FieldTrip] Combine multiple blocks In-Reply-To: References: Message-ID: Hi Jan-Mathijs, I want to do source analysis. I have 3 blocks with 40 trials each for one subject and want to do the source analysis. Yes, you pointed it correctly “ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs.” Please advise me how to do the source analysis on them. Best regards, Pramod From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Schoffelen, J.M. (Jan Mathijs) Sent: 19 November 2018 19:51 To: FieldTrip discussion list Subject: Re: [FieldTrip] Combine multiple blocks Pramod, I don’t understand how ‘combining all the three blocks before the preprocessing’ would help you to preserve the channel positions. Apparently, these are different for each of the blocks in your case. It’s also not clear to me, why this is causes problem in your specific case. Anyway, ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs. If you still want to obtain some kind of ‘average’ representation of the channel position, you can use ft_average_sens. If the positions are not altogether too different, it might be OK to average the positions across blocks, although technically it’s of course incorrect to do so. Yet, if for each of the blocks you have applied block-specific spatial transformations (e.g. maxfilter) then it’s a different story altogether, and things will become hairy very rapidly. Best wishes, Jan-Mathijs On 19 Nov 2018, at 19:19, Gaur, Pramod > wrote: Dear Team, I have a quick concern, I have a cognitive task and recorded three blocks. Is there any way in which I can combine all the three blocks before the preprocessing. I have alternate to preprocess the blocks separately and then combining them. In this case, I lose the data_MEG_filt .grad.chanpos location. cfg = []; cfg.dataset = filename; % % cfg = ft_definetrial(cfg); cfg.trialdef.eventtype = 'STI101'; %cfg.trialdef.eventtype = 'gui'; cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own cfg.trialdef.prestim = prestim; cfg.trialdef.poststim = poststim; % cfg.length = 1; cfg = ft_definetrial(cfg); % read in the data from the magnetometer % cfg.channel = {'MEGMAG','STI101'}; cfg.channel = {'MEG'};%,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};% % cfg.lpfilter = 'yes'; % cfg.lpfreq = 40; % cfg.hpfilter = 'yes'; % cfg.hpfreq = 1; cfg.continuous = 'yes'; cfg.detrend = 'no'; cfg.demean = 'yes'; cfg.dftfilter = 'yes'; % cfg.dftfreq =[50 100 150];% power line noise cfg.bpfreq = [1 48]; cfg.metric = 'zvalue'; cfg.layout = 'neuromag306all.lay'; cfg.baselinewindow = [-0.5 0]; data_MEG_filt = ft_preprocessing(cfg); if isempty(data_MEG_filt) data_MEG_filt=data; grad = data.grad; else cfg = []; data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data); end Any advice will be highly appreciated. Dr Pramod Gaur Research Assistant in Neuro-Imaging Technology School of Computing, Engineering and Intelligent Systems Magee Campus E: p.gaur at ulster.ac.uk W: www.ulster.ac.uk Social: Twitter: @SceisUni Facebook: @UlsterUniComputingEngineeringIntelligentSystems This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Tue Nov 20 09:07:58 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Tue, 20 Nov 2018 08:07:58 +0000 Subject: [FieldTrip] Combine multiple blocks In-Reply-To: References: Message-ID: <1F1F8765-C52F-4B0C-A7E0-9AFC30E1ED69@donders.ru.nl> Pramod, Your queries are lacking detail: i.e. there is no detail as to what you actually want to do (e.g. type of source reconstruction etc.), nor any detail about what you have tried yourself so far. Therefore, it is hard to give constructive feedback/directions. I suggest to first try and get a running pipeline to do source reconstruction using data from a single block. Then it’s relatively straightforward to extend this to a multiple block setting, where the exact sensible directions to take depend on the source reconstruction algorithm and the quality of the data per block. One possibility would be, as I already mentioned, to recover a more or less useable grad structure, using ft_average_sens. Alternatively, you could do the source reconstruction per block, and combine afterwards. Good luck, Jan-Mathijs J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands On 19 Nov 2018, at 23:04, Gaur, Pramod > wrote: Hi Jan-Mathijs, I want to do source analysis. I have 3 blocks with 40 trials each for one subject and want to do the source analysis. Yes, you pointed it correctly “ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs.” Please advise me how to do the source analysis on them. Best regards, Pramod From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Schoffelen, J.M. (Jan Mathijs) Sent: 19 November 2018 19:51 To: FieldTrip discussion list > Subject: Re: [FieldTrip] Combine multiple blocks Pramod, I don’t understand how ‘combining all the three blocks before the preprocessing’ would help you to preserve the channel positions. Apparently, these are different for each of the blocks in your case. It’s also not clear to me, why this is causes problem in your specific case. Anyway, ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs. If you still want to obtain some kind of ‘average’ representation of the channel position, you can use ft_average_sens. If the positions are not altogether too different, it might be OK to average the positions across blocks, although technically it’s of course incorrect to do so. Yet, if for each of the blocks you have applied block-specific spatial transformations (e.g. maxfilter) then it’s a different story altogether, and things will become hairy very rapidly. Best wishes, Jan-Mathijs On 19 Nov 2018, at 19:19, Gaur, Pramod > wrote: Dear Team, I have a quick concern, I have a cognitive task and recorded three blocks. Is there any way in which I can combine all the three blocks before the preprocessing. I have alternate to preprocess the blocks separately and then combining them. In this case, I lose the data_MEG_filt .grad.chanpos location. cfg = []; cfg.dataset = filename; % % cfg = ft_definetrial(cfg); cfg.trialdef.eventtype = 'STI101'; %cfg.trialdef.eventtype = 'gui'; cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own cfg.trialdef.prestim = prestim; cfg.trialdef.poststim = poststim; % cfg.length = 1; cfg = ft_definetrial(cfg); % read in the data from the magnetometer % cfg.channel = {'MEGMAG','STI101'}; cfg.channel = {'MEG'};%,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};% % cfg.lpfilter = 'yes'; % cfg.lpfreq = 40; % cfg.hpfilter = 'yes'; % cfg.hpfreq = 1; cfg.continuous = 'yes'; cfg.detrend = 'no'; cfg.demean = 'yes'; cfg.dftfilter = 'yes'; % cfg.dftfreq =[50 100 150];% power line noise cfg.bpfreq = [1 48]; cfg.metric = 'zvalue'; cfg.layout = 'neuromag306all.lay'; cfg.baselinewindow = [-0.5 0]; data_MEG_filt = ft_preprocessing(cfg); if isempty(data_MEG_filt) data_MEG_filt=data; grad = data.grad; else cfg = []; data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data); end Any advice will be highly appreciated. Dr Pramod Gaur Research Assistant in Neuro-Imaging Technology School of Computing, Engineering and Intelligent Systems Magee Campus E: p.gaur at ulster.ac.uk W: www.ulster.ac.uk Social: Twitter: @SceisUni Facebook: @UlsterUniComputingEngineeringIntelligentSystems This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From stephen.whitmarsh at gmail.com Tue Nov 20 09:43:48 2018 From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh) Date: Tue, 20 Nov 2018 09:43:48 +0100 Subject: [FieldTrip] Combine multiple blocks In-Reply-To: <1F1F8765-C52F-4B0C-A7E0-9AFC30E1ED69@donders.ru.nl> References: <1F1F8765-C52F-4B0C-A7E0-9AFC30E1ED69@donders.ru.nl> Message-ID: Pramod, *If *you can use MaxFilter (on NeuroMag system), you can also consider using MaxFilter to artificially align sensors locations to sensor position of e.g. the first recording. See their manual for that. However, I agree with Jan-Mathijs that that's technically questionable and probably unnecessary, and that combining blocks e.g. on source level, if data quality permits, would be a more proper approach. Cheers, Stephen On Tue, 20 Nov 2018 at 09:36, Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > Pramod, > > Your queries are lacking detail: i.e. there is no detail as to what you > actually want to do (e.g. type of source reconstruction etc.), nor any > detail about what you have tried yourself so far. Therefore, it is hard to > give constructive feedback/directions. > > I suggest to first try and get a running pipeline to do source > reconstruction using data from a single block. Then it’s relatively > straightforward to extend this to a multiple block setting, where the exact > sensible directions to take depend on the source reconstruction algorithm > and the quality of the data per block. One possibility would be, as I > already mentioned, to recover a more or less useable grad structure, using > ft_average_sens. Alternatively, you could do the source reconstruction per > block, and combine afterwards. > > Good luck, > Jan-Mathijs > > > > J.M.Schoffelen, MD PhD > Senior Researcher, VIDI-fellow - PI, language in interaction > Telephone: +31-24-3614793 > Physical location: room 00.028 > Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands > > On 19 Nov 2018, at 23:04, Gaur, Pramod wrote: > > Hi Jan-Mathijs, > > I want to do source analysis. I have 3 blocks with 40 trials each for one > subject and want to do the source analysis. Yes, you pointed it correctly “*ft_appenddata > throws the grad-field away, since it recognizes that the channel (and coil) > positions are different in each of the runs.*” Please advise me how to do > the source analysis on them. > > Best regards, > Pramod > > *From:* fieldtrip [mailto:fieldtrip-bounces at science.ru.nl > ] *On Behalf Of *Schoffelen, J.M. (Jan > Mathijs) > *Sent:* 19 November 2018 19:51 > *To:* FieldTrip discussion list > *Subject:* Re: [FieldTrip] Combine multiple blocks > > Pramod, > > I don’t understand how ‘combining all the three blocks before the > preprocessing’ would help you to preserve the channel positions. > Apparently, these are different for each of the blocks in your case. It’s > also not clear to me, why this is causes problem in your specific case. > > Anyway, ft_appenddata throws the grad-field away, since it recognizes that > the channel (and coil) positions are different in each of the runs. > If you still want to obtain some kind of ‘average’ representation of the > channel position, you can use ft_average_sens. If the positions are not > altogether too different, it might be OK to average the positions across > blocks, although technically it’s of course incorrect to do so. Yet, if for > each of the blocks you have applied block-specific spatial transformations > (e.g. maxfilter) then it’s a different story altogether, and things will > become hairy very rapidly. > > Best wishes, > > Jan-Mathijs > > > > On 19 Nov 2018, at 19:19, Gaur, Pramod wrote: > > Dear Team, > > I have a quick concern, I have a cognitive task and recorded three blocks. > Is there any way in which I can combine all the three blocks before the > preprocessing. I have alternate to preprocess the blocks separately and > then combining them. In this case, I lose the data_MEG_filt > .grad.chanpos location. > > cfg = []; > cfg.dataset = filename; > % % cfg = ft_definetrial(cfg); > cfg.trialdef.eventtype = 'STI101'; > %cfg.trialdef.eventtype = 'gui'; > cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own > cfg.trialdef.prestim = prestim; > cfg.trialdef.poststim = poststim; > % cfg.length = 1; > cfg = ft_definetrial(cfg); > > % read in the data from the magnetometer > % cfg.channel = {'MEGMAG','STI101'}; > cfg.channel = {'MEG'}; > %,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};% > % cfg.lpfilter = 'yes'; > % cfg.lpfreq = 40; > % cfg.hpfilter = 'yes'; > % cfg.hpfreq = 1; > cfg.continuous = 'yes'; > cfg.detrend = 'no'; > cfg.demean = 'yes'; > cfg.dftfilter = 'yes'; > % cfg.dftfreq =[50 100 150];% power line noise > cfg.bpfreq = [1 48]; > cfg.metric = 'zvalue'; > cfg.layout = 'neuromag306all.lay'; > cfg.baselinewindow = [-0.5 0]; > data_MEG_filt = ft_preprocessing(cfg); > > if isempty(data_MEG_filt) > data_MEG_filt=data; > grad = data.grad; > else > cfg = []; > data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data); > end > > > Any advice will be highly appreciated. > > > > > *Dr Pramod Gaur* > Research Assistant in Neuro-Imaging Technology > > School of Computing, Engineering and Intelligent Systems > > Magee Campus > > *E:* p.gaur at ulster.ac.uk *W:* www.ulster.ac.uk > *Social:* Twitter: @SceisUni > Facebook: @UlsterUniComputingEngineeringIntelligentSystems > > > > > > > > > > > > > This email and any attachments are confidential and intended solely for > the use of the addressee and may contain information which is covered by > legal, professional or other privilege. If you have received this email in > error please notify the system manager at postmaster at ulster.ac.uk and > delete this email immediately. Any views or opinions expressed are solely > those of the author and do not necessarily represent those of Ulster > University. > The University's computer systems may be monitored and communications > carried out on them may be recorded to secure the effective operation of > the system and for other lawful purposes. Ulster University does not > guarantee that this email or any attachments are free from viruses or 100% > secure. Unless expressly stated in the body of a separate attachment, the > text of email is not intended to form a binding contract. Correspondence to > and from the University may be subject to requests for disclosure by 3rd > parties under relevant legislation. > *The Ulster University was founded by Royal Charter in 1984 and is > registered with company number RC000726 and VAT registered number > GB672390524.The primary contact address for Ulster University in Northern > Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA* > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > > > This email and any attachments are confidential and intended solely for > the use of the addressee and may contain information which is covered by > legal, professional or other privilege. If you have received this email in > error please notify the system manager at postmaster at ulster.ac.uk and > delete this email immediately. Any views or opinions expressed are solely > those of the author and do not necessarily represent those of Ulster > University. > The University's computer systems may be monitored and communications > carried out on them may be recorded to secure the effective operation of > the system and for other lawful purposes. Ulster University does not > guarantee that this email or any attachments are free from viruses or 100% > secure. Unless expressly stated in the body of a separate attachment, the > text of email is not intended to form a binding contract. Correspondence to > and from the University may be subject to requests for disclosure by 3rd > parties under relevant legislation. > *The Ulster University was founded by Royal Charter in 1984 and is > registered with company number RC000726 and VAT registered number > GB672390524.The primary contact address for Ulster University in Northern > Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA* > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Wed Nov 21 14:47:07 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Wed, 21 Nov 2018 13:47:07 +0000 Subject: [FieldTrip] code update: ft_timelockanalysis Message-ID: <5032C771-6BB8-4379-ADD3-C2557FD6F2C9@donders.ru.nl> Dear community, As you know, the FieldTrip development model is based on continuous incremental improvements, where changes are made to the code repository on github, sometimes multiple times per day. In our implicit code of conduct, one of our core values is to maintain backward compatibility. This means that we not only aim not to break properly functioning user scripts that have been written with a (not too outdated) slightly older version of the FieldTrip code, but also that we aim at function behavior being stable over time (unless we find a bug in the code), both in terms of settings of the default parameters, and in terms of numerical output. Occasionally, we decide to slightly sacrifice backward compatibility at the benefit of overall code consistency and maintainability. Usually, we don’t write an e-mail to the list when this happens, because everything is properly documented on github or bugzilla, and we don’t want to bother you with these mundane issues, but today I make an exception. The reason for this is that I re-implemented ft_timelockanalysis, which is one of the oldest FieldTrip functions and a loyal companion in many an analysis project over the past 15 years. Since I assume that this is a function that many of you use, I just want to make you aware of this. The most noticeable (if at all) changes are: 1) the option cfg.vartrllength has been deprecated. 2) the default behavior has changed from expecting the trials of the input data to have fixed length (and throwing an error otherwise) into full support of variable trial lengths, representing missing data as NaNs. 3) if you want to explicitly use the old option cfg.vartrllength=0 you should now specify cfg.latency = ‘minperiod’ 4) with the cfg.keeptrials you will either get an output structure with a single trial representation or with an average representation, not with both. This means that structures with both a ‘trial’, and ‘avg’ field will not be generated anymore. Probably, you won’t need to change anything in your scripts, but since it is difficult to foresee all possible scenarios you might want to think this over yourself. Happy computing, Jan-Mathijs J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands -------------- next part -------------- An HTML attachment was scrubbed... URL: From martin.rosenfelder at uni-ulm.de Wed Nov 21 16:14:39 2018 From: martin.rosenfelder at uni-ulm.de (Martin Rosenfelder) Date: Wed, 21 Nov 2018 16:14:39 +0100 Subject: [FieldTrip] Support vector machine Message-ID: <3b26-5bf57680-1-3f44a540@39160932> Dear fieldtrip community, I am using the DMLT toolbox' svm in order to classify EEG data in two different conditions. More precisely, the SVM is trained on trigger-based trials for a mental motor imagery condition and a resting state condition. The classifier works fine with accuracy rates between 50-80 %. In order to boost the performance of the classifier I would now like to look into the parameterization of the classifier, e.g. how the classifier is build. This includes decision rules the classifier uses in order to analyze the data I am feeding in. I was able to detect the SVM behind the ft_crossvalidate function but not how it works or how I can customize it. Is there a possibility to 'personalize' the SVM in the DMLT Toolbox? Thank you very much in advance for your help and effort! Best, Martin -- M.Sc.-Psych. Martin Rosenfelder Wissenschaftlicher Mitarbeiter Klinische und Biologische Psychologie Universität Ulm Raum 47.2.259 +49 731-50 26592 martin.rosenfelder at uni-ulm.de From S.Arana at donders.ru.nl Wed Nov 21 18:10:21 2018 From: S.Arana at donders.ru.nl (Arana, S.L. (Sophie)) Date: Wed, 21 Nov 2018 17:10:21 +0000 Subject: [FieldTrip] Support vector machine In-Reply-To: <3b26-5bf57680-1-3f44a540@39160932> References: <3b26-5bf57680-1-3f44a540@39160932> Message-ID: <1542820221153.94026@donders.ru.nl> Hi Martin, the options for svm as supported by the dml toolbox are limited to the properties of the svm class, that is weights, regularization, precomputed kernels and output (see fieldtrip/external/dmlt/+dml/svm.m) You can adjust those via the config you pass to ft_timelockstatistics, such as for example: cfg = []; cfg.method = 'crossvalidate'; cfg.type = 'nfold' ... cfg.mva = dml.analysis({dml.svm('C',lambda,'anyotheroption',xx}) out = ft_timelockstatistics(cfg...) Concerning the implementation, I am not using the svm bit of the toolbox myself, so I am by no means an expert but let me give it a shot. From what I see you can either by option 'native'=true use the svm as implemented in the Bioinformatics toolbox, otherwise the toolbox will compute a linear kernel and compute the classifier with quadratic loss as specified in fieldtrip/external/dmlt/external/svm/l2svm_cg. It seems there are more options implemented there for finding the weights but I'm afraid in order to get at those you would have to adjust the input arguments yourself in the svm.m code (i.e. line 70 & 75). Hope this helps a bit. Best, Sophie ___________________ M.Sc. Sophie L. Arana Doctoral researcher Neurobiology of Language - MPI for Psycholinguistics Max Planck Institute for Psycholinguistics PO Box 310, 6500 AH Nijmegen Netherlands T +31 24-3610887 E sophie.arana at mpi.nl ________________________________________ From: fieldtrip on behalf of Martin Rosenfelder Sent: Wednesday, November 21, 2018 4:14 PM To: fieldtrip at science.ru.nl Subject: [FieldTrip] Support vector machine Dear fieldtrip community, I am using the DMLT toolbox' svm in order to classify EEG data in two different conditions. More precisely, the SVM is trained on trigger-based trials for a mental motor imagery condition and a resting state condition. The classifier works fine with accuracy rates between 50-80 %. In order to boost the performance of the classifier I would now like to look into the parameterization of the classifier, e.g. how the classifier is build. This includes decision rules the classifier uses in order to analyze the data I am feeding in. I was able to detect the SVM behind the ft_crossvalidate function but not how it works or how I can customize it. Is there a possibility to 'personalize' the SVM in the DMLT Toolbox? Thank you very much in advance for your help and effort! Best, Martin -- M.Sc.-Psych. Martin Rosenfelder Wissenschaftlicher Mitarbeiter Klinische und Biologische Psychologie Universität Ulm Raum 47.2.259 +49 731-50 26592 martin.rosenfelder at uni-ulm.de _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 From nemethd at gmail.com Fri Nov 23 11:13:16 2018 From: nemethd at gmail.com (Dezso Nemeth) Date: Fri, 23 Nov 2018 11:13:16 +0100 Subject: [FieldTrip] Posdoc in Lyon, France In-Reply-To: References: Message-ID: *Postdoc in Cognitive Neuroscience* · CRNL – Center for Research in Neuroscience in Lyon · Published: 25-10-2018 · Closing date: 01-12-2018 · Contract: Fixed-term, 2,5 years · Start date: March 01, 2019 (flexible) *Job description* Applications are invited for a highly motivated, enthusiastic postdoctoral researcher with a PhD in cognitive neuroscience (or related field) to join a well-supported, friendly research team, based in the internationally renowned Center for Research in Neuroscience in Lyon (University of Lyon, CRNS, INSERM). The postdoctoral position is part of a research project named REWIRING that is funded by IDEXLYON Fellowship. The postdoc will be embedded in the IDEXLYON team (PI: Dezso Nemeth) at CRNL, Lyon. Using methods of M/EEG, fMRI and non-invasive brain stimulation (e.g., TMS), the project aims to investigate how memory representations can be updated ('rewired') in the human brain. More specifically, we will investigate the entire process of how statistical and sequential regularities are extracted from the environment (memory formation), how the extracted knowledge is consolidated and how it can be rewired. For more details see the publications of Dezso Nemeth and Karolina Janacsek at http://nemethlab.com/publications/, and particularly the following paper: Szegedi-Hallgató, E., Janacsek, K., Vékony, T., Tasi, L. A., Kerepes, L., Hompoth, E. A., ... & Németh, D. (2017). Explicit instructions and consolidation promote rewiring of automatic behaviors in the human mind. Scientific Reports, 7(1), 4365. The overall aim of Project REWIRING is to improve human learning and memory performance and boost rewiring of automatic behaviors. Within this project, the post-holder will be responsible for designing and carrying out experiments, analyzing data, and writing up manuscripts. Additionally, the postdoc will be closely involved in daily supervision of PhD and MSc students who work on the project. *Profile (Person specification)* Candidates who only partially meet the following profile are nonetheless strongly encouraged to apply! · PhD in cognitive neuroscience or an adjacent field (psychological, biological, biomedical, or computer sciences, also physics and mathematics); · A strong academic track record including publications in leading (inter)disciplinary journals; · A strong interest for fundamental research in cognitive neurosciences; · Advanced computational and/or programming skills (Matlab, Python, or other languages); · Experience in functional connectivity analysis (EEG, MEG or MRI); · Experience and interest in training and supervising junior scientists; · Capacity to participate in an interdisciplinary and international research environment; · Excellent interpersonal and communication skills to effectively collaborate and communicate in academia; · A proactive and goal-directed attitude, good organizational skills; · Fluency in written and spoken English and motivation to learn French. *Organization* The project is embedded in the unique and excellent infrastructure of the CRNL - Center for Research in Neuroscience in Lyon. Researchers working on this theme jointly organize regular discussion meetings and lectures to promote integration of research conducted within systems, behavioral, and cognitive neurosciences. Read more about what it means *to work at * CRNL. *Employment conditions* Salary will be in accordance with the relevant national labor agreement and based on research experience and qualifications. The earliest start date for this position is March 2018 (later start possible upon agreement). *Comments and contact information* *Application* We request applicants to send the following documents: 1) A cover letter briefly describing how their skills and experience meet the profile as set out in the person specification (max 1 page) 2) A research statement explaining their research interests in relation to Project REWIRING or to the PI’s publications (max 2 pages) (optional) 3) A recent CV and publication list 4) Two writing samples of the applicant's most significant work (published or unpublished manuscripts). 5) Contact information of three professional references. *Information* All additional information about the vacancy can be obtained from Dezso Nemeth, Principal Investigator, via nemeth at nemethlab.com. Submit your application to the following email address: hr at nemethlab.com *Please apply before December 1 (23:59 GMT).* *We are committed to building a diverse, equitable and inclusive scientific community.* *For this post, we particularly welcome applications by women and ethnic minorities.* *Recruitment agencies are asked not to respond to this job posting.* -------------------------------------- NEMETH, Dezso (PhD, DSc) Brain, Memory and Language Lab: http://www.memory-and-language.com Phone: +36-1-4614500/3565, +36-1-4614500/3519 -------------- next part -------------- An HTML attachment was scrubbed... URL: From aitor.martinezegurcegui at uzh.ch Mon Nov 26 12:00:08 2018 From: aitor.martinezegurcegui at uzh.ch (Aitor Egurtzegi) Date: Mon, 26 Nov 2018 12:00:08 +0100 Subject: [FieldTrip] segmenting and merging trial data Message-ID: <95bab51c-4397-b725-79e1-d2860100ccca@uzh.ch> Dear researchers at Fieldtrip, I have defined a very long time window for each trial with ft_preprocessing. I'm actually only interested in the very beginning and the end of the trial, so now I'm trying to segment my trials by using ft_redefinetrial and specifying the time window of interest with cfg.toilim, which I run in two steps, one for the beginning and the other for the end of the trial. My problem arises when I try to merge both segments together, because I tried the various ft_append functions but they do not merge my trials the way I want, so I would like to know if there's a way to put the data together again, or on the other hand if I can use a different approach, such as slicing and throwing away the part of the trial I'm not interested in. Hence, instead of taking two slices of data for the trials and merging them, discarding what's in between my time window of interest for each trial. Thanks in advance, Aitor From jonas at obleser.de Mon Nov 26 12:36:59 2018 From: jonas at obleser.de (Jonas Obleser) Date: Mon, 26 Nov 2018 12:36:59 +0100 Subject: [FieldTrip] =?utf-8?q?4-y_postdoc_opportunity_in_L=C3=BCbeck=2C_?= =?utf-8?q?Germany?= Message-ID: <3C4AADDB-DB6E-46E0-88AA-D3E17127E356@obleser.de> Dear colleagues, New Postdoc opportunity, starting April 1: Come do a 4-y Postdoc with us in University of Lübeck! Modellers and Causal-inference-folks should feel especially targeted. Besides our own EEG lab, a shared research MR Scanner, we have ample data to play with (and a few undergrads to teach stats to now and then). Link: https://tinyurl.com/obleserlab-postdoc-EN Best wishes, Jonas Jonas Obleser Professor Chair in Physiological Psychology and Research Methods University of Lübeck Department of Psychology MFC 8, Maria-Goeppert-Straße 9a 23562 Lübeck, Germany Phone +49 (0)451 3101 3620 Mobile +49 (0)171 6993337 jonas.obleser at uni-luebeck.de http://jonasobleser.com From Silvia.Formica at UGent.be Mon Nov 26 14:19:22 2018 From: Silvia.Formica at UGent.be (Silvia Formica) Date: Mon, 26 Nov 2018 13:19:22 +0000 Subject: [FieldTrip] ft_redefinedata after pre-processing Message-ID: <1543238361133.67318@UGent.be> ?Dear all, I'm in need of suggestions on the correct analyses strategy for my data. I have an experiment with a cue followed (after ~2 seconds) by a target. I epoched the data cue-locked and pre-processed my dataset. In my preprocessing I'm downsampling, re-referencing, rejecting some trials, running ICA, etc etc. My problem is that I would want to also epoch my data target-locked, but I want to do it after pre-processing the cue-locked dataset. This is because I want to exclude the same trials and apply exactly the same corrections to the two different datasets. I tried to use the function ft_redefinetrial, creating the new probe-locked trial definition and applying it to the cue-locked dataset. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) But I do not obtain what I wished for, because I get a data_target dataset that doesn't account for the trial rejection I performed while pre-processing the cue-locked dataset. So my questions are: 1) is it possible to achieve my goal? 2) If yes, what is the right strategy to do it? Thanks for any input and suggestion! Best, Silvia -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Mon Nov 26 15:43:27 2018 From: christine.blume at sbg.ac.at (Blume Christine) Date: Mon, 26 Nov 2018 14:43:27 +0000 Subject: [FieldTrip] ft_redefinedata after pre-processing In-Reply-To: <1543238361133.67318@UGent.be> References: <1543238361133.67318@UGent.be> Message-ID: <257c087109ac4b95a365d036a6971f15@sbg.ac.at> Dear Silvia, Do I understand correctly that data_cue is epoched, but not pre-processed? You can apply ft_rejectartifact after ft_redefinetrial. We usually pre-process continuous datasets and save the cfg of this pre-processing (in the example below cfg_myartifactrejection). Only then we epoch the data and apply this artifactrejection. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) cfg = []; data_target_clean = ft_rejectartifact(cfg_myartifactrejection, data_target); I hope this helps… Best, Christine Von: fieldtrip Im Auftrag von Silvia Formica Gesendet: Montag, 26. November 2018 14:19 An: fieldtrip at science.ru.nl Betreff: [FieldTrip] ft_redefinedata after pre-processing ​Dear all, I'm in need of suggestions on the correct analyses strategy for my data. I have an experiment with a cue followed (after ~2 seconds) by a target. I epoched the data cue-locked and pre-processed my dataset. In my preprocessing I'm downsampling, re-referencing, rejecting some trials, running ICA, etc etc. My problem is that I would want to also epoch my data target-locked, but I want to do it after pre-processing the cue-locked dataset. This is because I want to exclude the same trials and apply exactly the same corrections to the two different datasets. I tried to use the function ft_redefinetrial, creating the new probe-locked trial definition and applying it to the cue-locked dataset. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) But I do not obtain what I wished for, because I get a data_target dataset that doesn't account for the trial rejection I performed while pre-processing the cue-locked dataset. So my questions are: 1) is it possible to achieve my goal? 2) If yes, what is the right strategy to do it? Thanks for any input and suggestion! Best, Silvia -------------- next part -------------- An HTML attachment was scrubbed... URL: From Silvia.Formica at UGent.be Mon Nov 26 16:11:48 2018 From: Silvia.Formica at UGent.be (Silvia Formica) Date: Mon, 26 Nov 2018 15:11:48 +0000 Subject: [FieldTrip] ft_redefinedata after pre-processing In-Reply-To: <257c087109ac4b95a365d036a6971f15@sbg.ac.at> References: <1543238361133.67318@UGent.be>, <257c087109ac4b95a365d036a6971f15@sbg.ac.at> Message-ID: <1543245106825.84804@UGent.be> Dear Christine, thank you for your quick response! In my example data_cue is epoched and pre-processed. Nevertheless, I think the approach you suggested might work also in my case. I also downsample, re-reference and filter the continuous data. After these first steps, I epoch the cue-locked dataset. After epoching, I perform artifact rejection and ICA. I guess I can apply your approach to my data by running this exact same pipeline and saving the "cfg_artifactrejection" and my ICA components. Then I would go back to the filtered continuous data, epoch them target-locked and apply to this target-locked dataset the "cfg_artifactrejection"? saved before and removing the same ICA components selected before. Do you think this would be an acceptable approach? Thanks a lot! Silvia ________________________________ From: fieldtrip on behalf of Blume Christine Sent: 26 November 2018 15:43 To: fieldtrip at science.ru.nl Subject: Re: [FieldTrip] ft_redefinedata after pre-processing Dear Silvia, Do I understand correctly that data_cue is epoched, but not pre-processed? You can apply ft_rejectartifact after ft_redefinetrial. We usually pre-process continuous datasets and save the cfg of this pre-processing (in the example below cfg_myartifactrejection). Only then we epoch the data and apply this artifactrejection. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) cfg = []; data_target_clean = ft_rejectartifact(cfg_myartifactrejection, data_target); I hope this helps... Best, Christine Von: fieldtrip Im Auftrag von Silvia Formica Gesendet: Montag, 26. November 2018 14:19 An: fieldtrip at science.ru.nl Betreff: [FieldTrip] ft_redefinedata after pre-processing ?Dear all, I'm in need of suggestions on the correct analyses strategy for my data. I have an experiment with a cue followed (after ~2 seconds) by a target. I epoched the data cue-locked and pre-processed my dataset. In my preprocessing I'm downsampling, re-referencing, rejecting some trials, running ICA, etc etc. My problem is that I would want to also epoch my data target-locked, but I want to do it after pre-processing the cue-locked dataset. This is because I want to exclude the same trials and apply exactly the same corrections to the two different datasets. I tried to use the function ft_redefinetrial, creating the new probe-locked trial definition and applying it to the cue-locked dataset. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) But I do not obtain what I wished for, because I get a data_target dataset that doesn't account for the trial rejection I performed while pre-processing the cue-locked dataset. So my questions are: 1) is it possible to achieve my goal? 2) If yes, what is the right strategy to do it? Thanks for any input and suggestion! Best, Silvia -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Mon Nov 26 18:30:47 2018 From: christine.blume at sbg.ac.at (Blume Christine) Date: Mon, 26 Nov 2018 17:30:47 +0000 Subject: [FieldTrip] ft_redefinedata after pre-processing In-Reply-To: <1543245106825.84804@UGent.be> References: <1543238361133.67318@UGent.be>, <257c087109ac4b95a365d036a6971f15@sbg.ac.at> <1543245106825.84804@UGent.be> Message-ID: Dear Silvia, What you could do is do your downsampling, re-referencing, filtering and ICA on continuous data and save the resulting dataset as data1. Then, you do the artefact rejection on continuous data (unless you have long episodes that are of no interest, then you may reconsider) and save this to cfg_artifactrejection. That way, you obtain an artefact rejection file that is independent from epoching, sometimes not the worst idea. Then you go back and load data1, do the epoching, and then do the artefact rejection. Best, Christine Von: fieldtrip Im Auftrag von Silvia Formica Gesendet: Montag, 26. November 2018 16:12 An: FieldTrip discussion list Betreff: Re: [FieldTrip] ft_redefinedata after pre-processing Dear Christine, thank you for your quick response! In my example data_cue is epoched and pre-processed. Nevertheless, I think the approach you suggested might work also in my case. I also downsample, re-reference and filter the continuous data. After these first steps, I epoch the cue-locked dataset. After epoching, I perform artifact rejection and ICA. I guess I can apply your approach to my data by running this exact same pipeline and saving the "cfg_artifactrejection" and my ICA components. Then I would go back to the filtered continuous data, epoch them target-locked and apply to this target-locked dataset the "cfg_artifactrejection"​ saved before and removing the same ICA components selected before. Do you think this would be an acceptable approach? Thanks a lot! Silvia ________________________________ From: fieldtrip > on behalf of Blume Christine > Sent: 26 November 2018 15:43 To: fieldtrip at science.ru.nl Subject: Re: [FieldTrip] ft_redefinedata after pre-processing Dear Silvia, Do I understand correctly that data_cue is epoched, but not pre-processed? You can apply ft_rejectartifact after ft_redefinetrial. We usually pre-process continuous datasets and save the cfg of this pre-processing (in the example below cfg_myartifactrejection). Only then we epoch the data and apply this artifactrejection. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) cfg = []; data_target_clean = ft_rejectartifact(cfg_myartifactrejection, data_target); I hope this helps… Best, Christine Von: fieldtrip > Im Auftrag von Silvia Formica Gesendet: Montag, 26. November 2018 14:19 An: fieldtrip at science.ru.nl Betreff: [FieldTrip] ft_redefinedata after pre-processing ​Dear all, I'm in need of suggestions on the correct analyses strategy for my data. I have an experiment with a cue followed (after ~2 seconds) by a target. I epoched the data cue-locked and pre-processed my dataset. In my preprocessing I'm downsampling, re-referencing, rejecting some trials, running ICA, etc etc. My problem is that I would want to also epoch my data target-locked, but I want to do it after pre-processing the cue-locked dataset. This is because I want to exclude the same trials and apply exactly the same corrections to the two different datasets. I tried to use the function ft_redefinetrial, creating the new probe-locked trial definition and applying it to the cue-locked dataset. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) But I do not obtain what I wished for, because I get a data_target dataset that doesn't account for the trial rejection I performed while pre-processing the cue-locked dataset. So my questions are: 1) is it possible to achieve my goal? 2) If yes, what is the right strategy to do it? Thanks for any input and suggestion! Best, Silvia -------------- next part -------------- An HTML attachment was scrubbed... URL: From junho0525 at gmail.com Tue Nov 27 13:18:27 2018 From: junho0525 at gmail.com (=?UTF-8?B?7IaQ7KSA7Zi4?=) Date: Tue, 27 Nov 2018 21:18:27 +0900 Subject: [FieldTrip] Predefined Source Orientations for Source Localization In-Reply-To: References: Message-ID: Dear fieldtrip community, I am currently trying to get source time series form my MEG data using beamformer LCMV. I have run ft_sourceanalysis with fixed orientation option, and I found that the estimated source orientations do not perpendicular to source surface. I thought that dendrites of pyramidal cells point perpendicular directions to the cortical surface, but the result was different from what I expected. Then I have tried to provide predefined orientations so that I use the provided orientations rather than estimate them. However, I also failed to get orientations that I expected. So, here is my question, 1) Why estimated source orientation does not perpendicular to source surface? 2) Are there any methods or options that use predefined source orientations for source localization? 3) If it is natural to get sources that are not perpendicular to the cortical surface, how can I explain those source activities in terms of neuronal structure and neuronal activity? Thank you, Junho -------------- next part -------------- An HTML attachment was scrubbed... URL: From aitor.martinezegurcegui at uzh.ch Wed Nov 28 11:49:06 2018 From: aitor.martinezegurcegui at uzh.ch (Aitor Egurtzegi) Date: Wed, 28 Nov 2018 11:49:06 +0100 Subject: [FieldTrip] automatic IC rejection Message-ID: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> Dear researchers at Fieldtrip, In order to make my work more reproducible, I would like to automatically reject ICs instead of doing visual inspection and rejection of the components. Unfortunately, I haven't found any documentation for such thing. Is there a way to do it in Fieldtrip? Best, Aitor From julian.keil at gmail.com Wed Nov 28 12:52:26 2018 From: julian.keil at gmail.com (Julian Keil) Date: Wed, 28 Nov 2018 12:52:26 +0100 Subject: [FieldTrip] automatic IC rejection In-Reply-To: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> References: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> Message-ID: Dear Aitor, irrespective of whether that is a good idea, I can suggest the EEGLab Toolbox SASICA (https://github.com/dnacombo/SASICA/ ). This toolbox can identify ICs based on a number of criteria and automatically reject these. So, if you manage to transform your FT-data into an EEGLab-like structure, this might work for you. Best, Julian > Am 28.11.2018 um 11:49 schrieb Aitor Egurtzegi : > > Dear researchers at Fieldtrip, > > > In order to make my work more reproducible, I would like to automatically reject ICs instead of doing visual inspection and rejection of the components. Unfortunately, I haven't found any documentation for such thing. Is there a way to do it in Fieldtrip? > > Best, > Aitor > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From david.schubring at uni-konstanz.de Wed Nov 28 13:47:00 2018 From: david.schubring at uni-konstanz.de (David Schubring) Date: Wed, 28 Nov 2018 12:47:00 +0000 Subject: [FieldTrip] automatic IC rejection In-Reply-To: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> References: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> Message-ID: <9b277ef4-067a-9a61-a11b-da7d504b9717@uni-konstanz.de> Dear Aitor, the closest thing I know of for a data-driven approach of selecting independent components is COMPASS, quote: "COMPASS is a MATLAB and EEGLAB based algorithm with the purpose of providing the user with a convenient technique for automatic Independent Component (IC) selection with respect to the contributions of the ICs to a certain ERP." Link to the toolbox: http://53450283.de.strato-hosting.eu/jrw/lab/e_compass.htm Paper: Wessel, J. R., & Ullsperger, M. (2011). Selection of independent components representing event-related brain potentials: a data-driven approach for greater objectivity. Neuroimage, 54(3), 2105-2115. https://doi.org/10.1016/j.neuroimage.2010.10.033 I have only theoretical experience with the toolbox as I only learned about it in a workshop and did not yet have the time to test and implement it in my personal FieldTrip workflow (even though it is on my ever growing to-do list). So far it looked like a useful thing to try out to me, especially as code can better be reproduced than "personal judgement". Best, David Am 28.11.2018 um 10:49 schrieb Aitor Egurtzegi: > Dear researchers at Fieldtrip, > > > In order to make my work more reproducible, I would like to > automatically reject ICs instead of doing visual inspection and > rejection of the components. Unfortunately, I haven't found any > documentation for such thing. Is there a way to do it in Fieldtrip? > > Best, > Aitor > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -- Dr. David Schubring General & Biological Psychology University of Konstanz Room C524 P.O. Box 36 78457 Konstanz Phone: +49-(0)7531-88-5350 Homepage: https://gpbp.uni-konstanz.de/people-page/david-schubring From MatthiasCMoeller at gmx.de Wed Nov 28 15:15:41 2018 From: MatthiasCMoeller at gmx.de (=?UTF-8?Q?=22Matthias_M=C3=B6ller=22?=) Date: Wed, 28 Nov 2018 15:15:41 +0100 Subject: [FieldTrip] Spike artifacts in EEG, 7-12Hz Message-ID: An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: 9 Hz Artefact.JPG Type: image/jpeg Size: 492043 bytes Desc: not available URL: From christine.blume at sbg.ac.at Wed Nov 28 16:04:32 2018 From: christine.blume at sbg.ac.at (Blume Christine) Date: Wed, 28 Nov 2018 15:04:32 +0000 Subject: [FieldTrip] Spike artifacts in EEG, 7-12Hz In-Reply-To: References: Message-ID: <07979b73a31a4cb5bd02398211e36ec6@sbg.ac.at> Dear Matthias, Admittedly, I do not know what this could be. While the first step should of course be to find the source and eliminate this (any devices in the EEG lab, artefact from the acoustic stimulation/headphones, …), in case you are unable to find it, you could remove the component(s) that correspond to the artefact. But as I said, the first goal should always be to record clean data… Best, Christine Von: fieldtrip Im Auftrag von "Matthias Möller" Gesendet: Mittwoch, 28. November 2018 15:16 An: fieldtrip at science.ru.nl Betreff: [FieldTrip] Spike artifacts in EEG, 7-12Hz Dear all, my name is Matthias and I am relatively new to the field of EEG analysis. I'm currently carrying out a study on the effects of natural sounds on the quantitative EEG in patients with Parkinson's disease at the universities of Vanvouver and Marburg. Right now I'm experiencing these weird artifacts as seen in the screenshot. It's sharp spikes, looking similar to ECG artifacts, but in frequencies of 7-12Hz. They are also showing up in the independent components after ICA as well. The recording was done at a sampling rate of 500 Hz, I've applied a Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz (recording took place in Canada). Has anyone seem similar artifacts before and maybe even knows how to get rid of them? Many thanks in advance and all the best, Matthias -------------- next part -------------- An HTML attachment was scrubbed... URL: From koculak.marcin at gmail.com Thu Nov 29 00:19:13 2018 From: koculak.marcin at gmail.com (Marcin Koculak) Date: Thu, 29 Nov 2018 00:19:13 +0100 Subject: [FieldTrip] Spike artifacts in EEG, 7-12Hz In-Reply-To: <07979b73a31a4cb5bd02398211e36ec6@sbg.ac.at> References: <07979b73a31a4cb5bd02398211e36ec6@sbg.ac.at> Message-ID: Dear Matthias, I have never seen such artifacts, but if you are working with patients with Parkinson's, have you checked if they have deep brain stimulation devices? Maybe that is causing the artifacts in the data? best, Marcin śr., 28 lis 2018 o 16:11 Blume Christine napisał(a): > Dear Matthias, > > > > Admittedly, I do not know what this could be. While the first step should > of course be to find the source and eliminate this (any devices in the EEG > lab, artefact from the acoustic stimulation/headphones, …), in case you are > unable to find it, you could remove the component(s) that correspond to the > artefact. But as I said, the first goal should always be to record clean > data… > > > > Best, > > Christine > > > > > > *Von:* fieldtrip *Im Auftrag von *"Matthias > Möller" > *Gesendet:* Mittwoch, 28. November 2018 15:16 > *An:* fieldtrip at science.ru.nl > *Betreff:* [FieldTrip] Spike artifacts in EEG, 7-12Hz > > > > Dear all, > > > > my name is Matthias and I am relatively new to the field of EEG analysis. > I'm currently carrying out a study on the effects of natural sounds on the > quantitative EEG in patients with Parkinson's disease at the universities > of Vanvouver and Marburg. > > > > Right now I'm experiencing these weird artifacts as seen in the > screenshot. It's sharp spikes, looking similar to ECG artifacts, but in > frequencies of 7-12Hz. > > They are also showing up in the independent components after ICA as well. > > > > The recording was done at a sampling rate of 500 Hz, I've applied a > Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz > (recording took place in Canada). > > > > Has anyone seem similar artifacts before and maybe even knows how to get > rid of them? > > > > Many thanks in advance and all the best, > > > > Matthias > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From anne.hauswald at me.com Thu Nov 29 09:26:20 2018 From: anne.hauswald at me.com (anne Hauswald) Date: Thu, 29 Nov 2018 09:26:20 +0100 Subject: [FieldTrip] Spike artifacts in EEG, 7-12Hz In-Reply-To: References: Message-ID: Dear Matthias, I don’t know where your artifacts come from, but I have some suggestions that might help you getting close to the source. - is it transient or do you find it throughout the whole recording? - do you find it in more than one recording? - does your choice of reference or filtering influence it? Not sure it will lead to something, but at least you will have a better understanding of this artifact. Best Anne > Am 28.11.2018 um 15:15 schrieb Matthias Möller : > > Dear all, > > my name is Matthias and I am relatively new to the field of EEG analysis. I'm currently carrying out a study on the effects of natural sounds on the quantitative EEG in patients with Parkinson's disease at the universities of Vanvouver and Marburg. > > Right now I'm experiencing these weird artifacts as seen in the screenshot. It's sharp spikes, looking similar to ECG artifacts, but in frequencies of 7-12Hz. > They are also showing up in the independent components after ICA as well. > > The recording was done at a sampling rate of 500 Hz, I've applied a Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz (recording took place in Canada). > > Has anyone seem similar artifacts before and maybe even knows how to get rid of them? > > Many thanks in advance and all the best, > > Matthias > <9 Hz Artefact.JPG>_______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From uwe.graichen at tu-ilmenau.de Thu Nov 29 10:08:29 2018 From: uwe.graichen at tu-ilmenau.de (Uwe Graichen) Date: Thu, 29 Nov 2018 10:08:29 +0100 Subject: [FieldTrip] =?utf-8?q?15_PhD_positions_in_Marie_Slodowskwa-Curie?= =?utf-8?q?_Innovative_Training_Network_=E2=80=9CINFANS=22?= In-Reply-To: <36f6a451-912e-ecfe-8796-7bde4fb9ff8e@tu-ilmenau.de> References: <36f6a451-912e-ecfe-8796-7bde4fb9ff8e@tu-ilmenau.de> Message-ID: As part of the Marie Skłodowska-Curie Innovative Training Network “INFANS - INtegrating Functional Assessment measures for Neonatal Safeguard" http://www.infansproject.eu , funded by the European Union’s Horizon 2020 Research and Innovation Programme, we advertise 15 PhD positions. The goal of INFANS is to develop a new neonatal brain monitoring system, designed to overcome the severe shortage of clinically viable means to high quality monitor the brain function in infancy, crucial to prevent later life neurological, cognitive and motor impairment. To accomplish this goal, INFANS established a structured European PhD training programme in biomedical engineering, signal processing and clinical procedures to train a new generation of creative and entrepreneurial young researchers. The individual research projects of the early stage researchers (ESR) encompass the topics: technological innovation, industrial development, clinical validation, identification of neonatal healthcare needs. As part of their research the INFANS ESRs will develop a novel platform for high quality, clinically-viable EEG-NIRS monitoring accessible worldwide. Well-targeted visits and secondments, soft skills and dynamic training activities, an Open Science strategy, extensive involvement of ESRs in the network events organization, extensive contacts with other research, training and industrial European networks, dissemination activities and the award of Double doctoral degrees are further assets offered to INFANS ESRs. Excellent science, industrial leadership and societal challenge are merged in the INFANS network. The INFANS consortium includes 6 academic and 4 non-academic partners from 6 EU countries, among which leading universities, companies and clinical institutions. The partners involved in INFANS share complementary expertise and facilities to provide international, interdisciplinary and intersectoral research training and mobility that will complement local doctoral training. The INFANS ESRs will become independent researchers with improved career prospects in both the academic and non-academic sectors, and will advance the EU capacity for innovation in biomedical engineering. The institution and the place where the different ESR projects will be carried out, the scientific supervisor(s), individual research project titles, and contact person for each available position can be found specified in the attached document. -------------- next part -------------- A non-text attachment was scrubbed... Name: ITN_INFANS Open_Position_20181129.pdf Type: application/pdf Size: 125155 bytes Desc: not available URL: From ablenkmann at gmail.com Thu Nov 29 15:30:27 2018 From: ablenkmann at gmail.com (Alejandro Blenkmann) Date: Thu, 29 Nov 2018 15:30:27 +0100 Subject: [FieldTrip] Fwd: FW: [all] 5 PhDs and 5 Postdoc fellowships (University of Oslo) In-Reply-To: References: Message-ID: Dear all, A new call for postdocs and PhDs is open at RITMO Center in Oslo. See information below, Best, Alejandro *From:* Alexander Refsum Jensenius *Sent:* Wednesday, November 21, 2018 10:01 PM *To:* all at ritmo.uio.no *Subject:* [all] 5 PhDs and 5 Postdoc fellowships (University of Oslo) Dear all, We are happy to announce a total of 10 recruit positions at RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion at the University of Oslo, Norway. 5 PhD fellowships in: · Rhythmic Robotics · Rhythm and Temporality in Audiovisual Narrative Media · Cognitive Neuroscience · Cross-modal Rhythms · Entrainment and Pleasure 5 postdoc fellowships in · Rhythmic Robotics · Eye Tracking and Motion Capture of rhythm-related behavior · Music, Time, and Consciousness · Electrophysiological basis of rhythm perception and production · fMRI RITMO is a Centre of Excellence funded by the Research Council of Norway, and focuses on rhythm as a structuring mechanism for temporal dimensions of human life. RITMO researchers work in a unique interdisciplinary constellation, with world-leading competence in musicology, psychology and informatics. The researchers have access to state-of-the-art facilities in sound/video recording, motion capture, eye tracking, physiological measurements, various types of brain imaging (EEG, fMRI), and rapid prototyping and robotics laboratories. · Application deadlines: 15 January / 15 March 2019 (check position) · Start-dates: August 2019 Please forward to relevant candidates. Apologies for cross-posting. *In addition: we are happy to host Marie Curie fellowship applications. Please get in touch if you are interested. * Best, -- Alexander Refsum Jensenius, Ph.D. Associate Professor, Department of Musicology, University of Oslo http://people.uio.no/alexanje/ Deputy Director, RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion http://www.uio.no/ritmo/ New book: "A NIME Reader" http://link.springer.com/book/10.1007/978-3-319-47214-0 New master's programme: " Music, Communication & Technology" http://www.uio.no/mct-master/ -- Alejandro Blenkmann, PhD Postdoctoral Fellow Front Neurolab Department of Psychology University of Oslo -- -------------- next part -------------- An HTML attachment was scrubbed... URL: From MatthiasCMoeller at gmx.de Thu Nov 29 16:55:26 2018 From: MatthiasCMoeller at gmx.de (=?UTF-8?Q?=22Matthias_M=C3=B6ller=22?=) Date: Thu, 29 Nov 2018 16:55:26 +0100 Subject: [FieldTrip] Spike artifacts 7-12Hz In-Reply-To: References: Message-ID: An HTML attachment was scrubbed... URL: From Hoang.Truong at colorado.edu Thu Nov 29 18:22:11 2018 From: Hoang.Truong at colorado.edu (Hoang Truong) Date: Thu, 29 Nov 2018 10:22:11 -0700 Subject: [FieldTrip] HELP: import simple data from txt file - non supported format, no header Message-ID: Dear community, This is Tony, from CS Department, University of Colorado Boulder. I am new to eeg analysis and fieldtrip. Currently I work on recording eeg and emg data using our own hardware with 2 eeg and 2 emg channels respectively. Since we use our custom hardware, the data are saved in a simple format of txt file with 7 columns (example data on Dropbox: link ): 3 first columns are data from environment sensors, the next 4 are eeg and emg data (250Hz sampling rate, 42s recording) I would like to ask what will be a proper way for me to import this data into fieldtrip. I checked the faq about importing custom data but I do not understand how to make custom readheader, readdata and readevent function. Any help would be appreciated (suggestions, code examples, etc). Thank you so much. Sincerely, Tony -------------- next part -------------- An HTML attachment was scrubbed... URL: From stephen.whitmarsh at gmail.com Thu Nov 29 19:37:11 2018 From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh) Date: Thu, 29 Nov 2018 19:37:11 +0100 Subject: [FieldTrip] HELP: import simple data from txt file - non supported format, no header In-Reply-To: References: Message-ID: Hi Tony, As long as you are able to load the data into MATLAB, you should be able to put it into a MATLAB structure that is in accordance with how FieldTrip expect data to be organized. See the second paragraph "Circumvent the FieldTrip reading functions" of the FAQ you referred to, and this FAQ on the data structures. It will be something like (syntax is probably wrong): % read from file mydatafromfile = tdfread('mydata.txt'); % check the help of the read function, not sure how this goes exactly % datastructure of raw data, i.e. not epoched mydata = []; mydata.label = {'env1','env2','env3','eeg1','eeg2','emg'}; % label for every column of data mydata.trial{1} = mydatafromfile; % data in columns, channels in rows mydata.time{1} = (1:size(mydatafromfile,1)) * 1/250; % create fake time-axis, should be same length as data If you are not able to program in MATLAB (yet), FieldTrip might not (yet) be for you. In that case you can take a look at other MATLAB based software such as e.g. EEGlab or Brainstorm , which use GUIs for importing data that might help (I'm not familiar with them though). Out of curiosity, what hardware are you using? Build it yourself? Good luck, Stephen On Thu, 29 Nov 2018 at 18:44, Hoang Truong wrote: > Dear community, > > This is Tony, from CS Department, University of Colorado Boulder. I am new > to eeg analysis and fieldtrip. Currently I work on recording eeg and emg > data using our own hardware with 2 eeg and 2 emg channels respectively. > Since we use our custom hardware, the data are saved in a simple format of > txt file with 7 columns (example data on Dropbox: link > ): 3 > first columns are data from environment sensors, the next 4 are eeg and emg > data (250Hz sampling rate, 42s recording) > I would like to ask what will be a proper way for me to import this data > into fieldtrip. I checked the faq about importing custom data but I do not > understand how to make custom readheader, readdata and readevent function. > Any help would be appreciated (suggestions, code examples, etc). > Thank you so much. > > Sincerely, > Tony > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Thu Nov 29 21:56:27 2018 From: christine.blume at sbg.ac.at (Blume Christine) Date: Thu, 29 Nov 2018 20:56:27 +0000 Subject: [FieldTrip] Spike artifacts 7-12Hz In-Reply-To: References: , Message-ID: Dear Matthias, I would guess that if the ICA nicely removes this component that might be the best approach, it is also commonly used to remove eye blinks or ECG artefacts from data. You can also try to design your own filter if the artefact is really centered around 9 Hz, although filtering is an issue on its own. Hope this helps! Christine ________________________________ Von: fieldtrip im Auftrag von "Matthias Möller" Gesendet: Donnerstag, 29. November 2018 16:55:26 An: fieldtrip at science.ru.nl Betreff: Re: [FieldTrip] Spike artifacts 7-12Hz Dear all, thanks a lot for your prompt answers, that is really supportive, thank you! Testing is already done, so I can't test for devices anymore whether they produce artifacts or not. To be honest I'm running out of ideas which devices might have been responsible. I used bluetooth headphones where the frequency is a lot higher, and in some sets I don't have the artifacts although subjects were stimulated using the same headphones. It's not in all recordings but in some. If it's in the recording, then it is throughout the whole one. It's not influenced by referencing or filtering. Deep brain stimulation seems to be to high in frequency as well. For now the only thing I can do is to remove the respective components indeed. Does anyone else maybe have an idea about how to filter out/get rid of those artifacts? Best, Matthias Gesendet: Donnerstag, 29. November 2018 um 10:08 Uhr Von: fieldtrip-request at science.ru.nl An: fieldtrip at science.ru.nl Betreff: fieldtrip Digest, Vol 96, Issue 22 Send fieldtrip mailing list submissions to fieldtrip at science.ru.nl To subscribe or unsubscribe via the World Wide Web, visit https://mailman.science.ru.nl/mailman/listinfo/fieldtrip or, via email, send a message with subject or body 'help' to fieldtrip-request at science.ru.nl You can reach the person managing the list at fieldtrip-owner at science.ru.nl When replying, please edit your Subject line so it is more specific than "Re: Contents of fieldtrip digest..." Today's Topics: 1. Re: Spike artifacts in EEG, 7-12Hz (Blume Christine) 2. Re: Spike artifacts in EEG, 7-12Hz (Marcin Koculak) 3. Re: Spike artifacts in EEG, 7-12Hz (anne Hauswald) 4. 15 PhD positions in Marie Slodowskwa-Curie Innovative Training Network “INFANS" (Uwe Graichen) ---------------------------------------------------------------------- Message: 1 Date: Wed, 28 Nov 2018 15:04:32 +0000 From: Blume Christine To: "fieldtrip at science.ru.nl" Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz Message-ID: <07979b73a31a4cb5bd02398211e36ec6 at sbg.ac.at> Content-Type: text/plain; charset="utf-8" Dear Matthias, Admittedly, I do not know what this could be. While the first step should of course be to find the source and eliminate this (any devices in the EEG lab, artefact from the acoustic stimulation/headphones, …), in case you are unable to find it, you could remove the component(s) that correspond to the artefact. But as I said, the first goal should always be to record clean data… Best, Christine Von: fieldtrip Im Auftrag von "Matthias Möller" Gesendet: Mittwoch, 28. November 2018 15:16 An: fieldtrip at science.ru.nl Betreff: [FieldTrip] Spike artifacts in EEG, 7-12Hz Dear all, my name is Matthias and I am relatively new to the field of EEG analysis. I'm currently carrying out a study on the effects of natural sounds on the quantitative EEG in patients with Parkinson's disease at the universities of Vanvouver and Marburg. Right now I'm experiencing these weird artifacts as seen in the screenshot. It's sharp spikes, looking similar to ECG artifacts, but in frequencies of 7-12Hz. They are also showing up in the independent components after ICA as well. The recording was done at a sampling rate of 500 Hz, I've applied a Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz (recording took place in Canada). Has anyone seem similar artifacts before and maybe even knows how to get rid of them? Many thanks in advance and all the best, Matthias -------------- next part -------------- An HTML attachment was scrubbed... URL: ------------------------------ Message: 2 Date: Thu, 29 Nov 2018 00:19:13 +0100 From: Marcin Koculak To: fieldtrip at science.ru.nl Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz Message-ID: Content-Type: text/plain; charset="utf-8" Dear Matthias, I have never seen such artifacts, but if you are working with patients with Parkinson's, have you checked if they have deep brain stimulation devices? Maybe that is causing the artifacts in the data? best, Marcin śr., 28 lis 2018 o 16:11 Blume Christine napisał(a): > Dear Matthias, > > > > Admittedly, I do not know what this could be. While the first step should > of course be to find the source and eliminate this (any devices in the EEG > lab, artefact from the acoustic stimulation/headphones, …), in case you are > unable to find it, you could remove the component(s) that correspond to the > artefact. But as I said, the first goal should always be to record clean > data… > > > > Best, > > Christine > > > > > > *Von:* fieldtrip *Im Auftrag von *"Matthias > Möller" > *Gesendet:* Mittwoch, 28. November 2018 15:16 > *An:* fieldtrip at science.ru.nl > *Betreff:* [FieldTrip] Spike artifacts in EEG, 7-12Hz > > > > Dear all, > > > > my name is Matthias and I am relatively new to the field of EEG analysis. > I'm currently carrying out a study on the effects of natural sounds on the > quantitative EEG in patients with Parkinson's disease at the universities > of Vanvouver and Marburg. > > > > Right now I'm experiencing these weird artifacts as seen in the > screenshot. It's sharp spikes, looking similar to ECG artifacts, but in > frequencies of 7-12Hz. > > They are also showing up in the independent components after ICA as well. > > > > The recording was done at a sampling rate of 500 Hz, I've applied a > Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz > (recording took place in Canada). > > > > Has anyone seem similar artifacts before and maybe even knows how to get > rid of them? > > > > Many thanks in advance and all the best, > > > > Matthias > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: ------------------------------ Message: 3 Date: Thu, 29 Nov 2018 09:26:20 +0100 From: anne Hauswald To: FieldTrip discussion list Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz Message-ID: Content-Type: text/plain; charset="utf-8" Dear Matthias, I don’t know where your artifacts come from, but I have some suggestions that might help you getting close to the source. - is it transient or do you find it throughout the whole recording? - do you find it in more than one recording? - does your choice of reference or filtering influence it? Not sure it will lead to something, but at least you will have a better understanding of this artifact. Best Anne > Am 28.11.2018 um 15:15 schrieb Matthias Möller : > > Dear all, > > my name is Matthias and I am relatively new to the field of EEG analysis. I'm currently carrying out a study on the effects of natural sounds on the quantitative EEG in patients with Parkinson's disease at the universities of Vanvouver and Marburg. > > Right now I'm experiencing these weird artifacts as seen in the screenshot. It's sharp spikes, looking similar to ECG artifacts, but in frequencies of 7-12Hz. > They are also showing up in the independent components after ICA as well. > > The recording was done at a sampling rate of 500 Hz, I've applied a Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz (recording took place in Canada). > > Has anyone seem similar artifacts before and maybe even knows how to get rid of them? > > Many thanks in advance and all the best, > > Matthias > <9 Hz Artefact.JPG>_______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: ------------------------------ Message: 4 Date: Thu, 29 Nov 2018 10:08:29 +0100 From: Uwe Graichen To: fieldtrip at science.ru.nl Subject: [FieldTrip] 15 PhD positions in Marie Slodowskwa-Curie Innovative Training Network “INFANS" Message-ID: Content-Type: text/plain; charset="utf-8" As part of the Marie Skłodowska-Curie Innovative Training Network “INFANS - INtegrating Functional Assessment measures for Neonatal Safeguard" http://www.infansproject.eu , funded by the European Union’s Horizon 2020 Research and Innovation Programme, we advertise 15 PhD positions. The goal of INFANS is to develop a new neonatal brain monitoring system, designed to overcome the severe shortage of clinically viable means to high quality monitor the brain function in infancy, crucial to prevent later life neurological, cognitive and motor impairment. To accomplish this goal, INFANS established a structured European PhD training programme in biomedical engineering, signal processing and clinical procedures to train a new generation of creative and entrepreneurial young researchers. The individual research projects of the early stage researchers (ESR) encompass the topics: technological innovation, industrial development, clinical validation, identification of neonatal healthcare needs. As part of their research the INFANS ESRs will develop a novel platform for high quality, clinically-viable EEG-NIRS monitoring accessible worldwide. Well-targeted visits and secondments, soft skills and dynamic training activities, an Open Science strategy, extensive involvement of ESRs in the network events organization, extensive contacts with other research, training and industrial European networks, dissemination activities and the award of Double doctoral degrees are further assets offered to INFANS ESRs. Excellent science, industrial leadership and societal challenge are merged in the INFANS network. The INFANS consortium includes 6 academic and 4 non-academic partners from 6 EU countries, among which leading universities, companies and clinical institutions. The partners involved in INFANS share complementary expertise and facilities to provide international, interdisciplinary and intersectoral research training and mobility that will complement local doctoral training. The INFANS ESRs will become independent researchers with improved career prospects in both the academic and non-academic sectors, and will advance the EU capacity for innovation in biomedical engineering. The institution and the place where the different ESR projects will be carried out, the scientific supervisor(s), individual research project titles, and contact person for each available position can be found specified in the attached document. -------------- next part -------------- A non-text attachment was scrubbed... Name: ITN_INFANS Open_Position_20181129.pdf Type: application/pdf Size: 125155 bytes Desc: not available URL: ------------------------------ Subject: Digest Footer _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 ------------------------------ End of fieldtrip Digest, Vol 96, Issue 22 ***************************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: From rmontefusco at med.uchile.cl Thu Nov 29 22:31:40 2018 From: rmontefusco at med.uchile.cl (Rodrigo Montefusco) Date: Thu, 29 Nov 2018 18:31:40 -0300 Subject: [FieldTrip] Spike artifacts 7-12Hz In-Reply-To: References: Message-ID: Dear Matthias, I use to have something like that. Have you noticed if that happened at some particular time of the day? In my case it was solved after turning some equipment off in a neighbor lab, or by doing the recordings early in the morning, late in the night or during weekends. Good luck! Rodrigo On Thu, Nov 29, 2018 at 5:56 PM Blume Christine wrote: > Dear Matthias, > > > I would guess that if the ICA nicely removes this component that might be > the best approach, it is also commonly used to remove eye blinks or ECG > artefacts from data. You can also try to design your own filter if the > artefact is really centered around 9 Hz, although filtering is an issue on > its own. > > > Hope this helps! > > Christine > > > ------------------------------ > *Von:* fieldtrip im Auftrag von > "Matthias Möller" > *Gesendet:* Donnerstag, 29. November 2018 16:55:26 > *An:* fieldtrip at science.ru.nl > *Betreff:* Re: [FieldTrip] Spike artifacts 7-12Hz > > Dear all, > > thanks a lot for your prompt answers, that is really supportive, thank > you! > > > Testing is already done, so I can't test for devices anymore whether they > produce artifacts or not. > > To be honest I'm running out of ideas which devices might have been > responsible. I used bluetooth headphones where the frequency is a lot > higher, and in some sets I don't have the artifacts although subjects were > stimulated using the same headphones. It's not in all recordings but in > some. If it's in the recording, then it is throughout the whole one. > It's not influenced by referencing or filtering. > > Deep brain stimulation seems to be to high in frequency as well. > > For now the only thing I can do is to remove the respective components > indeed. > Does anyone else maybe have an idea about how to filter out/get rid of > those artifacts? > > Best, > > Matthias > > *Gesendet:* Donnerstag, 29. November 2018 um 10:08 Uhr > *Von:* fieldtrip-request at science.ru.nl > *An:* fieldtrip at science.ru.nl > *Betreff:* fieldtrip Digest, Vol 96, Issue 22 > Send fieldtrip mailing list submissions to > fieldtrip at science.ru.nl > > To subscribe or unsubscribe via the World Wide Web, visit > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > or, via email, send a message with subject or body 'help' to > fieldtrip-request at science.ru.nl > > You can reach the person managing the list at > fieldtrip-owner at science.ru.nl > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of fieldtrip digest..." > > > Today's Topics: > > 1. Re: Spike artifacts in EEG, 7-12Hz (Blume Christine) > 2. Re: Spike artifacts in EEG, 7-12Hz (Marcin Koculak) > 3. Re: Spike artifacts in EEG, 7-12Hz (anne Hauswald) > 4. 15 PhD positions in Marie Slodowskwa-Curie Innovative > Training Network “INFANS" (Uwe Graichen) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Wed, 28 Nov 2018 15:04:32 +0000 > From: Blume Christine > To: "fieldtrip at science.ru.nl" > Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz > Message-ID: <07979b73a31a4cb5bd02398211e36ec6 at sbg.ac.at> > Content-Type: text/plain; charset="utf-8" > > Dear Matthias, > > Admittedly, I do not know what this could be. While the first step should > of course be to find the source and eliminate this (any devices in the EEG > lab, artefact from the acoustic stimulation/headphones, …), in case you are > unable to find it, you could remove the component(s) that correspond to the > artefact. But as I said, the first goal should always be to record clean > data… > > Best, > Christine > > > Von: fieldtrip Im Auftrag von "Matthias > Möller" > Gesendet: Mittwoch, 28. November 2018 15:16 > An: fieldtrip at science.ru.nl > Betreff: [FieldTrip] Spike artifacts in EEG, 7-12Hz > > Dear all, > > my name is Matthias and I am relatively new to the field of EEG analysis. > I'm currently carrying out a study on the effects of natural sounds on the > quantitative EEG in patients with Parkinson's disease at the universities > of Vanvouver and Marburg. > > Right now I'm experiencing these weird artifacts as seen in the > screenshot. It's sharp spikes, looking similar to ECG artifacts, but in > frequencies of 7-12Hz. > They are also showing up in the independent components after ICA as well. > > The recording was done at a sampling rate of 500 Hz, I've applied a > Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz > (recording took place in Canada). > > Has anyone seem similar artifacts before and maybe even knows how to get > rid of them? > > Many thanks in advance and all the best, > > Matthias > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: < > http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20181128/ccb3622e/attachment-0001.html > > > > ------------------------------ > > Message: 2 > Date: Thu, 29 Nov 2018 00:19:13 +0100 > From: Marcin Koculak > To: fieldtrip at science.ru.nl > Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz > Message-ID: > > Content-Type: text/plain; charset="utf-8" > > Dear Matthias, > I have never seen such artifacts, but if you are working with patients with > Parkinson's, have you checked if they have deep brain stimulation devices? > Maybe that is causing the artifacts in the data? > best, > Marcin > > śr., 28 lis 2018 o 16:11 Blume Christine > napisał(a): > > > Dear Matthias, > > > > > > > > Admittedly, I do not know what this could be. While the first step should > > of course be to find the source and eliminate this (any devices in the > EEG > > lab, artefact from the acoustic stimulation/headphones, …), in case you > are > > unable to find it, you could remove the component(s) that correspond to > the > > artefact. But as I said, the first goal should always be to record clean > > data… > > > > > > > > Best, > > > > Christine > > > > > > > > > > > > *Von:* fieldtrip *Im Auftrag von > *"Matthias > > Möller" > > *Gesendet:* Mittwoch, 28. November 2018 15:16 > > *An:* fieldtrip at science.ru.nl > > *Betreff:* [FieldTrip] Spike artifacts in EEG, 7-12Hz > > > > > > > > Dear all, > > > > > > > > my name is Matthias and I am relatively new to the field of EEG analysis. > > I'm currently carrying out a study on the effects of natural sounds on > the > > quantitative EEG in patients with Parkinson's disease at the universities > > of Vanvouver and Marburg. > > > > > > > > Right now I'm experiencing these weird artifacts as seen in the > > screenshot. It's sharp spikes, looking similar to ECG artifacts, but in > > frequencies of 7-12Hz. > > > > They are also showing up in the independent components after ICA as well. > > > > > > > > The recording was done at a sampling rate of 500 Hz, I've applied a > > Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz > > (recording took place in Canada). > > > > > > > > Has anyone seem similar artifacts before and maybe even knows how to get > > rid of them? > > > > > > > > Many thanks in advance and all the best, > > > > > > > > Matthias > > _______________________________________________ > > fieldtrip mailing list > > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > > https://doi.org/10.1371/journal.pcbi.1002202 > > > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: < > http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20181129/ee2745dc/attachment-0001.html > > > > ------------------------------ > > Message: 3 > Date: Thu, 29 Nov 2018 09:26:20 +0100 > From: anne Hauswald > To: FieldTrip discussion list > Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz > Message-ID: > Content-Type: text/plain; charset="utf-8" > > Dear Matthias, > > I don’t know where your artifacts come from, but I have some suggestions > that might help you getting close to the source. > - is it transient or do you find it throughout the whole recording? > - do you find it in more than one recording? > - does your choice of reference or filtering influence it? > > Not sure it will lead to something, but at least you will have a better > understanding of this artifact. > > Best Anne > > > > Am 28.11.2018 um 15:15 schrieb Matthias Möller >: > > > > Dear all, > > > > my name is Matthias and I am relatively new to the field of EEG > analysis. I'm currently carrying out a study on the effects of natural > sounds on the quantitative EEG in patients with Parkinson's disease at the > universities of Vanvouver and Marburg. > > > > Right now I'm experiencing these weird artifacts as seen in the > screenshot. It's sharp spikes, looking similar to ECG artifacts, but in > frequencies of 7-12Hz. > > They are also showing up in the independent components after ICA as well. > > > > The recording was done at a sampling rate of 500 Hz, I've applied a > Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz > (recording took place in Canada). > > > > Has anyone seem similar artifacts before and maybe even knows how to get > rid of them? > > > > Many thanks in advance and all the best, > > > > Matthias > > <9 Hz Artefact.JPG>_______________________________________________ > > fieldtrip mailing list > > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > > https://doi.org/10.1371/journal.pcbi.1002202 > > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: < > http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20181129/87d8fe88/attachment-0001.html > > > > ------------------------------ > > Message: 4 > Date: Thu, 29 Nov 2018 10:08:29 +0100 > From: Uwe Graichen > To: fieldtrip at science.ru.nl > Subject: [FieldTrip] 15 PhD positions in Marie Slodowskwa-Curie > Innovative Training Network “INFANS" > Message-ID: > Content-Type: text/plain; charset="utf-8" > > As part of the Marie Skłodowska-Curie Innovative Training Network > “INFANS - INtegrating Functional Assessment measures for Neonatal > Safeguard" http://www.infansproject.eu , funded by the European Union’s > Horizon 2020 Research and Innovation Programme, we advertise 15 PhD > positions. > > The goal of INFANS is to develop a new neonatal brain monitoring system, > designed to overcome the severe shortage of clinically viable means to > high quality monitor the brain function in infancy, crucial to prevent > later life neurological, cognitive and motor impairment. To accomplish > this goal, INFANS established a structured European PhD training > programme in biomedical engineering, signal processing and clinical > procedures to train a new generation of creative and entrepreneurial > young researchers. > > The individual research projects of the early stage researchers (ESR) > encompass the topics: technological innovation, industrial development, > clinical validation, identification of neonatal healthcare needs. As > part of their research the INFANS ESRs will develop a novel platform for > high quality, clinically-viable EEG-NIRS monitoring accessible > worldwide. Well-targeted visits and secondments, soft skills and dynamic > training activities, an Open Science strategy, extensive involvement of > ESRs in the network events organization, extensive contacts with other > research, training and industrial European networks, dissemination > activities and the award of Double doctoral degrees are further assets > offered to INFANS ESRs. > > Excellent science, industrial leadership and societal challenge are > merged in the INFANS network. The INFANS consortium includes 6 academic > and 4 non-academic partners from 6 EU countries, among which leading > universities, companies and clinical institutions. The partners involved > in INFANS share complementary expertise and facilities to provide > international, interdisciplinary and intersectoral research training and > mobility that will complement local doctoral training. The INFANS ESRs > will become independent researchers with improved career prospects in > both the academic and non-academic sectors, and will advance the EU > capacity for innovation in biomedical engineering. > > The institution and the place where the different ESR projects will be > carried out, the scientific supervisor(s), individual research project > titles, and contact person for each available position can be found > specified in the attached document. > > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: ITN_INFANS Open_Position_20181129.pdf > Type: application/pdf > Size: 125155 bytes > Desc: not available > URL: < > http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20181129/928f2253/attachment.pdf > > > > ------------------------------ > > Subject: Digest Footer > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > ------------------------------ > > End of fieldtrip Digest, Vol 96, Issue 22 > ***************************************** > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From Pranish.Kantak at UTSouthwestern.edu Thu Nov 29 22:47:42 2018 From: Pranish.Kantak at UTSouthwestern.edu (Pranish Kantak) Date: Thu, 29 Nov 2018 21:47:42 +0000 Subject: [FieldTrip] Remove from list serve Message-ID: <6D018139-18FD-44FB-808B-09268E09BE99@UTSouthwestern.edu> Hi! Could you please remove me from the field trip list serve? Thank you! Sent from my iPhone ________________________________ UT Southwestern Medical Center The future of medicine, today. From jason.taylor at manchester.ac.uk Fri Nov 30 02:05:05 2018 From: jason.taylor at manchester.ac.uk (Jason Taylor) Date: Fri, 30 Nov 2018 01:05:05 +0000 Subject: [FieldTrip] automatic IC rejection In-Reply-To: <9b277ef4-067a-9a61-a11b-da7d504b9717@uni-konstanz.de> References: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> <9b277ef4-067a-9a61-a11b-da7d504b9717@uni-konstanz.de> Message-ID: <48E6E95C4AB29E4BAD7908051F5C17EF016E1E31BC@MBXP07.ds.man.ac.uk> Hi Aitor, If you have an 'objective' measure of the artefact you're trying to remove (e.g., VEOG for blinks), a relatively straightforward method is to run a temporal correlation between each IC's activation time-course and the artefact channel's time-course. You can then reject any IC with a correlation higher than some threshold, or with a Z-score (r value relative to the distribution of IC r values) above some threshold. This tends to work very well for identifying blinks, and fairly well for eye-movements (*EOG), and can work for pulse artefact if you have recorded ECG. To avoid spurious correlations due to high-frequency noise, you can filter (e.g., 1 to 30 Hz) the component and artefact signals before correlating them (but obviously go back to the original unfiltered signals to continue with your analysis). Best wishes, Jason -----Original Message----- From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of David Schubring Sent: 28 November 2018 12:47 To: fieldtrip at science.ru.nl; aitor.martinezegurcegui at uzh.ch Subject: Re: [FieldTrip] automatic IC rejection Dear Aitor, the closest thing I know of for a data-driven approach of selecting independent components is COMPASS, quote: "COMPASS is a MATLAB and EEGLAB based algorithm with the purpose of providing the user with a convenient technique for automatic Independent Component (IC) selection with respect to the contributions of the ICs to a certain ERP." Link to the toolbox: http://53450283.de.strato-hosting.eu/jrw/lab/e_compass.htm Paper: Wessel, J. R., & Ullsperger, M. (2011). Selection of independent components representing event-related brain potentials: a data-driven approach for greater objectivity. Neuroimage, 54(3), 2105-2115. https://doi.org/10.1016/j.neuroimage.2010.10.033 I have only theoretical experience with the toolbox as I only learned about it in a workshop and did not yet have the time to test and implement it in my personal FieldTrip workflow (even though it is on my ever growing to-do list). So far it looked like a useful thing to try out to me, especially as code can better be reproduced than "personal judgement". Best, David Am 28.11.2018 um 10:49 schrieb Aitor Egurtzegi: > Dear researchers at Fieldtrip, > > > In order to make my work more reproducible, I would like to > automatically reject ICs instead of doing visual inspection and > rejection of the components. Unfortunately, I haven't found any > documentation for such thing. Is there a way to do it in Fieldtrip? > > Best, > Aitor > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -- Dr. David Schubring General & Biological Psychology University of Konstanz Room C524 P.O. Box 36 78457 Konstanz Phone: +49-(0)7531-88-5350 Homepage: https://gpbp.uni-konstanz.de/people-page/david-schubring _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 From y.visser at hotmail.com Fri Nov 30 10:47:03 2018 From: y.visser at hotmail.com (Yvonne Visser) Date: Fri, 30 Nov 2018 09:47:03 +0000 Subject: [FieldTrip] Cluster based permutation test interpretation Message-ID: Dear all, Thank you for welcoming me to the discussion list, my name is Yvonne Visser and I currently work as a research assistant with dr. Aaron Schurger at Neurospin. During my masters program I learned about cluster based permutation tests for electrophysiological data and distinctly remember how from this type of test one can not conclude that a particular cluster is significant (in line with what is said on the fieldtrip website here, http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test) We are currently using the cluster based permutation test in the analysis of our experiment, but we are a bit confused on how to interpret the results from our test. To give you a short introduction to our experiment: we are looking for a relationship between a behavioural variable and our collected EEG data. So we computed the grand average time frequency spectrum in a single channel of the time bins of interest. Then, we correlated each time/frequency point in this 2d matrix with the behavioural variable in that trial. This resulted in a correlation matrix like you can see in attachment1_correlationmatrix. As you can see, we also computed clusters of time/frequency points with p<0.05. After computing the permutations, we found that the biggest "real" cluster is bigger than any of the permuted clusters. Now, we would like to conclude something from this result about which frequency band at what time is correlated to our behavioural variable. We found a fieldtrip function called ft_clusterplot that does seem to suggest that you can highlight a specific cluster it if it survives the test, but isn't that exactly what my lectures and the webpage say we should not do? Can we say that activity in the alpha band around -0.75 to 0 (where the biggest cluster is located) is correlated to the size of the movement? Or should we not conclude something about which cluster is significant and can we only say that some time frequency power is correlated to our behavioural variable? If the second is true, do you have any advice for us to make the interpretation more specific? Thank you so much in advance, and please let us know if anything is unclear. Kind regards, Yvonne & Aaron. -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: attachment1_correlationmatrix.jpg Type: image/jpeg Size: 61990 bytes Desc: attachment1_correlationmatrix.jpg URL: From aitor.martinezegurcegui at uzh.ch Fri Nov 30 14:42:39 2018 From: aitor.martinezegurcegui at uzh.ch (Aitor Egurtzegi) Date: Fri, 30 Nov 2018 14:42:39 +0100 Subject: [FieldTrip] automatic IC rejection In-Reply-To: <48E6E95C4AB29E4BAD7908051F5C17EF016E1E31BC@MBXP07.ds.man.ac.uk> References: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> <9b277ef4-067a-9a61-a11b-da7d504b9717@uni-konstanz.de> <48E6E95C4AB29E4BAD7908051F5C17EF016E1E31BC@MBXP07.ds.man.ac.uk> Message-ID: <6630afcc-003d-3ecf-2d53-d0174a75ea09@uzh.ch> Dear Jason, Thanks a lot for your reply. Is there a Fieldtrip method already implemented to run such temporal correlation? or would I have to do the implementation in raw Matlab? Thanks in advance, Aitor On 11/30/18 2:05 AM, Jason Taylor wrote: > Hi Aitor, > > If you have an 'objective' measure of the artefact you're trying to remove (e.g., VEOG for blinks), a relatively straightforward method is to run a temporal correlation between each IC's activation time-course and the artefact channel's time-course. You can then reject any IC with a correlation higher than some threshold, or with a Z-score (r value relative to the distribution of IC r values) above some threshold. This tends to work very well for identifying blinks, and fairly well for eye-movements (*EOG), and can work for pulse artefact if you have recorded ECG. To avoid spurious correlations due to high-frequency noise, you can filter (e.g., 1 to 30 Hz) the component and artefact signals before correlating them (but obviously go back to the original unfiltered signals to continue with your analysis). > > Best wishes, > Jason > > > -----Original Message----- > From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of David Schubring > Sent: 28 November 2018 12:47 > To: fieldtrip at science.ru.nl; aitor.martinezegurcegui at uzh.ch > Subject: Re: [FieldTrip] automatic IC rejection > > Dear Aitor, > > the closest thing I know of for a data-driven approach of selecting > independent components is COMPASS, quote: > > "COMPASS is a MATLAB and EEGLAB based algorithm with the purpose of > providing the user with a convenient technique for automatic Independent > Component (IC) selection with respect to the contributions of the ICs to > a certain ERP." > > Link to the toolbox: > > http://53450283.de.strato-hosting.eu/jrw/lab/e_compass.htm > > Paper: > > Wessel, J. R., & Ullsperger, M. (2011). Selection of independent > components representing event-related brain potentials: a data-driven > approach for greater objectivity. Neuroimage, 54(3), 2105-2115. > https://doi.org/10.1016/j.neuroimage.2010.10.033 > > I have only theoretical experience with the toolbox as I only learned > about it in a workshop and did not yet have the time to test and > implement it in my personal FieldTrip workflow (even though it is on my > ever growing to-do list). So far it looked like a useful thing to try > out to me, especially as code can better be reproduced than "personal > judgement". > > Best, > David > > Am 28.11.2018 um 10:49 schrieb Aitor Egurtzegi: >> Dear researchers at Fieldtrip, >> >> >> In order to make my work more reproducible, I would like to >> automatically reject ICs instead of doing visual inspection and >> rejection of the components. Unfortunately, I haven't found any >> documentation for such thing. Is there a way to do it in Fieldtrip? >> >> Best, >> Aitor >> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 From jason.taylor at manchester.ac.uk Fri Nov 30 18:08:22 2018 From: jason.taylor at manchester.ac.uk (Jason Taylor) Date: Fri, 30 Nov 2018 17:08:22 +0000 Subject: [FieldTrip] automatic IC rejection In-Reply-To: <6630afcc-003d-3ecf-2d53-d0174a75ea09@uzh.ch> References: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> <9b277ef4-067a-9a61-a11b-da7d504b9717@uni-konstanz.de> <48E6E95C4AB29E4BAD7908051F5C17EF016E1E31BC@MBXP07.ds.man.ac.uk> <6630afcc-003d-3ecf-2d53-d0174a75ea09@uzh.ch> Message-ID: <48E6E95C4AB29E4BAD7908051F5C17EF016E1E3CBD@MBXP07.ds.man.ac.uk> Hi Aitor, No, not that I know of. I generally use a hacky combination of SPM, fieldtrip, and EEGLAB functions, but if you've already run ICA, you could accomplish what I suggested with some standard matlab functions. Best wishes, Jason -----Original Message----- From: Aitor Egurtzegi [mailto:aitor.martinezegurcegui at uzh.ch] Sent: 30 November 2018 13:43 To: Jason Taylor; FieldTrip discussion list Subject: Re: [FieldTrip] automatic IC rejection Dear Jason, Thanks a lot for your reply. Is there a Fieldtrip method already implemented to run such temporal correlation? or would I have to do the implementation in raw Matlab? Thanks in advance, Aitor On 11/30/18 2:05 AM, Jason Taylor wrote: > Hi Aitor, > > If you have an 'objective' measure of the artefact you're trying to remove (e.g., VEOG for blinks), a relatively straightforward method is to run a temporal correlation between each IC's activation time-course and the artefact channel's time-course. You can then reject any IC with a correlation higher than some threshold, or with a Z-score (r value relative to the distribution of IC r values) above some threshold. This tends to work very well for identifying blinks, and fairly well for eye-movements (*EOG), and can work for pulse artefact if you have recorded ECG. To avoid spurious correlations due to high-frequency noise, you can filter (e.g., 1 to 30 Hz) the component and artefact signals before correlating them (but obviously go back to the original unfiltered signals to continue with your analysis). > > Best wishes, > Jason > > > -----Original Message----- > From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of David Schubring > Sent: 28 November 2018 12:47 > To: fieldtrip at science.ru.nl; aitor.martinezegurcegui at uzh.ch > Subject: Re: [FieldTrip] automatic IC rejection > > Dear Aitor, > > the closest thing I know of for a data-driven approach of selecting > independent components is COMPASS, quote: > > "COMPASS is a MATLAB and EEGLAB based algorithm with the purpose of > providing the user with a convenient technique for automatic Independent > Component (IC) selection with respect to the contributions of the ICs to > a certain ERP." > > Link to the toolbox: > > http://53450283.de.strato-hosting.eu/jrw/lab/e_compass.htm > > Paper: > > Wessel, J. R., & Ullsperger, M. (2011). Selection of independent > components representing event-related brain potentials: a data-driven > approach for greater objectivity. Neuroimage, 54(3), 2105-2115. > https://doi.org/10.1016/j.neuroimage.2010.10.033 > > I have only theoretical experience with the toolbox as I only learned > about it in a workshop and did not yet have the time to test and > implement it in my personal FieldTrip workflow (even though it is on my > ever growing to-do list). So far it looked like a useful thing to try > out to me, especially as code can better be reproduced than "personal > judgement". > > Best, > David > > Am 28.11.2018 um 10:49 schrieb Aitor Egurtzegi: >> Dear researchers at Fieldtrip, >> >> >> In order to make my work more reproducible, I would like to >> automatically reject ICs instead of doing visual inspection and >> rejection of the components. Unfortunately, I haven't found any >> documentation for such thing. Is there a way to do it in Fieldtrip? >> >> Best, >> Aitor >> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 From abela.eugenio at gmail.com Fri Nov 30 18:25:51 2018 From: abela.eugenio at gmail.com (Eugenio Abela) Date: Fri, 30 Nov 2018 17:25:51 +0000 Subject: [FieldTrip] HELP: import simple data from txt file - non supported format, no header In-Reply-To: References: Message-ID: <666ECAD5-0675-42B3-AF4F-205F1747A735@gmail.com> Hi Tony Here’s a pretty quick and dirty fix - have a careful look to make sure it does what you want. You need FieldTrip installed and in the path; it works on my MATLAB2016a. Others on the list might have better ideas, or spot obvious errors. Good luck eugenio % Import you data D = importdata('Pressure_3_6.txt',' '); % Fill in FieldTrip data structure (check out ft_datatype_raw) % I assume columns 4, 5 are EEG, 6,7 EMG. % Channels go in rows, time in columns. data = struct(); data.label = {'EEG-1';'EEG-2';'EMG-1';'EMG-2'}; data.fsample = 250; data.time = {1:length(D)}; data.trial = {D(:,4:7)’}; % Check out how it looks cfg = []; cfg.viemode = 'vertical'; ft_databrowser(cfg,data); On 29 Nov 2018, at 17:22, Hoang Truong wrote: Dear community, This is Tony, from CS Department, University of Colorado Boulder. I am new to eeg analysis and fieldtrip. Currently I work on recording eeg and emg data using our own hardware with 2 eeg and 2 emg channels respectively. Since we use our custom hardware, the data are saved in a simple format of txt file with 7 columns (example data on Dropbox: link ): 3 first columns are data from environment sensors, the next 4 are eeg and emg data (250Hz sampling rate, 42s recording) I would like to ask what will be a proper way for me to import this data into fieldtrip. I checked the faq about importing custom data but I do not understand how to make custom readheader, readdata and readevent function. Any help would be appreciated (suggestions, code examples, etc). Thank you so much. Sincerely, Tony _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From Hoang.Truong at colorado.edu Fri Nov 30 18:47:10 2018 From: Hoang.Truong at colorado.edu (Hoang Truong) Date: Fri, 30 Nov 2018 10:47:10 -0700 Subject: [FieldTrip] HELP: import simple data from txt file - non supported format, no header In-Reply-To: <666ECAD5-0675-42B3-AF4F-205F1747A735@gmail.com> References: <666ECAD5-0675-42B3-AF4F-205F1747A735@gmail.com> Message-ID: Thanks for your prompt help, Eugenio and Stephen !!! I can view the data with Eugenio's code w MATLAB2018a. I'll continue to work from there. @Stephen: I build a small hardware piece that can capture multimodal biosignal and integrate this into some wearable prototypes. Sincerely, Tony On Fri, Nov 30, 2018 at 10:25 AM Eugenio Abela wrote: > Hi Tony > > Here’s a pretty quick and dirty fix - have a careful look to make sure it > does what you want. You need FieldTrip installed and in the path; it works > on my MATLAB2016a. Others on the list might have better ideas, or spot > obvious errors. > > Good luck > eugenio > > % Import you data > D = importdata('Pressure_3_6.txt',' '); > > > % Fill in FieldTrip data structure (check out ft_datatype_raw) > % I assume columns 4, 5 are EEG, 6,7 EMG. > % Channels go in rows, time in columns. > data = struct(); > data.label = {'EEG-1';'EEG-2';'EMG-1';'EMG-2'}; > data.fsample = 250; > data.time = {1:length(D)}; > data.trial = {D(:,4:7)’}; > > > % Check out how it looks > cfg = []; > cfg.viemode = 'vertical'; > ft_databrowser(cfg,data); > > > > On 29 Nov 2018, at 17:22, Hoang Truong wrote: > > Dear community, > > This is Tony, from CS Department, University of Colorado Boulder. I am new > to eeg analysis and fieldtrip. Currently I work on recording eeg and emg > data using our own hardware with 2 eeg and 2 emg channels respectively. > Since we use our custom hardware, the data are saved in a simple format of > txt file with 7 columns (example data on Dropbox: link > ): 3 > first columns are data from environment sensors, the next 4 are eeg and emg > data (250Hz sampling rate, 42s recording) > I would like to ask what will be a proper way for me to import this data > into fieldtrip. I checked the faq about importing custom data but I do not > understand how to make custom readheader, readdata and readevent function. > Any help would be appreciated (suggestions, code examples, etc). > Thank you so much. > > Sincerely, > Tony > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From ignasi.sols at nyu.edu Thu Nov 1 04:06:54 2018 From: ignasi.sols at nyu.edu (Ignasi Sols) Date: Wed, 31 Oct 2018 23:06:54 -0400 Subject: [FieldTrip] Brain-shift compensation Error In-Reply-To: References: Message-ID: Many thanks, Arjen. I could solve the problem following your suggestion. It was an issue with the format of the channel information, that was not recognized by* ft_channelselection.* Best, Ignasi On Wed, Oct 31, 2018 at 2:26 AM Arjen Stolk wrote: > Hi Ignasi, > > One can only guess based on that error msg alone. You might want to put a > debug marker at line 156, check whether coord0 is truly empty, and then try > to trace back to what's causing it to be empty (e.g., an empty elecpos > field in your elec structure?). > > Arjen > > On Tue, Oct 30, 2018 at 12:04 PM Ignasi Sols wrote: > >> Dear all, >> I'm following the method developed by Stolk et al (2018) to localize the >> electrodes of ECoG data. >> I'm getting this error on step 23 (Project the electrode grids to the >> surface hull of the implanted hemisphere) and I can't solve it. Could >> anyone help me with this? >> >> Thanks, >> Ignasi >> >> >> *using electrodes specified in the configuration* >> *using warp algorithm described in Dykstra et al. 2012 Neuroimage PMID: >> 22155045* >> *creating electrode pairs based on electrode positions* >> *Error using fmincon (line 241)* >> *You must provide a non-empty starting* >> *point.* >> *Error in warp_dykstra2012 (line 156)* >> *coord_snapped = fmincon(efun, coord0,* >> *[], [], [], [], [], [], cfun,* >> *options);* >> *Error in ft_electroderealign (line* >> *406)* >> * norm.elecpos =* >> warp_dykstra2012(cfg, elec, >> headshape); >> >> -- >> Ignasi Sols >> Postdoctoral Fellow >> Department of Psychology >> New York University >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> >> https://doi.org/10.1371/journal.pcbi.1002202 >> >> > _______________________________________________ > fieldtrip mailing list > > https://urldefense.proofpoint.com/v2/url?u=https-3A__mailman.science.ru.nl_mailman_listinfo_fieldtrip&d=DwIGaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=MfcAKxrNJUDchBMH7cGf8A7flR7i0Mg4wa1BIfRAxTM&m=ogxF67dx9cUcJr32WF9DDK-sz4FQ20js2rkptNpxMBA&s=Jx9wYcv0XnbORxGzfpEH0gqB1QyHZMaw-4hs4evzORE&e= > > https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1371_journal.pcbi.1002202&d=DwIGaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=MfcAKxrNJUDchBMH7cGf8A7flR7i0Mg4wa1BIfRAxTM&m=ogxF67dx9cUcJr32WF9DDK-sz4FQ20js2rkptNpxMBA&s=9nWIVPM4Xf2SQkk3fMSCzXvE82mHFH-pnn67Fhygfyk&e= > -- Ignasi Sols Postdoctoral Fellow Department of Psychology New York University -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Thu Nov 1 08:51:05 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 1 Nov 2018 07:51:05 +0000 Subject: [FieldTrip] Phase Information For PCC Beamformer In-Reply-To: <74e003128d5a0219ae6febed2c6ff119@inserm.fr> References: <74e003128d5a0219ae6febed2c6ff119@inserm.fr> Message-ID: Hi Hesham, If memory serves me well, but you may want to verify this in the code of ft_specest_mtmfft, the phase estimate is expressed relative to t=0 (considering the epoch’s time axis that is passed on to ft_specest_mtmfft). Best wishes, Jan-Mathijs > On 30 Oct 2018, at 17:05, Hesham ElShafei wrote: > > Hello Fieldtrippers! > > So I am trying to do some whole brain connectivity analysis according to this tutorial: http://www.fieldtriptoolbox.org/tutorial/networkanalysis > > All is going fine :) I just would like to understand how the phase information is obtained using these options in ft_freqanalysis > > cfg.method = 'mtmfft'; > cfg.output = 'fourier'; > > In other words , how is time incorporated in the computed phase values? > In other other words, these phase values represent the signal at which time point? > > hope I was clear enough > > Cheers! > Hesham > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From jan.schoffelen at donders.ru.nl Thu Nov 1 09:13:34 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 1 Nov 2018 08:13:34 +0000 Subject: [FieldTrip] Problem using ft_sensrorealign with Yokogawa MEG In-Reply-To: References: Message-ID: <684D7E73-1417-44EB-A75B-DE1A83864556@donders.ru.nl> Hi Victor, You don’t mention which fieldtrip version you are currently using, but ft_sensorrealign has been moved to compat/obsolete about a year ago. This means that this particular function is not actively maintained and supported anymore. At the moment, I don’t remember the reason for this, but it would mean that there is functionality in the main fieldtrip functions that got rid of the raison d’etre of ft_sensorrealign. As is mentioned in the README of the compat/obsolete directory, you should move ft_sensorrealign up a few directories. The reason for this is that the function might rely on low-level functions that are located in fieldtrip/private, which are only visible from function that are located in the directory that contains the private folder. ‘fixpos’ might be one of them. Best wishes, Jan-Mathijs J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands On 30 Oct 2018, at 10:43, Victor RG > wrote: HI Fieldtrip experts! I'm trying to align a headmodel (the one from example "Subject1") with my MEG sensors (Yokogawa system). I'm trying to do that interactively, in order to construct a Leadfied matrix as accurate as possible. To do that, I am testing this code: cfg = []; cfg.method = 'interactive'; cfg.headshape = vol.bnd(1); cfg.senstype = 'meggrad'; grad_aligned = ft_sensorrealign(cfg, grad); % Im using ft_sensorrealign cause I think is the MEG version of ft_electroderealign The variables employed consist of: >> grad = struct with fields: * balance: [1×1 struct] * chanori: [160×3 double] * chanpos: [160×3 double] * chantype: {160×1 cell} * chanunit: {160×1 cell} * coilori: [320×3 double] * coilpos: [320×3 double] * label: {160×1 cell} * tra: [160×320 double] * type: 'yokogawa160' * unit: 'cm' * fid: [1×1 struct] >> vol.bnd(1) = struct with fields: * pos: [1000×3 double] * tri: [1996×3 double] * coordsys: 'ctf' I have looked at the tutorial for EEG sensors realignment, and I have copied the procedure, since there is not a specific tutorial for MEG sensors. I thought it would be the same, but when executing it I obtain the following errors: >> Undefined function 'fixpos' for input arguments of type 'struct'. Error in ft_sensorrealign (line 255) headshape = fixpos(cfg.headshape); Error in generating_leadfield (line 63) grad_aligned = ft_sensorrealign(cfg, grad); Does anybody know how to do that, or how to do an interactive realignment with MEG sensors? Thanks in advance. Víctor. Víctor Rodríguez González Grupo de Ingeniería Biomédica, ETSIT. Universidad de Valladolid, España. _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Thu Nov 1 09:23:11 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 1 Nov 2018 08:23:11 +0000 Subject: [FieldTrip] ft_mergealgin: high residual variance? In-Reply-To: References: Message-ID: <863F96C2-45E4-4441-B330-6C0292880D72@donders.ru.nl> Hi Maria, Are you sure about the units in your headmodel (and gradiometers)? Using the value 1.0 as an inwardshift parameter suggests ‘cm’. Not sure what will happen when by accident the hs_file is in ‘m’ (which, as far as I know, is usually the case). Best wishes, Jan-Mathijs J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands On 29 Oct 2018, at 13:33, Maria Hakonen > wrote: Dear FieldTrip experts, I have run ft_mergealign across subjects to align the head positions. However, the residual variance between the original and the realigned data seems to be high: original -> template RV 21232.46 % original -> original RV 36.96 % original -> template -> original RV 9579.95 % Could someone please let me know what would be the largest acceptable change in the residual variance, and what should I do if the residual variance is too high? Does the increase in residual variance mean that there is a large shift in the head position? I have used ft_mergealign as follows: template = list of subjects (i.e. I want to calculate an average head position over the subjects) grad = data.grad; hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); vol = ft_headmodel_localspheres(hs,grad); cfg = []; cfg.template = template; cfg.inwardshift = 1.0; cfg.vol = vol; data_aligned = ft_megrealign(cfg, data); Best, Maria _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From C.Mazzetti at donders.ru.nl Thu Nov 1 12:04:23 2018 From: C.Mazzetti at donders.ru.nl (Mazzetti, C. (Cecilia)) Date: Thu, 1 Nov 2018 11:04:23 +0000 Subject: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage Message-ID: <1541070268890.93169@donders.ru.nl> Dear everyone, I have some stimulus locked data which I have to baseline with respect to the same data but cue locked. I was wondering whether there is a setting in fieldtrip which allows to do that (i.e. set the baseline timw window according to a different dataset, the cue-locked data) and applying it to the stimulus locked data. Thanks in advance! Best, Cecilia Cecilia Mazzetti - Ph.D. Candidate Donders Centre for Cognitive Neuroimaging, room 0.068 Kapittelweg 29 | 6525 EN Nijmegen -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Thu Nov 1 21:07:08 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Thu, 1 Nov 2018 16:07:08 -0400 Subject: [FieldTrip] Post hoc test following significant interaction Message-ID: Dear FieldTrip community, I have a 2 x 2 mixed study design (within factor being pre post, and between factor being group 1 and group 2) in which I am hoping to use an ANOVA to analyze. Since I have a 2 x 2 factorial design, I have been following the tutorial: -------------- next part -------------- An HTML attachment was scrubbed... URL: From pdhami06 at gmail.com Thu Nov 1 21:21:38 2018 From: pdhami06 at gmail.com (Paul Dhami) Date: Thu, 1 Nov 2018 16:21:38 -0400 Subject: [FieldTrip] Post hoc test following significant interaction Message-ID: Dear FieldTrip community, I have a 2 x 2 mixed study design (within factor being pre post, and between factor being group 1 and group 2) in which I am hoping to use an ANOVA in the cluster based permutation framework to analyze. Since I have a 2 x 2 factorial design, I have been following the tutorial: http://www.fieldtriptoolbox.org/faq/how_can_i_test_an_interaction_effect_using_cluster-based_permutation_tests as well as various previous responses on the mailing list. In the context that I have an interaction in certain electrodes at certain time points, my questions are: 1. How would I go about testing the simple effects to disentangle the interaction. Would it simply be a case of skipping the main effects and running the appropriate indep/dep t-test between each 4 of the possible contrasts for simple effects? (i.e. group 1 vs group 2 at pre, group 1 vs group 2 at post, group 1 at pre vs group 1 at post, group 2 at pre vs group 2 at post). 2). If an interaction is initially found, should the post-hoc (e.g. simple main effects) be limited to the electrodes and time in which the interactions are present, or would the simple effect contrasts be run across all electrodes and time points as were run in the initial interaction test. Any insight would be greatly appreciated. Thank you, Paul -------------- next part -------------- An HTML attachment was scrubbed... URL: From pascualm at key.uzh.ch Fri Nov 2 02:36:01 2018 From: pascualm at key.uzh.ch (pascualm at key.uzh.ch) Date: Fri, 2 Nov 2018 10:36:01 +0900 Subject: [FieldTrip] Comparison of measures of electrophysiological connectivity Message-ID: Dear Colleagues, The preprint entitled: "A comparison of bivariate frequency domain measures of electrophysiological connectivity" at: https://doi.org/10.1101/459503 might be of interest to those performing research related to electrophysiological connectivity inference. The abstract can be found below. Cordially, Roberto ... Roberto D. Pascual-Marqui, PhD, PD The KEY Institute for Brain-Mind Research, University of Zurich Visiting Professor at Neuropsychiatry, Kansai Medical University, Osaka [https://www.uzh.ch/keyinst/loreta] [scholar.google.com/citations?user=pascualmarqui] ... Abstract: The problem of interest here concerns electrophysiological signals from two cortical sites, acquired as invasive intracranial recordings, or from non-invasive estimates of cortical electric neuronal activity computed from EEG or MEG recordings (see e.g. https://doi.org/10.1101/269753). In the absence of other sources, these measured signals consist of an instantaneous linear mixture of the true, actual, unobserved local signals, due to low spatial resolution and volume conduction. A connectivity measure is unreliable as a true indicator of electrophysiological connectivity if it is not invariant to mixing, or if it reports a significant connection for a mixture of independent signals. In (Vinck et al 2011 Neuroimage 55:1548) it was shown that coherence, imaginary coherence, and phase locking value are not invariant to mixing, while the phase lag index (PLI) and the weighted version (wPLI) are invariant to mixing. Here we show that the lagged coherence (LagCoh) measure (2007, https://arxiv.org/abs/0711.1455), not studied in Vinck et al, is invariant to mixing. Additionally, we present here a new mixture-invariant connectivity statistic: the "standardized imaginary covariance" (sImCov). We also include in the comparisons the directed PLI (dPLI) by Stam et al (2012 Neuroimage 62:1415). Fourier coefficients for "N" trials are generated from a linear unidirectional causal time domain model with electrophysiological delay "k" and regression coefficient "b". 1000 random data sets of "N" trials are simulated, and for each one, and for each connectivity measure, non-parametric randomization tests are performed. The "true positive detection rate" is calculated as the fraction of 1000 cases that have significant connectivity at p<0.05, 0.1, and 0.2. The connectivity methods were compared in terms of detection rates, under non-mixed and mixed conditions, for small and large sample sizes "N", with and without jitter, and for different values of signal to noise. Under mixing, the results show that LagCoh outperforms wPLI, PLI, dPLI, and sImCov. Without mixing, LagCoh and sImCov outperform wPLI, PLI, and dPLI. Finally, it is shown that dPLI is an invalid estimator of flow direction, i.e. it reverses and "goes against the flow" by merely changing the sign of one of the time series, a fact that violates the basic definition of Granger causality. From maria.hakonen at gmail.com Fri Nov 2 07:37:44 2018 From: maria.hakonen at gmail.com (Maria Hakonen) Date: Fri, 2 Nov 2018 08:37:44 +0200 Subject: [FieldTrip] ft_mergealgin: high residual variance? In-Reply-To: <863F96C2-45E4-4441-B330-6C0292880D72@donders.ru.nl> References: <863F96C2-45E4-4441-B330-6C0292880D72@donders.ru.nl> Message-ID: Hi Jan-Mathias! Thank you for the answer! I changed the units of gradiometers and head model to cm. This clearly decreased the residual variances. Also, singleshell seems to work better than localspheres. However, the transformation seems to still increase the residual variance a lot. Here are some examples: original -> template RV 407.64 % original -> original RV 9.85 % original -> template -> original RV 10.75 % realigning trial 706 original -> template RV 393.13 % original -> original RV 9.00 % original -> template -> original RV 9.90 % realigning trial 707 original -> template RV 362.10 % original -> original RV 8.33 % original -> template -> original RV 9.18 % realigning trial 708 original -> template RV 377.15 % original -> original RV 9.43 % original -> template -> original RV 10.33 % The code is now as follows: load([data_path nameList{subj} '.mat']); grad = datafinal.grad; hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); cfg = []; cfg.method = 'singlesphere'; cfg.geom = hs; cfg.grad = grad; cfg.feedback = true; vol = ft_prepare_headmodel(cfg); vol = ft_convert_units(vol,'cm'); grad = ft_convert_units(grad,'cm'); cfg = []; cfg.template = template; cfg.inwardshift = 2.5; cfg.feedback ='no'; cfg.vol = vol; data = ft_megrealign(cfg, data); Best, Maria to 1. marrask. 2018 klo 10.35 Schoffelen, J.M. (Jan Mathijs) ( jan.schoffelen at donders.ru.nl) kirjoitti: > Hi Maria, > > Are you sure about the units in your headmodel (and gradiometers)? Using > the value 1.0 as an inwardshift parameter suggests ‘cm’. Not sure what will > happen when by accident the hs_file is in ‘m’ (which, as far as I know, is > usually the case). > > Best wishes, > > Jan-Mathijs > > J.M.Schoffelen, MD PhD > Senior Researcher, VIDI-fellow - PI, language in interaction > Telephone: +31-24-3614793 > Physical location: room 00.028 > Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands > > > > > On 29 Oct 2018, at 13:33, Maria Hakonen wrote: > > Dear FieldTrip experts, > > I have run ft_mergealign across subjects to align the head positions. > However, the residual variance between the original and the realigned data > seems to be high: > > original -> template RV 21232.46 % > original -> original RV 36.96 % > original -> template -> original RV 9579.95 % > > Could someone please let me know what would be the largest acceptable > change in the residual variance, and what should I do if the residual > variance is too high? Does the increase in residual variance mean that > there is a large shift in the head position? > > I have used ft_mergealign as follows: > > template = list of subjects (i.e. I want to calculate an average head > position over the subjects) > > grad = data.grad; > hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); > vol = ft_headmodel_localspheres(hs,grad); > > cfg = []; > cfg.template = template; > cfg.inwardshift = 1.0; > cfg.vol = vol; > data_aligned = ft_megrealign(cfg, data); > > Best, > Maria > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From tom.marshall at psy.ox.ac.uk Fri Nov 2 23:29:10 2018 From: tom.marshall at psy.ox.ac.uk (Tom Marshall) Date: Fri, 2 Nov 2018 22:29:10 +0000 Subject: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage In-Reply-To: <1541070268890.93169@donders.ru.nl> References: <1541070268890.93169@donders.ru.nl> Message-ID: Hey Cecilia, I guess you could do that with a combination of 'ft_selectdata', and 'ft_math'. Let's say you have datasets called 'stimlocked' and 'cuelocked' You'd do something like: % cut out baseline from cuelocked cfg = []; cfg.latency = [-0.5 0]; % or whatever your baseline window is baseline = ft_selectdata(cfg, cuelocked); % subtract baseline from stimlocked cfg = []; cfg.operation = 'subtract'; baseline_corrected_stimlocked = ft_math(cfg, stimlocked, baseline); I think that ft_math allows you to input algebraic expressions too. So if you wanted to do, for example, a 'relchange' baseline correction you should substitute (something like) cfg.operation = '(x1-x2)/x2'; Best, Tom ________________________________ From: fieldtrip on behalf of Mazzetti, C. (Cecilia) Sent: 01 November 2018 11:04:23 To: fieldtrip at science.ru.nl Subject: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage Dear everyone, I have some stimulus locked data which I have to baseline with respect to the same data but cue locked. I was wondering whether there is a setting in fieldtrip which allows to do that (i.e. set the baseline timw window according to a different dataset, the cue-locked data) and applying it to the stimulus locked data. Thanks in advance! Best, Cecilia Cecilia Mazzetti - Ph.D. Candidate Donders Centre for Cognitive Neuroimaging, room 0.068 Kapittelweg 29 | 6525 EN Nijmegen -------------- next part -------------- An HTML attachment was scrubbed... URL: From silvana.silva at upf.edu Mon Nov 5 16:14:35 2018 From: silvana.silva at upf.edu (SILVA PEREIRA, SILVANA) Date: Mon, 5 Nov 2018 16:14:35 +0100 Subject: [FieldTrip] ICA inspection: Error using ft_icabrowser.m Message-ID: Dear all, I'm trying to use the funtion ft_icabrowser.m, but I get the following error message: Error using topoplot_common (line 523) labels in data and labels in layout do not match Error in ft_topoplotIC (line 184) [cfg] = topoplot_common(cfg, comp); Error in ft_icabrowser (line 151) ft_topoplotIC(cfgtopo, comp); I understand that the error is due to a mismatch between the labels of the cfg struct and the layout I'm using. I modified the entries of the struct in acticap-64ch-standard2.mat, where I removed four of the channels, since we have in addition RM, LM, Heog and Veog. Adding these four labels to the original label list does not solve the problem. Any idea to work around this issue? Thank you! Silvana *Silvana Silva Pereira* /Postdoctoral Researcher/ Center for Brain and Cognition [image: Universitat Pompeu Fabra, Barcelona] -------------- next part -------------- An HTML attachment was scrubbed... URL: From dlozanosoldevilla at gmail.com Mon Nov 5 16:54:24 2018 From: dlozanosoldevilla at gmail.com (Diego Lozano-Soldevilla) Date: Mon, 5 Nov 2018 16:54:24 +0100 Subject: [FieldTrip] ICA inspection: Error using ft_icabrowser.m In-Reply-To: References: Message-ID: Hi Silvana, Could you try the component viewmode option? cfg = []; cfg.layout = 'acticap-64ch-standard'; cfg.viewmode = 'component'; % Mode specifically suited to browse through ICA data ft_databrowser(cfg, comp); Best, Diego On Mon, Nov 5, 2018, 16:38 SILVA PEREIRA, SILVANA Dear all, > > I'm trying to use the funtion ft_icabrowser.m, but I get the following > error message: > > Error using topoplot_common (line 523) > labels in data and labels in layout do not match > > Error in ft_topoplotIC (line 184) > [cfg] = topoplot_common(cfg, comp); > > Error in ft_icabrowser (line 151) > ft_topoplotIC(cfgtopo, comp); > > I understand that the error is due to a mismatch between the labels of the > cfg struct and the layout I'm using. I modified the entries of the struct > in acticap-64ch-standard2.mat, where I removed four of the channels, since > we have in addition RM, LM, Heog and Veog. Adding these four labels to the > original label list does not solve the problem. Any idea to work around > this issue? > > Thank you! > Silvana > > > *Silvana Silva Pereira* > /Postdoctoral Researcher/ > Center for Brain and Cognition > [image: Universitat Pompeu Fabra, Barcelona] > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Mon Nov 5 17:21:05 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Mon, 5 Nov 2018 17:21:05 +0100 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra Message-ID: Fellow FieldTrippers, I have identified two points of interest in the brain, between which I want to compute connectivity metrics. One is in early visual cortex, the other is in right temporal lobe, somewhat medial (compatible with hippocampus, but exact interpretation not relevant right now). I reconstructed activity for these grid points using LCMV beamformer (on MEG data) and computed source-level Fourier spectra (taper = 'dpss', tapsmofrq = 3) after applying a band-stop filter around 50 Hz and harmonics. Using ft_connectivityanalysis, I computed both debiased wPLI and Granger causality between the two points of interest. In both spectra, I see a clear peak at 60 Hz in the grand average across 36 subjects, which is also there in the majority of individual subjects (though very strong only in 2/36). Plots are attached. Now this would be exciting news if indeed it turns out to be a true highly band-limited gamma effect! But of course I suspect that this peak could very well be artifactual. I can't think of any artifact source I may have missed that would cause this, though. The projector refresh rate during the experiment was set to 120 Hz, and not 60. (Also note this is European data so AC frequency is 50 Hz and not 60, hence the band stop I mentioned earlier.) Does anyone have any idea what might be causing this sharp peak? (A genuine effect after all?) Thanks, Eelke -------------- next part -------------- A non-text attachment was scrubbed... Name: granger-spectrum.png Type: image/png Size: 14990 bytes Desc: not available URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: wpli-spectrum.png Type: image/png Size: 11765 bytes Desc: not available URL: From jan.schoffelen at donders.ru.nl Mon Nov 5 18:13:05 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Mon, 5 Nov 2018 17:13:05 +0000 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra In-Reply-To: References: Message-ID: This looks like an artifact to me. In addition, the zigzag in the Granger spectra is suggestive of convergence issues in the non-parametric spectral matrix factorization in at least a few of your subjects. This is often caused by strong discontinuities in the spectra (spectral lines!) or poor behaviour of the time domain data at the edges of your epochs, causing spectral leakage. JM > On 5 Nov 2018, at 17:21, Eelke Spaak wrote: > > Fellow FieldTrippers, > > I have identified two points of interest in the brain, between which I > want to compute connectivity metrics. One is in early visual cortex, > the other is in right temporal lobe, somewhat medial (compatible with > hippocampus, but exact interpretation not relevant right now). I > reconstructed activity for these grid points using LCMV beamformer (on > MEG data) and computed source-level Fourier spectra (taper = 'dpss', > tapsmofrq = 3) after applying a band-stop filter around 50 Hz and > harmonics. Using ft_connectivityanalysis, I computed both debiased > wPLI and Granger causality between the two points of interest. > > In both spectra, I see a clear peak at 60 Hz in the grand average > across 36 subjects, which is also there in the majority of individual > subjects (though very strong only in 2/36). Plots are attached. Now > this would be exciting news if indeed it turns out to be a true highly > band-limited gamma effect! > > But of course I suspect that this peak could very well be artifactual. > I can't think of any artifact source I may have missed that would > cause this, though. The projector refresh rate during the experiment > was set to 120 Hz, and not 60. (Also note this is European data so AC > frequency is 50 Hz and not 60, hence the band stop I mentioned > earlier.) > > Does anyone have any idea what might be causing this sharp peak? (A > genuine effect after all?) > > Thanks, > Eelke > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From silvana.silva at upf.edu Mon Nov 5 18:23:06 2018 From: silvana.silva at upf.edu (SILVA PEREIRA, SILVANA) Date: Mon, 5 Nov 2018 18:23:06 +0100 Subject: [FieldTrip] ICA inspection: Error using ft_icabrowser.m In-Reply-To: References: Message-ID: Hi Diego, Your suggestion works fine for ft_databrowser, but not for ft_icabrowser! best regards, Silvana El lun., 5 nov. 2018 a las 16:58, Diego Lozano-Soldevilla (< dlozanosoldevilla at gmail.com>) escribió: > Hi Silvana, > Could you try the component viewmode option? > > cfg = []; > cfg.layout = 'acticap-64ch-standard'; > cfg.viewmode = 'component'; % Mode specifically suited to browse through ICA data > ft_databrowser(cfg, comp); > > Best, > Diego > > > On Mon, Nov 5, 2018, 16:38 SILVA PEREIRA, SILVANA wrote: > >> Dear all, >> >> I'm trying to use the funtion ft_icabrowser.m, but I get the following >> error message: >> >> Error using topoplot_common (line 523) >> labels in data and labels in layout do not match >> >> Error in ft_topoplotIC (line 184) >> [cfg] = topoplot_common(cfg, comp); >> >> Error in ft_icabrowser (line 151) >> ft_topoplotIC(cfgtopo, comp); >> >> I understand that the error is due to a mismatch between the labels of >> the cfg struct and the layout I'm using. I modified the entries of the >> struct in acticap-64ch-standard2.mat, where I removed four of the channels, >> since we have in addition RM, LM, Heog and Veog. Adding these four labels >> to the original label list does not solve the problem. Any idea to work >> around this issue? >> >> Thank you! >> Silvana >> >> >> *Silvana Silva Pereira* >> /Postdoctoral Researcher/ >> Center for Brain and Cognition >> >> [image: Universitat Pompeu Fabra, Barcelona] >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From rdm146 at newark.rutgers.edu Mon Nov 5 18:26:35 2018 From: rdm146 at newark.rutgers.edu (Ravi Mill) Date: Mon, 5 Nov 2018 17:26:35 +0000 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> References: , <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> Message-ID: Hi Carsten and Johannes Many thanks for responding, and for developing these great tools! I'm in the process of acquiring a large diffusion MR dataset from which I can hopefully create an 'averaged' atlas. From your responses I think I have a sense of how to integrate the conductivity tensors derived from this atlas with the Fieldtrip FEM pipeline. But I was wondering if you had any advice on how to compute these conductivity tensors in the first place? From the paper that Carsten sent, It seems like the FDT program within FSL is what I need to compute diffusion tensors from the raw diffusion images (steps 1-6 from the FDT user guide https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Processing_pipeline)? Seemingly, these diffusion tensors need to then be converted to conductivity tensors - any advice on how to do this (or if you could point me to some example code) would be greatly appreciated. Thanks Ravi FDT/UserGuide - FslWiki - University of Oxford fsl.fmrib.ox.ac.uk Eddy Current Correction. Eddy currents in the gradient coils induce (approximate) stretches and shears in the diffusion weighted images. These distortions are different for different gradient directions. Thanks again Ravi ________________________________ From: fieldtrip on behalf of Johannes Vorwerk Sent: Monday, October 29, 2018 10:14:16 AM To: FieldTrip discussion list Subject: Re: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio Dear Ravi, as Carsten already said, calculating FEM with anisotropic conductivities is not directly supported by the FieldTrip-SimBio implementation. However, if you are willing to invest a bit of time it is possible to work around this. The „only“ thing that needs to be changed is the calculation of the FEM stiffness matrix, which is performed by the routine „calc_stiffness_matrix_val“ in the function sb_calc_stiff (usually called from ft_prepare_headmodel). The problem is that FieldTrip does not support anisotropic conductivities, so that you would have to call calc_stiffness_matrix_val directly. You can see the correct call in sb_calc_stiff. For anisotropic conductivities you have to replace the input „cond“ by a #elements x 6 matrix containing your anisotropic conductivities in the format "xx yy zz xy yz zx“. If you now follow the normal FieldTrip-SimBio workflow using the resulting stiffness matrix, you will get results for anisotropic conductivities. Best, Johannes Am 29.10.2018 um 12:31 schrieb Carsten Wolters >: Dear Ravi, 1) You can use the pure SimBio-code from https://www.mrt.uni-jena.de/simbio/index.php/Main_Page to treat WM anisotropy. While it would in principle also be possible to use anisotropic conductivities with FieldTrip-SimBio, this is currently not implemented using ft_prepare_headmodel. Johannes (in CC), who implemented Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: "Depending on your matlab skills and your available time, I could help you to give it a try though. It should be possible with using some direct function calls instead of the high-level fieldtrip-functions." 2) We recommend http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf on individual data. I could imagine that an atlas does a reasonable job w.r.t. the main bigger fiber tracts such as corpus callosum or pyramidal tracts, but that the finer details in the cortices are individual. We always measure T1, T2 and DTI from each subject and I personally do not have experience with such a group-level anisotropy compared to the individual one. Might be interesting to hear from others what they think!? BR Carsten Am 25.10.18 um 23:05 schrieb Ravi Mill: Dear Fieldtrippers I have applied the FEM simbio head modeling pipeline implemented in Fieldtrip to my EEG data. My understanding is that this pipeline assumes isotropic conductivities for 5 head compartments (as specified by cfg.conductivity in ft_prepare_headmodel). After reading some papers (e.g. Vorwerk et al 2014 https://doi.org/10.1016/j.neuroimage.2014.06.040), it seems like incorporating white matter conductivity anisotropy has a relatively small albeit significant effect on the source solution. I am interested in comparing FEM results when treating white matter as anisotropic. My questions are as follows: 1. Is there a way to implement the FEM simbio head model whilst treating WM as anisotropic within Fieldtrip? If so, how would one do this (or are there any resources available that demonstrate this)? 2. From previous papers and some simbio documentation (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples) it seems like diffusion MRI data is required to calculate the WM conductivity for each individual subject. I only have T1 and T2 scans for my subjects. So would it be possible to use WM anisotropic information obtained from some kind of diffusion MRI group average/atlas instead (accepting some loss in subject-level precision)? If so, does such a group average/atlas exist? Any help would be greatly appreciated! Thanks Ravi _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -- Prof. Dr.rer.nat. Carsten H. Wolters University of Münster Institute for Biomagnetism and Biosignalanalysis Malmedyweg 15 48149 Münster, Germany Phone: +49 (0)251 83 56904 +49 (0)251 83 56865 (secr.) Fax: +49 (0)251 83 56874 Email: carsten.wolters at uni-muenster.de Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From ekenaykut at gmail.com Mon Nov 5 18:41:35 2018 From: ekenaykut at gmail.com (Aykut Eken) Date: Mon, 5 Nov 2018 18:41:35 +0100 Subject: [FieldTrip] ICA inspection: Error using ft_icabrowser.m In-Reply-To: References: Message-ID: Hi Silvana, Can you check the match_str function? [seldat, sellay] = match_str(label, cfg.layout.label); if isempty(seldat) ft_error('labels in data and labels in layout do not match'); end Best Aykut > On 5 Nov 2018, at 18:23, SILVA PEREIRA, SILVANA wrote: > > > Hi Diego, > > Your suggestion works fine for ft_databrowser, but not for ft_icabrowser! > > best regards, > Silvana > > > El lun., 5 nov. 2018 a las 16:58, Diego Lozano-Soldevilla (>) escribió: > Hi Silvana, > Could you try the component viewmode option? > > cfg = []; > cfg.layout = 'acticap-64ch-standard'; > cfg.viewmode = 'component'; % Mode specifically suited to browse through ICA data > ft_databrowser(cfg, comp); > Best, > Diego > > > On Mon, Nov 5, 2018, 16:38 SILVA PEREIRA, SILVANA wrote: > Dear all, > > I'm trying to use the funtion ft_icabrowser.m, but I get the following error message: > > Error using topoplot_common (line 523) > labels in data and labels in layout do not match > > Error in ft_topoplotIC (line 184) > [cfg] = topoplot_common(cfg, comp); > > Error in ft_icabrowser (line 151) > ft_topoplotIC(cfgtopo, comp); > > I understand that the error is due to a mismatch between the labels of the cfg struct and the layout I'm using. I modified the entries of the struct in acticap-64ch-standard2.mat, where I removed four of the channels, since we have in addition RM, LM, Heog and Veog. Adding these four labels to the original label list does not solve the problem. Any idea to work around this issue? > > Thank you! > Silvana > > > Silvana Silva Pereira > /Postdoctoral Researcher/ > Center for Brain and Cognition > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From carsten.wolters at uni-muenster.de Tue Nov 6 12:10:36 2018 From: carsten.wolters at uni-muenster.de (Carsten Wolters) Date: Tue, 6 Nov 2018 12:10:36 +0100 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: References: <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> Message-ID: Hi Ravi, Marios (in CC) promised to send you the short Matlab-script that we use to transform the diffusion tensors to conductivity tensors using Tuch's effective medium approach. For this approach, please check e.g. the subsection "Calibrated Finite Element Head Model and Forward Solution" in https://link.springer.com/article/10.1007/s10548-017-0568-9 BR    Carsten Am 05.11.18 um 18:26 schrieb Ravi Mill: > > Hi Carsten and Johannes > > > Many thanks for responding, and for developing these great tools! > > > I'm in the process of acquiring a large diffusion MR dataset from > which I can hopefully create an 'averaged' atlas. From your > responses I think I have a sense of how to integrate the conductivity > tensors derived from this atlas with the Fieldtrip FEM pipeline. > > > But I was wondering if you had any advice on how to compute > these conductivity tensors in the first place? From the paper that > Carsten sent, It seems like the FDT program within FSL is what I need > to compute diffusion tensors from the raw diffusion images (steps 1-6 > from the FDT user guide > https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Processing_pipeline)? > Seemingly, these diffusion tensors need to then be converted to > conductivity tensors - any advice on how to do this (or if you could > point me to some example code) would be greatly appreciated. > > > Thanks > > Ravi > > > FDT/UserGuide - FslWiki - University of Oxford > > fsl.fmrib.ox.ac.uk > Eddy Current Correction. Eddy currents in the gradient coils induce > (approximate) stretches and shears in the diffusion weighted images. > These distortions are different for different gradient directions. > > > > Thanks again > Ravi > > > ------------------------------------------------------------------------ > *From:* fieldtrip on behalf of > Johannes Vorwerk > *Sent:* Monday, October 29, 2018 10:14:16 AM > *To:* FieldTrip discussion list > *Subject:* Re: [FieldTrip] Incorporating White Matter conductivity > anisotropy into FEM simbio > Dear Ravi, > > as Carsten already said, calculating FEM with anisotropic > conductivities is not directly supported by the FieldTrip-SimBio > implementation. However, if you are willing to invest a bit of time it > is possible to work around this. > > The „only“ thing that needs to be changed is the calculation of the > FEM stiffness matrix, which is performed by the routine > „calc_stiffness_matrix_val“ in the function sb_calc_stiff (usually > called from ft_prepare_headmodel). The problem is that FieldTrip does > not support anisotropic conductivities, so that you would have to call > calc_stiffness_matrix_val directly. You can see the correct call in > sb_calc_stiff. For anisotropic conductivities you have to replace the > input „cond“ by a #elements x 6 matrix containing your anisotropic > conductivities in the format "xx yy zz xy yz zx“. If you now follow > the normal FieldTrip-SimBio workflow using the resulting stiffness > matrix, you will get results for anisotropic conductivities. > > Best, > Johannes > >> Am 29.10.2018 um 12:31 schrieb Carsten Wolters >> > >: >> >> Dear Ravi, >> >> 1) You can use the pure SimBio-code from >> https://www.mrt.uni-jena.de/simbio/index.php/Main_Page >> >> to treat WM anisotropy. >> While it would in principle also be possible to use anisotropic >> conductivities with FieldTrip-SimBio, >> this is currently not implemented using ft_prepare_headmodel. >> Johannes (in CC), who implemented >> Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: >> "Depending on your matlab skills and your available time, I could >> help you to give it a >> try though. It should be possible with using some direct function >> calls instead of the high-level fieldtrip-functions." >> >> 2) We recommend >> http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf >> >> on individual data. I could imagine that an atlas does a reasonable >> job w.r.t. the main >> bigger fiber tracts such as corpus callosum or pyramidal tracts, but >> that the finer details >> in the cortices are individual. We always measure T1, T2 and DTI from >> each subject >> and I personally do not have experience with such a group-level >> anisotropy compared >> to the individual one. Might be interesting to hear from others what >> they think!? >> >> BR >>    Carsten >> >> >> >> Am 25.10.18 um 23:05 schrieb Ravi Mill: >>> Dear Fieldtrippers >>> >>> I have applied the FEM simbio head modeling pipeline implemented >>> in Fieldtrip to my EEG data. My understanding is that this pipeline >>> assumes isotropic conductivities for 5 head compartments (as >>> specified by cfg.conductivity in ft_prepare_headmodel). After >>> reading some papers (e.g. Vorwerk et al 2014 >>> https://doi.org/10.1016/j.neuroimage.2014.06.040 >>> ), >>> it seems like incorporating white matter conductivity anisotropy has >>> a relatively small albeit significant effect on the source solution. >>> I am interested in comparing FEM results when treating white matter >>> as anisotropic. My questions are as follows: >>> >>> 1. Is there a way to implement the FEM simbio head model whilst >>> treating WM as anisotropic within Fieldtrip? If so, how would >>> one do this (or are there any resources available that >>> demonstrate this)? >>> 2. From previous papers and some simbio documentation >>> (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples >>> ) >>> it seems like diffusion MRI data is required to calculate the WM >>> conductivity for each individual subject. I only have T1 and T2 >>> scans for my subjects. So would it be possible to use WM >>> anisotropic information obtained from some kind of diffusion >>> MRI group average/atlas instead (accepting some loss in >>> subject-level precision)? If so, does such a group average/atlas >>> exist? >>> >>> >>> Any help would be greatly appreciated! >>> >>> Thanks >>> Ravi >>> >>> >>> _______________________________________________ >>> fieldtrip mailing list >>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >>> https://doi.org/10.1371/journal.pcbi.1002202 >> >> >> -- >> Prof. Dr.rer.nat. Carsten H. Wolters >> University of Münster >> Institute for Biomagnetism and Biosignalanalysis >> Malmedyweg 15 >> 48149 Münster, Germany >> >> Phone: >> +49 (0)251 83 56904 >> +49 (0)251 83 56865 (secr.) >> >> Fax: >> +49 (0)251 83 56874 >> >> Email:carsten.wolters at uni-muenster.de >> Web:https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -- Prof. Dr.rer.nat. Carsten H. Wolters University of Münster Institute for Biomagnetism and Biosignalanalysis Malmedyweg 15 48149 Münster, Germany Phone: +49 (0)251 83 56904 +49 (0)251 83 56865 (secr.) Fax: +49 (0)251 83 56874 Email: carsten.wolters at uni-muenster.de Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From antoine.ducorps at orange.fr Tue Nov 6 12:44:17 2018 From: antoine.ducorps at orange.fr (Antoine Ducorps) Date: Tue, 6 Nov 2018 12:44:17 +0100 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger(fieldtrip Digest, Vol 96, Issue 4) In-Reply-To: References: Message-ID: <1D61E3DF-EEDF-473C-BF56-EA9A1E1772BC@orange.fr> Dear Eelke, I agree that a sharp 60 Hz peak is certainly an artefact. You mention that your projector is set to 120 Hz, but unless you use a very sophisticated one, the internal rate for most (if not all) commercial devices is still 60 Hz, and any input rate will be interpolated to that fundamental. In your case, every second frame would be displayed, or an average light value of two frames, I am not sure. You can check that on your technical manual, or alternating black/white frames and measuring the light output with a photodiode. If it really happens that your projector runs internally at 60 Hz, then you should also set your computer output at the same value, otherwise you don’t know what the interpolation or down sampling will do, most probably not documented by the vendor. Then, either you have a shielding problem with your projector electronics, or the 60 Hz rate is a real light effect in your stimulus, reflected in the visual cortex. Best, Antoine. > ------------------------------ > > Message: 3 > Date: Mon, 5 Nov 2018 17:21:05 +0100 > From: Eelke Spaak > To: FieldTrip discussion list > Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger > spectra > Message-ID: > > Content-Type: text/plain; charset="utf-8" > > Fellow FieldTrippers, > > I have identified two points of interest in the brain, between which I > want to compute connectivity metrics. One is in early visual cortex, > the other is in right temporal lobe, somewhat medial (compatible with > hippocampus, but exact interpretation not relevant right now). I > reconstructed activity for these grid points using LCMV beamformer (on > MEG data) and computed source-level Fourier spectra (taper = 'dpss', > tapsmofrq = 3) after applying a band-stop filter around 50 Hz and > harmonics. Using ft_connectivityanalysis, I computed both debiased > wPLI and Granger causality between the two points of interest. > > In both spectra, I see a clear peak at 60 Hz in the grand average > across 36 subjects, which is also there in the majority of individual > subjects (though very strong only in 2/36). Plots are attached. Now > this would be exciting news if indeed it turns out to be a true highly > band-limited gamma effect! > > But of course I suspect that this peak could very well be artifactual. > I can't think of any artifact source I may have missed that would > cause this, though. The projector refresh rate during the experiment > was set to 120 Hz, and not 60. (Also note this is European data so AC > frequency is 50 Hz and not 60, hence the band stop I mentioned > earlier.) > > Does anyone have any idea what might be causing this sharp peak? (A > genuine effect after all?) > > Thanks, > Eelke > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: granger-spectrum.png > Type: image/png > Size: 14990 bytes > Desc: not available > URL: > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: wpli-spectrum.png > Type: image/png > Size: 11765 bytes > Desc: not available > URL: > > ------------------------------ > > Message: 4 > Date: Mon, 5 Nov 2018 17:13:05 +0000 > From: "Schoffelen, J.M. (Jan Mathijs)" > To: FieldTrip discussion list > Subject: Re: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and > Granger spectra > Message-ID: > Content-Type: text/plain; charset="us-ascii" > > This looks like an artifact to me. > > In addition, the zigzag in the Granger spectra is suggestive of convergence issues in the non-parametric spectral matrix factorization in at least a few of your subjects. This is often caused by strong discontinuities in the spectra (spectral lines!) or poor behaviour of the time domain data at the edges of your epochs, causing spectral leakage. > > JM > > From antonakakismar at gmail.com Tue Nov 6 14:21:56 2018 From: antonakakismar at gmail.com (Marios Antonakakis) Date: Tue, 6 Nov 2018 14:21:56 +0100 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: References: <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> Message-ID: Hi Ravi, assuming that you work under Linux and you have already the DTI data registered to MRI, you need then to run the below script. *%%1, calculate conductivity tensor for every voxel* % mri_segmented_final, V1, ... L1 variables have ft MRI structure %skin,skull compacta, skull spongiosa, CSF, GM, WM conductivity = [0.4300, 0.0024, 0.0086, 1.7900, 0.3300, 0.1400]; cfg =[]; cfg.compartments = mri_segmented_final.anatomylabel; % anatomical labels, skin: 0, skull spongiosa 2 and show on... cfg.conductivity = conductivity; [condcell, s, fail] = sb_calcTensorCond_tuch(cfg,mri_segmented_final,V1,V2,V3,L1,L2,L3); *%% 2. assign the conductivity tensor with the hex mesh * [mesh.nodes,mesh.elements,mesh.labels]= read_vista_mesh('foo_mesh.v); mask = mri_segmented_final; mask.anatomy((mask.anatomy~=5) & (mask.anatomy~=6))=0; mask.anatomy((mask.anatomy==5) | (mask.anatomy==6))=1; tensors = sb_assiTensorCond(mask,mesh.nodes,mesh.elements,mesh.labels,condcell); write_vista_mesh('foo_mesh_aniso.v',mesh.nodes,mesh.elements,mesh.labels,tensors'); Do not hesitate to ask further questions. **Note that write_vista_mesh and read_vista_mesh are private ft functions.* Best regards, Marios ᐧ Στις Τρί, 6 Νοε 2018 στις 12:10 μ.μ., ο/η Carsten Wolters < carsten.wolters at uni-muenster.de> έγραψε: > Hi Ravi, > > Marios (in CC) promised to send you the short Matlab-script that we use to > transform the diffusion tensors > to conductivity tensors using Tuch's effective medium approach. > > For this approach, please check e.g. the subsection > "Calibrated Finite Element Head Model and Forward Solution" > in > https://link.springer.com/article/10.1007/s10548-017-0568-9 > > BR > Carsten > > Am 05.11.18 um 18:26 schrieb Ravi Mill: > > Hi Carsten and Johannes > > > Many thanks for responding, and for developing these great tools! > > > I'm in the process of acquiring a large diffusion MR dataset from which I > can hopefully create an 'averaged' atlas. From your responses I think I > have a sense of how to integrate the conductivity tensors derived from this > atlas with the Fieldtrip FEM pipeline. > > > But I was wondering if you had any advice on how to compute > these conductivity tensors in the first place? From the paper that Carsten > sent, It seems like the FDT program within FSL is what I need to compute > diffusion tensors from the raw diffusion images (steps 1-6 from the FDT > user guide > https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Processing_pipeline)? > Seemingly, these diffusion tensors need to then be converted to > conductivity tensors - any advice on how to do this (or if you could point > me to some example code) would be greatly appreciated. > > > Thanks > > Ravi > > > FDT/UserGuide - FslWiki - University of Oxford > > fsl.fmrib.ox.ac.uk > Eddy Current Correction. Eddy currents in the gradient coils induce > (approximate) stretches and shears in the diffusion weighted images. These > distortions are different for different gradient directions. > > > Thanks again > Ravi > > > ------------------------------ > *From:* fieldtrip > on behalf of Johannes Vorwerk > > *Sent:* Monday, October 29, 2018 10:14:16 AM > *To:* FieldTrip discussion list > *Subject:* Re: [FieldTrip] Incorporating White Matter conductivity > anisotropy into FEM simbio > > Dear Ravi, > > as Carsten already said, calculating FEM with anisotropic conductivities > is not directly supported by the FieldTrip-SimBio implementation. However, > if you are willing to invest a bit of time it is possible to work around > this. > > The „only“ thing that needs to be changed is the calculation of the FEM > stiffness matrix, which is performed by the routine > „calc_stiffness_matrix_val“ in the function sb_calc_stiff (usually called > from ft_prepare_headmodel). The problem is that FieldTrip does not support > anisotropic conductivities, so that you would have to call > calc_stiffness_matrix_val directly. You can see the correct call in > sb_calc_stiff. For anisotropic conductivities you have to replace the input > „cond“ by a #elements x 6 matrix containing your anisotropic conductivities > in the format "xx yy zz xy yz zx“. If you now follow the normal > FieldTrip-SimBio workflow using the resulting stiffness matrix, you will > get results for anisotropic conductivities. > > Best, > Johannes > > Am 29.10.2018 um 12:31 schrieb Carsten Wolters < > carsten.wolters at uni-muenster.de>: > > Dear Ravi, > > 1) You can use the pure SimBio-code from > https://www.mrt.uni-jena.de/simbio/index.php/Main_Page > > to treat WM anisotropy. > While it would in principle also be possible to use anisotropic > conductivities with FieldTrip-SimBio, > this is currently not implemented using ft_prepare_headmodel. Johannes (in > CC), who implemented > Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: > "Depending on your matlab skills and your available time, I could help you > to give it a > try though. It should be possible with using some direct function calls > instead of the high-level fieldtrip-functions." > > 2) We recommend > > http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf > > on individual data. I could imagine that an atlas does a reasonable job > w.r.t. the main > bigger fiber tracts such as corpus callosum or pyramidal tracts, but that > the finer details > in the cortices are individual. We always measure T1, T2 and DTI from each > subject > and I personally do not have experience with such a group-level anisotropy > compared > to the individual one. Might be interesting to hear from others what they > think!? > > BR > Carsten > > > > Am 25.10.18 um 23:05 schrieb Ravi Mill: > > Dear Fieldtrippers > > I have applied the FEM simbio head modeling pipeline implemented > in Fieldtrip to my EEG data. My understanding is that this pipeline > assumes isotropic conductivities for 5 head compartments (as specified by > cfg.conductivity in ft_prepare_headmodel). After reading some papers > (e.g. Vorwerk et al 2014 https://doi.org/10.1016/j.neuroimage.2014.06.040 > ), > it seems like incorporating white matter conductivity anisotropy has a > relatively small albeit significant effect on the source solution. I am > interested in comparing FEM results when treating white matter as > anisotropic. My questions are as follows: > > > 1. Is there a way to implement the FEM simbio head model whilst > treating WM as anisotropic within Fieldtrip? If so, how would one do this > (or are there any resources available that demonstrate this)? > 2. From previous papers and some simbio documentation ( > https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples > ) > it seems like diffusion MRI data is required to calculate the WM > conductivity for each individual subject. I only have T1 and T2 scans for > my subjects. So would it be possible to use WM anisotropic information > obtained from some kind of diffusion MRI group average/atlas instead > (accepting some loss in subject-level precision)? If so, does such a group > average/atlas exist? > > > Any help would be greatly appreciated! > > Thanks > Ravi > > > > _______________________________________________ > fieldtrip mailing listhttps://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 > > > > -- > Prof. Dr.rer.nat. Carsten H. Wolters > University of Münster > Institute for Biomagnetism and Biosignalanalysis > Malmedyweg 15 > 48149 Münster, Germany > > Phone: > +49 (0)251 83 56904 > +49 (0)251 83 56865 (secr.) > > Fax: > +49 (0)251 83 56874 > > Email: carsten.wolters at uni-muenster.de > Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ > > > > _______________________________________________ > fieldtrip mailing listhttps://mailman.science.ru.nl/mailman/listinfo/fieldtriphttps://doi.org/10.1371/journal.pcbi.1002202 > > > > -- > Prof. Dr.rer.nat. Carsten H. Wolters > University of Münster > Institute for Biomagnetism and Biosignalanalysis > Malmedyweg 15 > 48149 Münster, Germany > > Phone: > +49 (0)251 83 56904 > +49 (0)251 83 56865 (secr.) > > Fax: > +49 (0)251 83 56874 > > Email: carsten.wolters at uni-muenster.de > Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ > > -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- function condtensor = sb_assiTensorCond(mask,nodes,elem,labels,condcell) %% condtensor = zeros(9,size(elem,1)); count = 0; for i = 1 : size(elem) pos = nodes(elem(i,7),:); pos = round(pos); if (~(mask.anatomy(pos(1),pos(2),pos(3)) == 0)) if labels(i) ~= 5 && labels(i) ~= 6 labels(i) disp('not 5 or 6') end count = count+1; for j = 1 : 3 for k = 1 : 3 condtensor((j-1)*3 + k,i) = condcell{pos(1),pos(2),pos(3)}(j,k); end end end end count end -------------- next part -------------- function [condtensor, s, fail] = sb_calcTensorCond_tuch(cfg,mask,V1,V2,V3,L1,L2,L3) check1 = isequal(V1.dim,V2.dim,V3.dim); check2 = isequal(V1.dim,size(V1.anatomy),size(V2.anatomy),size(V3.anatomy)); check3 = isequal(L1.dim,L2.dim,L3.dim); check4 = isequal(L1.dim,size(L1.anatomy),size(L2.anatomy),size(L3.anatomy)); check5 = isequal(V1.dim(1:3),L1.dim,mask.dim); check6 = ~(isempty(cfg)||isempty(cfg.conductivity)); check7 = ~(length(cfg.conductivity)<6); if (check1 && check2 && check3 && check4 && check5 && check6 && check7) fail = 0; failnan = 0; condtensor = cell(mask.dim); N = zeros(1,3); vol = zeros(1,3); for i = 1 : V1.dim(1) for j = 1 : V1.dim(2) for k = 1 : V1.dim(3) if (((mask.anatomy(i,j,k) == 5)|(mask.anatomy(i,j,k) == 6))&(L1.anatomy(i,j,k)>10^-4)&(L2.anatomy(i,j,k)>10^-4)&(L3.anatomy(i,j,k)>10^-5)) S = zeros(3); S(:,1) = V1.anatomy(i,j,k,:); S(:,2) = V2.anatomy(i,j,k,:); S(:,3) = V3.anatomy(i,j,k,:); if(norm(S'*S-diag([1,1,1]),2)>10e-7) S(:,1) = S(:,1) / norm(S(:,1)); S(:,2) = S(:,2) - (S(:,1)'*S(:,2))*S(:,1); S(:,2) = S(:,2) / norm(S(:,2)); S(:,3) = S(:,3) - (S(:,1)'*S(:,3))*S(:,1) - (S(:,2)'*S(:,3))*S(:,2); S(:,3) = S(:,3) / norm(S(:,3)); failnan = failnan + 1; end if(sum(sum(isnan(S),1),2)>0) condtensor{i,j,k} = cfg.conductivity(mask.anatomy(i,j,k))*diag([1,1,1]); else D = diag([L1.anatomy(i,j,k),L2.anatomy(i,j,k),L3.anatomy(i,j,k)]); T = S * D * S'; %T = hier skalieren condtensor{i,j,k} = T; if (mask.anatomy(i,j,k) == 5) N(1) = N(1) + 1; vol(1) = vol(1) + L1.anatomy(i,j,k)*L2.anatomy(i,j,k)*L3.anatomy(i,j,k); elseif (mask.anatomy(i,j,k) == 6) N(2) = N(2) + 1; vol(2) = vol(2) + L1.anatomy(i,j,k)*L2.anatomy(i,j,k)*L3.anatomy(i,j,k); % elseif (mask.anatomy(i,j,k) == 3) % N(3) = N(3) + 1; % vol(3) = vol(3) + L1.anatomy(i,j,k)*L2.anatomy(i,j,k)*L3.anatomy(i,j,k); end end elseif(((mask.anatomy(i,j,k) == 5)|(mask.anatomy(i,j,k) == 6))) condtensor{i,j,k} = cfg.conductivity(mask.anatomy(i,j,k))*diag([1,1,1]); if(((mask.anatomy(i,j,k) == 5)|(mask.anatomy(i,j,k) == 6))&(~((L1.anatomy(i,j,k)>10^-4)&(L2.anatomy(i,j,k)>10^-4)&(L3.anatomy(i,j,k)>10^-5)))) fail = fail + 1; end else condtensor{i,j,k} = zeros(3); end end end end d(1) = vol(1) / N(1); d(1) = d(1)^(1/3); d(2) = vol(2) / N(2); d(2) = d(2)^(1/3); % d(3) = vol(3) / N(3); % d(3) = d(3)^(1/3); s = d(1)*cfg.conductivity(5)+d(2)*cfg.conductivity(6); s = s / (d(1)^2 + d(2)^2); fprintf('s*d = %.6f\n',s*(d(1)+d(2))) fprintf('s = %.6f\n',s) % failper = fail / (N(1)+N(2)); mx = -100000; mn = 100000; for i = 1 : V1.dim(1) for j = 1 : V1.dim(2) for k = 1 : V1.dim(3) if (((mask.anatomy(i,j,k) == 5)||(mask.anatomy(i,j,k) == 6))&(L2.anatomy(i,j,k)>10^-4)&(L3.anatomy(i,j,k)>10^-5)) condtensor{i,j,k} = s * condtensor{i,j,k}; %keep outliers wma bigger from the highest cond around cond of wm if max(condtensor{i,j,k}(:)) > cfg.conductivity(4) condtensor{i,j,k}(1:1+size(condtensor{i,j,k},1):end) = cfg.conductivity(6); end if mx < max(max(condtensor{i,j,k})) mx = max(max(condtensor{i,j,k})); fprintf('mx %d, %d, %d\n',i,j,k) end if mn > min(min(condtensor{i,j,k})) mn = min(min(condtensor{i,j,k})); fprintf('mn %d, %d, %d\n',i,j,k) end end end end end mn mx end end From silvana.silva at upf.edu Tue Nov 6 16:01:27 2018 From: silvana.silva at upf.edu (SILVA PEREIRA, SILVANA) Date: Tue, 6 Nov 2018 16:01:27 +0100 Subject: [FieldTrip] ICA inspection: Error using ft_icabrowser.m In-Reply-To: References: Message-ID: Ok, I finally found a solution, just modifying line 53 in ft_icabrowser from: lay = ft_prepare_layout(cfglay, comp); to: lay = ft_prepare_layout(cfglay); since the layout somehow gets modified if I add the ica data structure (comp) in the input parameters. Now it works! Best regards, Silvana El lun., 5 nov. 2018 a las 18:44, Aykut Eken () escribió: > Hi Silvana, > > Can you check the match_str function? > > [seldat, sellay] = match_str(label, cfg.layout.label); > if isempty(seldat) > ft_error('labels in data and labels in layout do not match'); > end > > Best > > Aykut > > On 5 Nov 2018, at 18:23, SILVA PEREIRA, SILVANA > wrote: > > > Hi Diego, > > Your suggestion works fine for ft_databrowser, but not for ft_icabrowser! > > best regards, > Silvana > > > El lun., 5 nov. 2018 a las 16:58, Diego Lozano-Soldevilla (< > dlozanosoldevilla at gmail.com>) escribió: > >> Hi Silvana, >> Could you try the component viewmode option? >> >> cfg = []; >> cfg.layout = 'acticap-64ch-standard'; >> cfg.viewmode = 'component'; % Mode specifically suited to browse through ICA data >> ft_databrowser(cfg, comp); >> >> Best, >> Diego >> >> >> On Mon, Nov 5, 2018, 16:38 SILVA PEREIRA, SILVANA > wrote: >> >>> Dear all, >>> >>> I'm trying to use the funtion ft_icabrowser.m, but I get the following >>> error message: >>> >>> Error using topoplot_common (line 523) >>> labels in data and labels in layout do not match >>> >>> Error in ft_topoplotIC (line 184) >>> [cfg] = topoplot_common(cfg, comp); >>> >>> Error in ft_icabrowser (line 151) >>> ft_topoplotIC(cfgtopo, comp); >>> >>> I understand that the error is due to a mismatch between the labels of >>> the cfg struct and the layout I'm using. I modified the entries of the >>> struct in acticap-64ch-standard2.mat, where I removed four of the channels, >>> since we have in addition RM, LM, Heog and Veog. Adding these four labels >>> to the original label list does not solve the problem. Any idea to work >>> around this issue? >>> >>> Thank you! >>> Silvana >>> >>> >>> *Silvana Silva Pereira* >>> /Postdoctoral Researcher/ >>> Center for Brain and Cognition >>> >>> [image: Universitat Pompeu Fabra, Barcelona] >>> _______________________________________________ >>> fieldtrip mailing list >>> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >>> https://doi.org/10.1371/journal.pcbi.1002202 >>> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From tineke.snijders at donders.ru.nl Wed Nov 7 13:28:46 2018 From: tineke.snijders at donders.ru.nl (Snijders, T.M. (Tineke)) Date: Wed, 7 Nov 2018 12:28:46 +0000 Subject: [FieldTrip] ft_rejectvisual reduces trial length Message-ID: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl> Hi, I am using trials of different lengths, which seems to give a problem in ft_rejectvisual. It wasn't a problem before (I think in 2016) when I did the same analysis. When using ft_rejectvisual, in the current fieldtrip version in the output data all trial lengths are reduced to the shortest trial-length, cutting away the remainder of the trial. In the command window it gives a warning of 'inconsistent sampling info'. Is there a workaround to still get the full trial-lengths for the artifact rejected data? I do want to keep the complete trials at this point. Thanks, Tineke -- Dr T.M. Snijders Research Staff Max Planck Institute for Psycholinguistics, Nijmegen www.ru.nl/people/donders/snijders-t http://www.mpi.nl/departments/language-development MPI | Wundtlaan 1 | Room 246 | tel 024 35 21246 | PO Box 310 | 6500 AH Nijmegen -------------- next part -------------- An HTML attachment was scrubbed... URL: From vincent.fontanier at inserm.fr Wed Nov 7 13:53:52 2018 From: vincent.fontanier at inserm.fr (vincent.fontanier at inserm.fr) Date: Wed, 07 Nov 2018 13:53:52 +0100 Subject: [FieldTrip] Spike-field analysis (combine freq and spike ; using ft_spiketriggeredspectrum) Message-ID: Hi everybody! I want to do some spike-field analysis on my dataset and have some questions regarding how fieldtrip handle such data and about the use of some of the functions related to this topic. 1. I have not found a fieldtrip way to combine freq structure (typically output from ft_freqanalysis) and spike structures. If I got it right, the fieldtrip pipeline to do spike-field is as follow: assuming filt_trials is the epoched LFP and spike is a fieldtrip spike structure · EPOCH the spike data like the LFP % spike to trials based on the epoched raw signal cfg = []; cfg.hdr = filt_trials.hdr; % contains information for conversion of samples to timestamps cfg.trlunit = 'samples'; cfg.trl = filt_trials.cfg.trl; % now in samples spikeTrials = ft_spike_maketrials(cfg,spike); · Compute the spike triggered spectrum cfg = []; cfg.method = 'mtmfft'; %'mtmconvol' is more powerful with many neurons and great firing rate cfg.foilim = [0 40]; % cfg.timwin determines spacing cfg.taper = 'hanning'; cfg.timwin = [-0.1 0.1]; %time around each spike stsConvol = ft_spiketriggeredspectrum(cfg, filt_trials, spikeTrials); · Make some analysis on the spike triggered spectrum cfg = []; cfg.method = 'ppc0'; % compute the Pairwise Phase Consistency cfg.avgoverchan = 'unweighted'; % weight spike-LFP phases irrespective of LFP power cfg.timwin = 'all'; % compute over all available spikes in the window cfg.latency = [-1 3]; statSts = ft_spiketriggeredspectrum_stat(cfg, stsConvol ); However having already performed time-frequency decomposition of all my LFP data I find this inefficient having to compute them again. Furthermore the methods of TF decomposition implemented in ft_spiketriggeredspectrum are much more limited than the one in ft_freqanalysis. So is there a way to combine the two together? A workaround is to realign the two together taking the sample of each spike in spikeTrials.timestamp{1}; and the start and end sample of each trial from the freq structure (freq.cfg.previous.trl. But this does not keep the fieldtrip way of formatting the data. Moreover this would require adjustments for further fieldtrip computations such as pairwise-phase consistency analysis using ft_spiketriggeredspectrum_stat. 2. (Useless if there is a solution to 1.) In ft_spiketriggeredspectrum you can provide a time window around each spike in the input to compute the spectrum. However the output spectrum is not time-resolved. Basically the output is just the average spectrum during the provided time window. Thus it is impossible to reconstruct the spike triggered time-frequency representation of the data. It is possible to run many iteration of ft_spiketriggeredspectrum for each timebin and store the output in a {chan}_spike_lfpchan_freq_time cell but it sounds like a very inefficient way to go. Therefore I am wondering if there is one way to do that more efficiently, for example an option that I missed in ft_spiketriggeredspectrum? Additionally could this time-resolved spiketriggeredspectrum output be used as an input to ft_spiketriggeredspectrum_stat in order to have a time-resolved output of the analysis? Many thanks! -- Vincent Fontanier Inserm U1208 (ex-U846) Stem Cell and Brain Research Institute Team Neurobiology of Executive Functions https://www.labex-cortex.com/en/team/neurobiology-executive-functions 18 av Du Doyen Lepine 69675 Bron CEDEX, (Lyon) FRANCE From tineke.snijders at donders.ru.nl Wed Nov 7 14:15:30 2018 From: tineke.snijders at donders.ru.nl (Snijders, T.M. (Tineke)) Date: Wed, 7 Nov 2018 13:15:30 +0000 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger(fieldtrip Digest, Vol 96, Issue 4) In-Reply-To: <1D61E3DF-EEDF-473C-BF56-EA9A1E1772BC@orange.fr> References: , <1D61E3DF-EEDF-473C-BF56-EA9A1E1772BC@orange.fr> Message-ID: <1541596532255.87599@donders.ru.nl> Hi Eelke, Yes I would also interpret this as artefact due to the projector (or: real visual entrainment to the projected visual stimuli). Your effect reminds me of a very clear 60 Hz response I had in my data, which appeared to be related to the refresh rate of the screen. We ran a few subjects with a refresh rate of 75 Hz and then the frequency mostly shifted to 75 Hz. See Snijders et al 2013, https://doi.org/10.1016/j.nicl.2013.06.015 Best, Tineke ________________________________________ From: fieldtrip on behalf of Antoine Ducorps Sent: Tuesday, November 6, 2018 12:44 PM To: fieldtrip at science.ru.nl Subject: Re: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger(fieldtrip Digest, Vol 96, Issue 4) Dear Eelke, I agree that a sharp 60 Hz peak is certainly an artefact. You mention that your projector is set to 120 Hz, but unless you use a very sophisticated one, the internal rate for most (if not all) commercial devices is still 60 Hz, and any input rate will be interpolated to that fundamental. In your case, every second frame would be displayed, or an average light value of two frames, I am not sure. You can check that on your technical manual, or alternating black/white frames and measuring the light output with a photodiode. If it really happens that your projector runs internally at 60 Hz, then you should also set your computer output at the same value, otherwise you don’t know what the interpolation or down sampling will do, most probably not documented by the vendor. Then, either you have a shielding problem with your projector electronics, or the 60 Hz rate is a real light effect in your stimulus, reflected in the visual cortex. Best, Antoine. > ------------------------------ > > Message: 3 > Date: Mon, 5 Nov 2018 17:21:05 +0100 > From: Eelke Spaak > To: FieldTrip discussion list > Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger > spectra > Message-ID: > > Content-Type: text/plain; charset="utf-8" > > Fellow FieldTrippers, > > I have identified two points of interest in the brain, between which I > want to compute connectivity metrics. One is in early visual cortex, > the other is in right temporal lobe, somewhat medial (compatible with > hippocampus, but exact interpretation not relevant right now). I > reconstructed activity for these grid points using LCMV beamformer (on > MEG data) and computed source-level Fourier spectra (taper = 'dpss', > tapsmofrq = 3) after applying a band-stop filter around 50 Hz and > harmonics. Using ft_connectivityanalysis, I computed both debiased > wPLI and Granger causality between the two points of interest. > > In both spectra, I see a clear peak at 60 Hz in the grand average > across 36 subjects, which is also there in the majority of individual > subjects (though very strong only in 2/36). Plots are attached. Now > this would be exciting news if indeed it turns out to be a true highly > band-limited gamma effect! > > But of course I suspect that this peak could very well be artifactual. > I can't think of any artifact source I may have missed that would > cause this, though. The projector refresh rate during the experiment > was set to 120 Hz, and not 60. (Also note this is European data so AC > frequency is 50 Hz and not 60, hence the band stop I mentioned > earlier.) > > Does anyone have any idea what might be causing this sharp peak? (A > genuine effect after all?) > > Thanks, > Eelke > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: granger-spectrum.png > Type: image/png > Size: 14990 bytes > Desc: not available > URL: > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: wpli-spectrum.png > Type: image/png > Size: 11765 bytes > Desc: not available > URL: > > ------------------------------ > > Message: 4 > Date: Mon, 5 Nov 2018 17:13:05 +0000 > From: "Schoffelen, J.M. (Jan Mathijs)" > To: FieldTrip discussion list > Subject: Re: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and > Granger spectra > Message-ID: > Content-Type: text/plain; charset="us-ascii" > > This looks like an artifact to me. > > In addition, the zigzag in the Granger spectra is suggestive of convergence issues in the non-parametric spectral matrix factorization in at least a few of your subjects. This is often caused by strong discontinuities in the spectra (spectral lines!) or poor behaviour of the time domain data at the edges of your epochs, causing spectral leakage. > > JM > > _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 From M.vanEs at donders.ru.nl Wed Nov 7 17:23:54 2018 From: M.vanEs at donders.ru.nl (Es, M.W.J. van (Mats)) Date: Wed, 7 Nov 2018 16:23:54 +0000 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra In-Reply-To: References: Message-ID: <3bda2dd4a5374570998b2f1d951a525d@EXPRD06.hosting.ru.nl> Hi Eelke, I actually had the exact same issue with one of my pilot datasets, recorded in February this year. It was recorded from the same MEG system in Nijmegen. I only found out a while later, so it wasn't possible to check if anything in the setup had changed. I assumed someone changed the refresh rate to 60 Hz and forgot to set it to the default 120 Hz afterwards, but I can't be certain. I do remember thinking whether it could be caused by the power shortages in Eastern Europe that were going on around that time, having heard that this affected internal circuitry of electrical devices. Not sure if it could cause this though. When were your data recorded? In the end I worked around this by putting a dft filter on 60 Hz, which worked reasonably well. Hope this mystery gets solved at some point! Cheers, Mats -----Original Message----- From: Eelke Spaak Sent: maandag 5 november 2018 17:21 To: FieldTrip discussion list Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra Fellow FieldTrippers, I have identified two points of interest in the brain, between which I want to compute connectivity metrics. One is in early visual cortex, the other is in right temporal lobe, somewhat medial (compatible with hippocampus, but exact interpretation not relevant right now). I reconstructed activity for these grid points using LCMV beamformer (on MEG data) and computed source-level Fourier spectra (taper = 'dpss', tapsmofrq = 3) after applying a band-stop filter around 50 Hz and harmonics. Using ft_connectivityanalysis, I computed both debiased wPLI and Granger causality between the two points of interest. In both spectra, I see a clear peak at 60 Hz in the grand average across 36 subjects, which is also there in the majority of individual subjects (though very strong only in 2/36). Plots are attached. Now this would be exciting news if indeed it turns out to be a true highly band-limited gamma effect! But of course I suspect that this peak could very well be artifactual. I can't think of any artifact source I may have missed that would cause this, though. The projector refresh rate during the experiment was set to 120 Hz, and not 60. (Also note this is European data so AC frequency is 50 Hz and not 60, hence the band stop I mentioned earlier.) Does anyone have any idea what might be causing this sharp peak? (A genuine effect after all?) Thanks, Eelke From maria.hakonen at gmail.com Thu Nov 8 08:35:20 2018 From: maria.hakonen at gmail.com (Maria Hakonen) Date: Thu, 8 Nov 2018 09:35:20 +0200 Subject: [FieldTrip] ft_mergealgin: high residual variance? In-Reply-To: References: <863F96C2-45E4-4441-B330-6C0292880D72@donders.ru.nl> Message-ID: Dear FieldTrip experts, Could the reason for the increased variance be that I have magnetometer data? However, the ft_megrealign and ft_prepare_headmodel don't have an option to specify whether I have magnetometers or gradiometers. Could you please let me know whether co-registration is needed before ft_megrealign (it is not needed with maxfilter)? Thank you already in advance! Best, Maria pe 2. marrask. 2018 klo 8.37 Maria Hakonen (maria.hakonen at gmail.com) kirjoitti: > Hi Jan-Mathias! > > Thank you for the answer! > I changed the units of gradiometers and head model to cm. > This clearly decreased the residual variances. > Also, singleshell seems to work better than localspheres. > > However, the transformation seems to still increase the residual variance > a lot. > > Here are some examples: > original -> template RV 407.64 % > original -> original RV 9.85 % > original -> template -> original RV 10.75 % > realigning trial 706 > original -> template RV 393.13 % > original -> original RV 9.00 % > original -> template -> original RV 9.90 % > realigning trial 707 > original -> template RV 362.10 % > original -> original RV 8.33 % > original -> template -> original RV 9.18 % > realigning trial 708 > original -> template RV 377.15 % > original -> original RV 9.43 % > original -> template -> original RV 10.33 % > > The code is now as follows: > load([data_path nameList{subj} '.mat']); > grad = datafinal.grad; > hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); > > cfg = []; > cfg.method = 'singlesphere'; > cfg.geom = hs; > cfg.grad = grad; > cfg.feedback = true; > vol = ft_prepare_headmodel(cfg); > > vol = ft_convert_units(vol,'cm'); > grad = ft_convert_units(grad,'cm'); > cfg = []; > cfg.template = template; > cfg.inwardshift = 2.5; > cfg.feedback ='no'; > cfg.vol = vol; > data = ft_megrealign(cfg, data); > > Best, > Maria > > to 1. marrask. 2018 klo 10.35 Schoffelen, J.M. (Jan Mathijs) ( > jan.schoffelen at donders.ru.nl) kirjoitti: > >> Hi Maria, >> >> Are you sure about the units in your headmodel (and gradiometers)? Using >> the value 1.0 as an inwardshift parameter suggests ‘cm’. Not sure what will >> happen when by accident the hs_file is in ‘m’ (which, as far as I know, is >> usually the case). >> >> Best wishes, >> >> Jan-Mathijs >> >> J.M.Schoffelen, MD PhD >> Senior Researcher, VIDI-fellow - PI, language in interaction >> Telephone: +31-24-3614793 >> Physical location: room 00.028 >> Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands >> >> >> >> >> On 29 Oct 2018, at 13:33, Maria Hakonen wrote: >> >> Dear FieldTrip experts, >> >> I have run ft_mergealign across subjects to align the head positions. >> However, the residual variance between the original and the realigned data >> seems to be high: >> >> original -> template RV 21232.46 % >> original -> original RV 36.96 % >> original -> template -> original RV 9579.95 % >> >> Could someone please let me know what would be the largest acceptable >> change in the residual variance, and what should I do if the residual >> variance is too high? Does the increase in residual variance mean that >> there is a large shift in the head position? >> >> I have used ft_mergealign as follows: >> >> template = list of subjects (i.e. I want to calculate an average head >> position over the subjects) >> >> grad = data.grad; >> hs=ft_read_headshape([hdr_path nameList{subj} '/hs_file']); >> vol = ft_headmodel_localspheres(hs,grad); >> >> cfg = []; >> cfg.template = template; >> cfg.inwardshift = 1.0; >> cfg.vol = vol; >> data_aligned = ft_megrealign(cfg, data); >> >> Best, >> Maria >> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> >> >> >> >> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 >> > -------------- next part -------------- An HTML attachment was scrubbed... URL: From e.spaak at donders.ru.nl Thu Nov 8 09:33:17 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Thu, 8 Nov 2018 09:33:17 +0100 Subject: [FieldTrip] ft_rejectvisual reduces trial length In-Reply-To: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl> References: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl> Message-ID: Hi Tineke, That sounds like a bug to me. You could consider filing it: https://github.com/fieldtrip/fieldtrip/issues Meanwhile, as a workaround, I usually ensure that one of the columns of my data.trialinfo contains unique trial identifiers (e.g. data.trialinfo(:,end+1) = 1:numel(data.trial);). Then, after datclean = ft_rejectvisual(cfg, data), the datclean.trialinfo(:,end) will contain those trial IDs that are kept. Store those IDs somewhere and then you can select the appropriate trials from the original data using ft_selectdata. Hope that helps, Eelke On Wed, 7 Nov 2018 at 13:28, Snijders, T.M. (Tineke) wrote: > > Hi, > > > > I am using trials of different lengths, which seems to give a problem in ft_rejectvisual. It wasn’t a problem before (I think in 2016) when I did the same analysis. > > > > When using ft_rejectvisual, in the current fieldtrip version in the output data all trial lengths are reduced to the shortest trial-length, cutting away the remainder of the trial. In the command window it gives a warning of ‘inconsistent sampling info’. > > > > Is there a workaround to still get the full trial-lengths for the artifact rejected data? I do want to keep the complete trials at this point. > > > > Thanks, > > > > Tineke > > > > -- > Dr T.M. Snijders > Research Staff > Max Planck Institute for Psycholinguistics, Nijmegen > > www.ru.nl/people/donders/snijders-t > > http://www.mpi.nl/departments/language-development > > MPI | Wundtlaan 1 | Room 246 | tel 024 35 21246 | PO Box 310 | 6500 AH Nijmegen > > > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From e.spaak at donders.ru.nl Thu Nov 8 09:51:51 2018 From: e.spaak at donders.ru.nl (Eelke Spaak) Date: Thu, 8 Nov 2018 09:51:51 +0100 Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra In-Reply-To: <3bda2dd4a5374570998b2f1d951a525d@EXPRD06.hosting.ru.nl> References: <3bda2dd4a5374570998b2f1d951a525d@EXPRD06.hosting.ru.nl> Message-ID: Thanks a lot JM, Lasha, Antoine, Tineke, & Mats for all the fast responses! Just to be clear: I agree that it's extremely unlikely that the peak reflects an endogenous rhythm. @Antoine: the projector is supposed to be very high-end (vPixx Propixx) and I've used it in the past at 1440 Hz, which worked well. So I would assume it's not a matter of mismatch between the GPU refresh rate and the projector's. But this is worth checking with a photodiode. Also I guess it's possible that I forgot to check the computer's refresh rate on some of the recording sessions and that it was actually 60 Hz instead of 120. For now I think I will deal with this with a notch filter. @Mats: the data were recorded between April and June of this year (2018). Cheers, Eelke On Wed, 7 Nov 2018 at 17:23, Es, M.W.J. van (Mats) wrote: > > Hi Eelke, > > I actually had the exact same issue with one of my pilot datasets, recorded in February this year. It was recorded from the same MEG system in Nijmegen. I only found out a while later, so it wasn't possible to check if anything in the setup had changed. I assumed someone changed the refresh rate to 60 Hz and forgot to set it to the default 120 Hz afterwards, but I can't be certain. I do remember thinking whether it could be caused by the power shortages in Eastern Europe that were going on around that time, having heard that this affected internal circuitry of electrical devices. Not sure if it could cause this though. When were your data recorded? > In the end I worked around this by putting a dft filter on 60 Hz, which worked reasonably well. > > Hope this mystery gets solved at some point! > Cheers, > Mats > > -----Original Message----- > From: Eelke Spaak > Sent: maandag 5 november 2018 17:21 > To: FieldTrip discussion list > Subject: [FieldTrip] Sharp 60 Hz peak (in Europe!) in wPLI and Granger spectra > > Fellow FieldTrippers, > > I have identified two points of interest in the brain, between which I want to compute connectivity metrics. One is in early visual cortex, the other is in right temporal lobe, somewhat medial (compatible with hippocampus, but exact interpretation not relevant right now). I reconstructed activity for these grid points using LCMV beamformer (on MEG data) and computed source-level Fourier spectra (taper = 'dpss', tapsmofrq = 3) after applying a band-stop filter around 50 Hz and harmonics. Using ft_connectivityanalysis, I computed both debiased wPLI and Granger causality between the two points of interest. > > In both spectra, I see a clear peak at 60 Hz in the grand average across 36 subjects, which is also there in the majority of individual subjects (though very strong only in 2/36). Plots are attached. Now this would be exciting news if indeed it turns out to be a true highly band-limited gamma effect! > > But of course I suspect that this peak could very well be artifactual. > I can't think of any artifact source I may have missed that would cause this, though. The projector refresh rate during the experiment was set to 120 Hz, and not 60. (Also note this is European data so AC frequency is 50 Hz and not 60, hence the band stop I mentioned > earlier.) > > Does anyone have any idea what might be causing this sharp peak? (A genuine effect after all?) > > Thanks, > Eelke > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 From Silvia.Formica at UGent.be Thu Nov 8 11:40:06 2018 From: Silvia.Formica at UGent.be (Silvia Formica) Date: Thu, 8 Nov 2018 10:40:06 +0000 Subject: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage In-Reply-To: References: <1541070268890.93169@donders.ru.nl>, Message-ID: <1541673605780.63773@UGent.be> Dear Cecilia and Tom, I am in a similar situation to the one described by Cecilia, so I thought of asking you for a suggestion. I have a dataset locked to the onset of the cue, and I would like to use the same baseline I used in this dataset to baseline-correct also the same dataset locked to the onset of the target. I tried the solution Tom suggested, but it is not working for me. The reason is that I preprocessed the cue-locked and target-locked datasets separately, therefore they end up having a slightly different number of trials. Do you have any idea of how this could be solved? Would it make sense to baseline-correct at the grandaverage level? Another option I have been thinking about is to use ft_redefinetrial. In this case I have other problems, though. If I try to use the ft_redefinetrial function after preprocessing and cleaning my cue-locked dataset, it will output all the trials in the raw data (therefore not accounting for the trial rejection I performed on the cue-locked dataset). Is this the right way to use this function or am I missing something? Thanks in advance for any input and sorry if my question is not very clear (still a newbie!) Best, Silvia ________________________________ From: fieldtrip on behalf of Tom Marshall Sent: 02 November 2018 23:29 To: FieldTrip discussion list Subject: Re: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage Hey Cecilia, I guess you could do that with a combination of 'ft_selectdata', and 'ft_math'. Let's say you have datasets called 'stimlocked' and 'cuelocked' You'd do something like: % cut out baseline from cuelocked cfg = []; cfg.latency = [-0.5 0]; % or whatever your baseline window is baseline = ft_selectdata(cfg, cuelocked); % subtract baseline from stimlocked cfg = []; cfg.operation = 'subtract'; baseline_corrected_stimlocked = ft_math(cfg, stimlocked, baseline); I think that ft_math allows you to input algebraic expressions too. So if you wanted to do, for example, a 'relchange' baseline correction you should substitute (something like) cfg.operation = '(x1-x2)/x2'; Best, Tom ________________________________ From: fieldtrip on behalf of Mazzetti, C. (Cecilia) Sent: 01 November 2018 11:04:23 To: fieldtrip at science.ru.nl Subject: [FieldTrip] baseline correcting data with baseline coming from a different grandaverage Dear everyone, I have some stimulus locked data which I have to baseline with respect to the same data but cue locked. I was wondering whether there is a setting in fieldtrip which allows to do that (i.e. set the baseline timw window according to a different dataset, the cue-locked data) and applying it to the stimulus locked data. Thanks in advance! Best, Cecilia Cecilia Mazzetti - Ph.D. Candidate Donders Centre for Cognitive Neuroimaging, room 0.068 Kapittelweg 29 | 6525 EN Nijmegen -------------- next part -------------- An HTML attachment was scrubbed... URL: From afsamani at hst.aau.dk Thu Nov 8 12:29:29 2018 From: afsamani at hst.aau.dk (Afshin Samani) Date: Thu, 8 Nov 2018 11:29:29 +0000 Subject: [FieldTrip] explore cluster statistics Message-ID: <2E4AC5E2A691FE4B89DA44B0E70DF70A014154A5E6@AD-EXCHMBX3-3.aau.dk> Hi, I have never used fieldtrip before but I was trying to get familiar with its statistical analysis tools. I tried to run the online example from: http://www.fieldtriptoolbox.org/example/use_simulated_erps_to_explore_cluster_statistics I am using MATLAB Version: 9.4.0.813654 (R2018a) and I downloaded that latest version of fieldtrip on 5 nov 2018 I face two errors: first at ft_math function: Error using ft_math (line 151) the requested parameter is not present in the data Error in Nonparametric_statistical_testingEEGandMEG_data (line 45) difference = ft_math(cfg, timelock1, timelock2); % line 137 of ft_datatype_timelock removes the fields and if I comment that line out, I face another error at ft_timelcokstatistics Error using chol Matrix must be square. Error in montecarlo (line 13) R = chol(Sigma); % Y = R^{-T}X has covariance I Error in ft_timelockstatistics (line 185) [stat] = statmethod(cfg, dat, design); Error in Nonparametric_statistical_testingEEGandMEG_data (line 63) stat = ft_timelockstatistics(cfg, timelock1, timelock2); Any comment? Best regards, Associate Prof. Afshin Samani, PhD Sport Sciences Dept. of Health Science and Technology Aalborg University, Aalborg Denmark [Description: Beskrivelse: AAU_LINE_blue_rgb] -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image001.png Type: image/png Size: 4637 bytes Desc: image001.png URL: From hesham.elshafei at inserm.fr Thu Nov 8 16:40:42 2018 From: hesham.elshafei at inserm.fr (Hesham ElShafei) Date: Thu, 08 Nov 2018 16:40:42 +0100 Subject: [FieldTrip] Software Developer Opportunity in Lyon ! Message-ID: Hello fieldtrippers !! Our team is looking for a software developer to participate in the development of signal processing and visualization (topographies, time-frequency plots) tools for EEG and MEG signals. This project is in continuity with the ELAN software that has been developed and updated in our laboratory for more than 20 years (https://www.ncbi.nlm.nih.gov/pubmed/21687568) and will be in close collaboration with the MNE-Python development team (https://martinos.org/mne/stable/index.html) You will be responsible for developing a new graphic interface to the already available visualization tools. Regular interactions with team members will be organized to better sense the future users' needs. Moreover, different data sets (EEG, MEG) corresponding to various experimental conditions will be available to test the developed tools. You should have a master's degree computer science. Experience in (C/C++, Python programming) and knowledge of (Qt and signal processing). Knowledge of human electrophysiology (EEG/MEG) would be a plus. You should have strong organizational skills, be able to work independently, and have excellent interpersonal communication skills. You should be able to work in a dynamic, collaborative and international environment. Intended starting data is January 1st, 2019. Initial contract will be for 12 months with possibility of an extension According to education level and work experience salary will range between 1800 and 2500 euros net/month. For more information please contact : Pierre-Emmanuel Aguera : pe.aguera at inserm.fr Aurélie Bidet-Caulet : aurelie.bidet-caulet at inserm.fr Anne Caclin : anne.caclin at inserm.fr Or visit our website(s) ! https://crnl.univ-lyon1.fr/index.php/fr http://dycog.lyon.inserm.fr/ Cheers Hesham ps. maybe I'm not smart enough to figure out how to reply to answers to my questions , but thanks a lot for answering them !! :) -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Fri Nov 9 11:18:26 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Fri, 9 Nov 2018 10:18:26 +0000 Subject: [FieldTrip] ft_rejectvisual reduces trial length In-Reply-To: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl> References: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl> Message-ID: <17306DD6-842B-4153-A0D1-CFA9DA13CD90@donders.ru.nl> Hi Tineke, This is indeed inappropriate behaviour of the function, which has been tracked down to something going wrong in ft_selectdata. I have submitted a PR https://github.com/fieldtrip/fieldtrip/pull/873 on the github-repo which fixes the issue https://github.com/fieldtrip/fieldtrip/issues/871 I am pretty sure that this fix does not come with unwanted side effects, but since ft_selectdata is used almost everywhere I’d appreciate some moral support before merging. Let me know what you think. Jan-Mathijs PS: for those who are around: please do join Tineke and me this coming Sunday Nov 11, when we will commemorate the centenary of the 1918 armistice with a choral concert at the church of St. Anthony of Padua in Nijmegen. It starts at 15.30, see http://www.kamerkoormnemosyne.nl J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands On 7 Nov 2018, at 13:28, Snijders, T.M. (Tineke) > wrote: Hi, I am using trials of different lengths, which seems to give a problem in ft_rejectvisual. It wasn’t a problem before (I think in 2016) when I did the same analysis. When using ft_rejectvisual, in the current fieldtrip version in the output data all trial lengths are reduced to the shortest trial-length, cutting away the remainder of the trial. In the command window it gives a warning of ‘inconsistent sampling info’. Is there a workaround to still get the full trial-lengths for the artifact rejected data? I do want to keep the complete trials at this point. Thanks, Tineke -- Dr T.M. Snijders Research Staff Max Planck Institute for Psycholinguistics, Nijmegen www.ru.nl/people/donders/snijders-t http://www.mpi.nl/departments/language-development MPI | Wundtlaan 1 | Room 246 | tel 024 35 21246 | PO Box 310 | 6500 AH Nijmegen _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Fri Nov 9 15:05:33 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Fri, 9 Nov 2018 14:05:33 +0000 Subject: [FieldTrip] explore cluster statistics In-Reply-To: <2E4AC5E2A691FE4B89DA44B0E70DF70A014154A5E6@AD-EXCHMBX3-3.aau.dk> References: <2E4AC5E2A691FE4B89DA44B0E70DF70A014154A5E6@AD-EXCHMBX3-3.aau.dk> Message-ID: <2263141F-A069-45C9-8FDA-0C7D24149CCC@donders.ru.nl> Afshin, I face two errors: first at ft_math function: Error using ft_math (line 151) the requested parameter is not present in the data As the error indicates, the requested parameter ‘avg’ is apparently not in the data. In other words, the timelock1/2 structures shouldn't have an ‘avg’ field, causing ft_math to complain. The reason for this is that the example script pre-dates some changes to the fieldtrip code base, which caused the ‘avg’ field to disappear if ft_timelockanalysis is called with cfg.keeptrials = ‘yes’. Probably, if you use cfg.parameter = ‘trial’, rather than ‘avg’ in your call to ft_math, it’ll work. Error in Nonparametric_statistical_testingEEGandMEG_data (line 45) difference = ft_math(cfg, timelock1, timelock2); % line 137 of ft_datatype_timelock removes the fields and if I comment that line out, I face another error at ft_timelcokstatistics Error using chol Matrix must be square. Error in montecarlo (line 13) R = chol(Sigma); % Y = R^{-T}X has covariance I For some reason, you end up in a function called ‘montecarlo’, which is incorrect, since you should have ended up in ft_statistics_montecarlo. The cause of all this is probably that the folder that has the ‘montecarlo’ function is higher on the matlab search path than the fieldtrip folder. Jan-Mathijs J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands Error in ft_timelockstatistics (line 185) [stat] = statmethod(cfg, dat, design); Error in Nonparametric_statistical_testingEEGandMEG_data (line 63) stat = ft_timelockstatistics(cfg, timelock1, timelock2); Any comment? Best regards, Associate Prof. Afshin Samani, PhD Sport Sciences Dept. of Health Science and Technology Aalborg University, Aalborg Denmark _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From twater14 at student.aau.dk Sun Nov 11 21:40:19 2018 From: twater14 at student.aau.dk (Toby Steven Waterstone) Date: Sun, 11 Nov 2018 20:40:19 +0000 Subject: [FieldTrip] Comparing spatio-temporal data with FieldTrip toolbox Message-ID: <63EFD5634C86B743A236791097CE83866D363D17@AD-EXCHMBX3-2.aau.dk> Hi, I am analyzing high density surface electromyography (HD-sEMG) data, where I am calculating the similarities between each channel in the sensor grid. This is done in two conditions to see if there are any effect from a specific intervention, from 12 subjects, where pre and post recordings has been performed for both a control and intervention group. For the similarity measure I have calculated normalised mutual information (NMI), where I have two different outcome measures from my calculations. NMI heatmaps (12x5 matrices) representing the aggregated NMI over the HD-sEMG sensor grid, and connectivity maps (60x60 matrices) representing the similarity between each electrode pair. I have attached examples of the data, so you get the idea. I am trying to find a way to compare this data, which has a spatio-temporal pattern. The FieldTrip toolbox has a lot of functions to analyse EEG/MEG data and as my data (HD-sEMG) has many of the same characteristics as EEG data, this toolbox might be useful to analyse my data. But in my case, I have already analysed the EMG signals by calculating NMI, and only need to do the statistical comparison now. FieldTrip is provided with a couple of statistical functions, but I'm unsure about how to use these on my data and if it is possible with this toolbox. The approach I'm looking into is FieldTrip cluster-based permutation tests, but it cannot directly be transferred to my case, because I am comparing the already analysed data. So my data kind of already have the data structure of the output from the analysis of this tutorial (the plots). So my question is: How can I use the statistical functions of the FieldTrip toolbox for comparison of NMI (spatio-temporal data)? If this is possible? I want to compare the heatmaps (12x5 vs 12x5 data structures) to each other and connectivity maps (60x60 vs 60x60 data structures) to each other. Thank you Best regards Toby S. Waterstone -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Heatmap.png Type: image/png Size: 162770 bytes Desc: Heatmap.png URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Connectivity_map.png Type: image/png Size: 306140 bytes Desc: Connectivity_map.png URL: From rdm146 at newark.rutgers.edu Mon Nov 12 17:36:29 2018 From: rdm146 at newark.rutgers.edu (Ravi Mill) Date: Mon, 12 Nov 2018 16:36:29 +0000 Subject: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio In-Reply-To: References: <0C93B118-2E7D-4366-9C93-4EB478730C7D@gmail.com> , Message-ID: Many thanks Marios and Carsten - I will try out the scripts you sent me and let you know if I have any issues. Best wishes Ravi ________________________________ From: Marios Antonakakis Sent: Tuesday, November 6, 2018 8:21:56 AM To: Carsten Wolters Cc: fieldtrip at science.ru.nl; Ravi Mill Subject: Re: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio Hi Ravi, assuming that you work under Linux and you have already the DTI data registered to MRI, you need then to run the below script. %%1, calculate conductivity tensor for every voxel % mri_segmented_final, V1, ... L1 variables have ft MRI structure %skin,skull compacta, skull spongiosa, CSF, GM, WM conductivity = [0.4300, 0.0024, 0.0086, 1.7900, 0.3300, 0.1400]; cfg =[]; cfg.compartments = mri_segmented_final.anatomylabel; % anatomical labels, skin: 0, skull spongiosa 2 and show on... cfg.conductivity = conductivity; [condcell, s, fail] = sb_calcTensorCond_tuch(cfg,mri_segmented_final,V1,V2,V3,L1,L2,L3); %% 2. assign the conductivity tensor with the hex mesh [mesh.nodes,mesh.elements,mesh.labels]= read_vista_mesh('foo_mesh.v); mask = mri_segmented_final; mask.anatomy((mask.anatomy~=5) & (mask.anatomy~=6))=0; mask.anatomy((mask.anatomy==5) | (mask.anatomy==6))=1; tensors = sb_assiTensorCond(mask,mesh.nodes,mesh.elements,mesh.labels,condcell); write_vista_mesh('foo_mesh_aniso.v',mesh.nodes,mesh.elements,mesh.labels,tensors'); Do not hesitate to ask further questions. *Note that write_vista_mesh and read_vista_mesh are private ft functions. Best regards, Marios [https://mailfoogae.appspot.com/t?sender=aYW50b25ha2FraXNtYXJAZ21haWwuY29t&type=zerocontent&guid=420ef8db-499e-48d0-b63b-37a4d6551822]ᐧ Στις Τρί, 6 Νοε 2018 στις 12:10 μ.μ., ο/η Carsten Wolters > έγραψε: Hi Ravi, Marios (in CC) promised to send you the short Matlab-script that we use to transform the diffusion tensors to conductivity tensors using Tuch's effective medium approach. For this approach, please check e.g. the subsection "Calibrated Finite Element Head Model and Forward Solution" in https://link.springer.com/article/10.1007/s10548-017-0568-9 BR Carsten Am 05.11.18 um 18:26 schrieb Ravi Mill: Hi Carsten and Johannes Many thanks for responding, and for developing these great tools! I'm in the process of acquiring a large diffusion MR dataset from which I can hopefully create an 'averaged' atlas. From your responses I think I have a sense of how to integrate the conductivity tensors derived from this atlas with the Fieldtrip FEM pipeline. But I was wondering if you had any advice on how to compute these conductivity tensors in the first place? From the paper that Carsten sent, It seems like the FDT program within FSL is what I need to compute diffusion tensors from the raw diffusion images (steps 1-6 from the FDT user guide https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide#Processing_pipeline)? Seemingly, these diffusion tensors need to then be converted to conductivity tensors - any advice on how to do this (or if you could point me to some example code) would be greatly appreciated. Thanks Ravi FDT/UserGuide - FslWiki - University of Oxford fsl.fmrib.ox.ac.uk Eddy Current Correction. Eddy currents in the gradient coils induce (approximate) stretches and shears in the diffusion weighted images. These distortions are different for different gradient directions. Thanks again Ravi ________________________________ From: fieldtrip on behalf of Johannes Vorwerk Sent: Monday, October 29, 2018 10:14:16 AM To: FieldTrip discussion list Subject: Re: [FieldTrip] Incorporating White Matter conductivity anisotropy into FEM simbio Dear Ravi, as Carsten already said, calculating FEM with anisotropic conductivities is not directly supported by the FieldTrip-SimBio implementation. However, if you are willing to invest a bit of time it is possible to work around this. The „only“ thing that needs to be changed is the calculation of the FEM stiffness matrix, which is performed by the routine „calc_stiffness_matrix_val“ in the function sb_calc_stiff (usually called from ft_prepare_headmodel). The problem is that FieldTrip does not support anisotropic conductivities, so that you would have to call calc_stiffness_matrix_val directly. You can see the correct call in sb_calc_stiff. For anisotropic conductivities you have to replace the input „cond“ by a #elements x 6 matrix containing your anisotropic conductivities in the format "xx yy zz xy yz zx“. If you now follow the normal FieldTrip-SimBio workflow using the resulting stiffness matrix, you will get results for anisotropic conductivities. Best, Johannes Am 29.10.2018 um 12:31 schrieb Carsten Wolters >: Dear Ravi, 1) You can use the pure SimBio-code from https://www.mrt.uni-jena.de/simbio/index.php/Main_Page to treat WM anisotropy. While it would in principle also be possible to use anisotropic conductivities with FieldTrip-SimBio, this is currently not implemented using ft_prepare_headmodel. Johannes (in CC), who implemented Fieldtrip-SimBio, answered a same question by Junjie Wu in March 2018: "Depending on your matlab skills and your available time, I could help you to give it a try though. It should be possible with using some direct function calls instead of the high-level fieldtrip-functions." 2) We recommend http://www.sci.utah.edu/~wolters/PaperWolters/2012/RuthottoEtAl_PhysMedBiol_2012.pdf on individual data. I could imagine that an atlas does a reasonable job w.r.t. the main bigger fiber tracts such as corpus callosum or pyramidal tracts, but that the finer details in the cortices are individual. We always measure T1, T2 and DTI from each subject and I personally do not have experience with such a group-level anisotropy compared to the individual one. Might be interesting to hear from others what they think!? BR Carsten Am 25.10.18 um 23:05 schrieb Ravi Mill: Dear Fieldtrippers I have applied the FEM simbio head modeling pipeline implemented in Fieldtrip to my EEG data. My understanding is that this pipeline assumes isotropic conductivities for 5 head compartments (as specified by cfg.conductivity in ft_prepare_headmodel). After reading some papers (e.g. Vorwerk et al 2014 https://doi.org/10.1016/j.neuroimage.2014.06.040), it seems like incorporating white matter conductivity anisotropy has a relatively small albeit significant effect on the source solution. I am interested in comparing FEM results when treating white matter as anisotropic. My questions are as follows: 1. Is there a way to implement the FEM simbio head model whilst treating WM as anisotropic within Fieldtrip? If so, how would one do this (or are there any resources available that demonstrate this)? 2. From previous papers and some simbio documentation (https://www.mrt.uni-jena.de/simbio/index.php/SIMBIO/Releasenotes/Examples) it seems like diffusion MRI data is required to calculate the WM conductivity for each individual subject. I only have T1 and T2 scans for my subjects. So would it be possible to use WM anisotropic information obtained from some kind of diffusion MRI group average/atlas instead (accepting some loss in subject-level precision)? If so, does such a group average/atlas exist? Any help would be greatly appreciated! Thanks Ravi _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -- Prof. Dr.rer.nat. Carsten H. Wolters University of Münster Institute for Biomagnetism and Biosignalanalysis Malmedyweg 15 48149 Münster, Germany Phone: +49 (0)251 83 56904 +49 (0)251 83 56865 (secr.) Fax: +49 (0)251 83 56874 Email: carsten.wolters at uni-muenster.de Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -- Prof. Dr.rer.nat. Carsten H. Wolters University of Münster Institute for Biomagnetism and Biosignalanalysis Malmedyweg 15 48149 Münster, Germany Phone: +49 (0)251 83 56904 +49 (0)251 83 56865 (secr.) Fax: +49 (0)251 83 56874 Email: carsten.wolters at uni-muenster.de Web: https://campus.uni-muenster.de/biomag/das-institut/mitarbeiter/carsten-wolters/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Wed Nov 14 12:28:37 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Wed, 14 Nov 2018 11:28:37 +0000 Subject: [FieldTrip] Comparing spatio-temporal data with FieldTrip toolbox In-Reply-To: <63EFD5634C86B743A236791097CE83866D363D17@AD-EXCHMBX3-2.aau.dk> References: <63EFD5634C86B743A236791097CE83866D363D17@AD-EXCHMBX3-2.aau.dk> Message-ID: <230E33B4-CF3E-448C-A6C9-AB2D94688863@donders.ru.nl> Dear Toby, I think that this is not altogether too difficult. What you would need to do is to put your numeric data into a structure that FieldTrip can work with. Specifically, if you manage to create a so-called ’timelock’ or ‘freq’ representation of your data, you can use ft_timelockstatistics or ft_freqstatistics for the statistical inference. (as a side note, I think it’s up to you to think whether it makes sense to use the spatial clustering heuristic for family-wise error control when comparing the connectivity matrices; yet, you can still do a permutation test to test the null hypothesis of exchangeability between groups). Long story short for the 12x5 NMI data, I’d create 2 data structures, let’s call them freq1 (intervention group) and freq2 (controls), with the following fields freq1.label = {‘thenameofthisisnotrelevant’}; freq1.freq = 1:12 freq1.time = 1:5 freq1.dimord = ‘rpt_chan_freq_time’; freq1.powspctrm = zeros(number-of-subjects, 1, 12, 5); for i = 1:nsubj freq1.powspctrm(i,1,:,:) = nmi; % this should yield a 1 x 12 x 5 matrix end and the same thing for freq2. Then you can use ft_freqstatistics for statistical inference, with optional clustering for multiple comparison correction. In this case, the clustering will take place across the ‘freq’, and ‘time’ dimensions, which in your case boils down to spatial clustering across adjacent electrodes in the x and y directions, respectively. For the connectivity matrices, I’d convert the single subject matrices into a vector (using the lower triangular part only), but I’d say: first things first. Best wishes, Jan-Mathijs On 11 Nov 2018, at 21:40, Toby Steven Waterstone > wrote: Hi, I am analyzing high density surface electromyography (HD-sEMG) data, where I am calculating the similarities between each channel in the sensor grid. This is done in two conditions to see if there are any effect from a specific intervention, from 12 subjects, where pre and post recordings has been performed for both a control and intervention group. For the similarity measure I have calculated normalised mutual information (NMI), where I have two different outcome measures from my calculations. NMI heatmaps (12x5 matrices) representing the aggregated NMI over the HD-sEMG sensor grid, and connectivity maps (60x60 matrices) representing the similarity between each electrode pair. I have attached examples of the data, so you get the idea. I am trying to find a way to compare this data, which has a spatio-temporal pattern. The FieldTrip toolbox has a lot of functions to analyse EEG/MEG data and as my data (HD-sEMG) has many of the same characteristics as EEG data, this toolbox might be useful to analyse my data. But in my case, I have already analysed the EMG signals by calculating NMI, and only need to do the statistical comparison now. FieldTrip is provided with a couple of statistical functions, but I'm unsure about how to use these on my data and if it is possible with this toolbox. The approach I'm looking into is FieldTrip cluster-based permutation tests, but it cannot directly be transferred to my case, because I am comparing the already analysed data. So my data kind of already have the data structure of the output from the analysis of this tutorial (the plots). So my question is: How can I use the statistical functions of the FieldTrip toolbox for comparison of NMI (spatio-temporal data)? If this is possible? I want to compare the heatmaps (12x5 vs 12x5 data structures) to each other and connectivity maps (60x60 vs 60x60 data structures) to each other. Thank you Best regards Toby S. Waterstone _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From stanabe at wisc.edu Wed Nov 14 21:26:33 2018 From: stanabe at wisc.edu (SEAN TANABE) Date: Wed, 14 Nov 2018 20:26:33 +0000 Subject: [FieldTrip] Concerning powcorr_orth method in ft_connectivityanalysis Message-ID: Dear all, I am trying to use the powcorr_orth method in ft_connectivityanalysis and noticed the results always produce a random connectivity pattern. A similar issue has been raised a year ago here; https://mailman.science.ru.nl/pipermail/fieldtrip/2017-July/011695.html I found after editing a line from ft_connectivityanalysis the results look more reasonable, and wanted to confirm if this editing is correct with the fieldtrip developers. In fieldtrip-20170820 ft_connectivityanalysis I noticed a line which reshapes the fourierspctrm before inputting to ft_connectivity_powcorr_orth (line 852 in fieldtrip-20181113). dat = reshape(data.fourierspctrm(:,:,i)', nrpttap, nchan).'; datout{i} = ft_connectivity_powcorr_ortho(dat, optarg{:}); This seems to not fit the description of ft_connectivity_powcorr_orth, which requires "dat" to have nchan rows. I changed this to dat = data.fourierspctrm(:,:,i)'; datout{i} = ft_connectivity_powcorr_ortho(dat, optarg{:}); The above gave natural looking blobs of connectivity in the topology. Thank you. Sean -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Thu Nov 15 09:25:39 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Thu, 15 Nov 2018 08:25:39 +0000 Subject: [FieldTrip] Concerning powcorr_orth method in ft_connectivityanalysis In-Reply-To: References: Message-ID: <3E50C67C-9EF0-4C04-8988-AAA879C87134@donders.ru.nl> Dear Sean, Thanks for catching this. The original line 852 does not make sense at all, indeed. May I suggest to replace this with data.fourierspctrm(:,:,i).’ (note the ‘.’)? Can you please submit this as a pull request on github? http://www.fieldtriptoolbox.org/development/git Many thanks, Jan-Mathijs On 14 Nov 2018, at 21:26, SEAN TANABE > wrote: Dear all, I am trying to use the powcorr_orth method in ft_connectivityanalysis and noticed the results always produce a random connectivity pattern. A similar issue has been raised a year ago here; https://mailman.science.ru.nl/pipermail/fieldtrip/2017-July/011695.html I found after editing a line from ft_connectivityanalysis the results look more reasonable, and wanted to confirm if this editing is correct with the fieldtrip developers. In fieldtrip-20170820 ft_connectivityanalysis I noticed a line which reshapes the fourierspctrm before inputting to ft_connectivity_powcorr_orth (line 852 in fieldtrip-20181113). dat = reshape(data.fourierspctrm(:,:,i)', nrpttap, nchan).'; datout{i} = ft_connectivity_powcorr_ortho(dat, optarg{:}); This seems to not fit the description of ft_connectivity_powcorr_orth, which requires "dat" to have nchan rows. I changed this to dat = data.fourierspctrm(:,:,i)'; datout{i} = ft_connectivity_powcorr_ortho(dat, optarg{:}); The above gave natural looking blobs of connectivity in the topology. Thank you. Sean _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From tineke.snijders at donders.ru.nl Thu Nov 15 12:27:26 2018 From: tineke.snijders at donders.ru.nl (Snijders, T.M. (Tineke)) Date: Thu, 15 Nov 2018 11:27:26 +0000 Subject: [FieldTrip] ft_rejectvisual reduces trial length In-Reply-To: <17306DD6-842B-4153-A0D1-CFA9DA13CD90@donders.ru.nl> References: <6ab24f95f8ef4b8eae7f911f5d9946af@EXPRD07.hosting.ru.nl>, <17306DD6-842B-4153-A0D1-CFA9DA13CD90@donders.ru.nl> Message-ID: <1542281246035.19816@donders.ru.nl> Thanks for fixing this Jan-Mathijs, it works beautifully now! Tineke ________________________________ From: fieldtrip on behalf of Schoffelen, J.M. (Jan Mathijs) Sent: Friday, November 9, 2018 11:18 AM To: FieldTrip discussion list Subject: Re: [FieldTrip] ft_rejectvisual reduces trial length Hi Tineke, This is indeed inappropriate behaviour of the function, which has been tracked down to something going wrong in ft_selectdata. I have submitted a PR https://github.com/fieldtrip/fieldtrip/pull/873 on the github-repo which fixes the issue https://github.com/fieldtrip/fieldtrip/issues/871 I am pretty sure that this fix does not come with unwanted side effects, but since ft_selectdata is used almost everywhere I'd appreciate some moral support before merging. Let me know what you think. Jan-Mathijs PS: for those who are around: please do join Tineke and me this coming Sunday Nov 11, when we will commemorate the centenary of the 1918 armistice with a choral concert at the church of St. Anthony of Padua in Nijmegen. It starts at 15.30, see http://www.kamerkoormnemosyne.nl J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands On 7 Nov 2018, at 13:28, Snijders, T.M. (Tineke) > wrote: Hi, I am using trials of different lengths, which seems to give a problem in ft_rejectvisual. It wasn't a problem before (I think in 2016) when I did the same analysis. When using ft_rejectvisual, in the current fieldtrip version in the output data all trial lengths are reduced to the shortest trial-length, cutting away the remainder of the trial. In the command window it gives a warning of 'inconsistent sampling info'. Is there a workaround to still get the full trial-lengths for the artifact rejected data? I do want to keep the complete trials at this point. Thanks, Tineke -- Dr T.M. Snijders Research Staff Max Planck Institute for Psycholinguistics, Nijmegen www.ru.nl/people/donders/snijders-t http://www.mpi.nl/departments/language-development MPI | Wundtlaan 1 | Room 246 | tel 024 35 21246 | PO Box 310 | 6500 AH Nijmegen _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From kai.hwang at gmail.com Fri Nov 16 17:47:38 2018 From: kai.hwang at gmail.com (Kai Hwang) Date: Fri, 16 Nov 2018 10:47:38 -0600 Subject: [FieldTrip] Postdoc Position | Cognitive Neuroscience | University of Iowa Message-ID: The Hwang lab for Neurocognitive Dynamics in the Psychological and Brain Sciences Department at the University of Iowa has a fully funded postdoc position available. Our lab focuses on brain network mechanisms, cognitive control, and their developmental processes with a strong emphasis on the human thalamocortical system and neural oscillations. Our research utilizes multimodal neuroimaging (EEG and fMRI), TMS, and lesion studies in combination with network neuroscience approaches. For more info, please see: https://kaihwang.github.io/ The lab is affiliated with the DeLTA Center and the Iowa Neuroscience Institute, which offers a collaborative research environment with access to research dedicated 3T and 7T MRI systems, TMS, EEG, neurosurgery patients, and a large lesion patient registry. Postdoc Candidate Qualifications - Ph.D. in Psychology, Neuroscience, or other related disciplines. - Experience with neuroimaging (fMRI, EEG/MEG). - Strong Programming skills (Matlab or Python) The postdoc position is opened immediately until filled. To apply, please send a cover letter and CV to: kai-hwang at uiowa.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From Eliana.Klier at uth.tmc.edu Fri Nov 16 19:33:21 2018 From: Eliana.Klier at uth.tmc.edu (Klier, Eliana M) Date: Fri, 16 Nov 2018 18:33:21 +0000 Subject: [FieldTrip] Postdoc Job Posting Message-ID: <0e5edb17c01c46b8a4516640461d11b4@uth.tmc.edu> Hello Fieldtrip, Is it possible for the Tandon Lab to post the attached postdoctoral ad to your mailing list? Sincerely, Eliana Eliana M Klier, Ph.D. Senior Program Manager - Research McGovern Medical School part of UTHealth | The University of Texas Health Science Center at Houston Department of Neurosurgery 6431 Fannin St | Rm G.550G | Houston, TX 77030 Phone: 713-500-5442 Email: Eliana.Klier at uth.tmc.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Tandon Lab Post Doc.docx Type: application/vnd.openxmlformats-officedocument.wordprocessingml.document Size: 159409 bytes Desc: Tandon Lab Post Doc.docx URL: From Oscar.Woolnough at uth.tmc.edu Fri Nov 16 17:39:54 2018 From: Oscar.Woolnough at uth.tmc.edu (Woolnough, Oscar) Date: Fri, 16 Nov 2018 16:39:54 +0000 Subject: [FieldTrip] Multiple Postdocs available in the Neurobiology of Language Message-ID: <064DCE91-3B14-4E59-91E5-F1AD8A0EE289@uth.tmc.edu> POSTDOCTORAL RESEARCH POSITIONS Multiple Postdoctoral research positions are available in the Tandon Lab at The University of Texas in Houston as part of the newly formed Texas Epilepsy Neurotechnologies and Neuroinformatics (TENN) Institute. Positions are funded either via multi year Institute funding or by NIH funds (an R01 and a U01). The lab uses multimodal approaches – fMRI, lesional analysis following epilepsy surgery, intracranial recordings and direct stimulation to create validate network level representations of language. Lab Collaborators include Greg Hickok (UCI), Stanislas Dehaene (NeuroSpin), Nathan Crone (JHU), Simon Fisher Baum (Rice) and Xaq Pitkow (Rice-Baylor); the post-doc will benefit from a close interaction with these experts in the fields of reading, semantics, speech production and computational neuroscience. The selected individual must have a Ph.D. in one or more of the following: neuroscience, psychology, cognitive science, mathematics, electrical engineering or computer science. Previous experience in neural time series data analysis, functional imaging studies of language, or studies of speech production are desirable – but not crucial. They must possess the ability to independently code in any or all of the following: MATLAB, R or python. They are expected to be highly motivated, team players with a passion to study cognitive processes using any or all of the various modalities available in the lab - imaging, direct recordings and closed-loop cortical stimulation in humans. Given the multiple unpredictable variables and privacy issues around data collection in human patients, the individual must possess high ethical and professional standards and be adaptable. A strong publication record and excellent academic credentials are highly desirable. CONTACT: Nitin.Tandon at uth.tmc.edu Eliana.Klier at uth.tmc.edu More information @ www.tandonlab.org -------------- next part -------------- An HTML attachment was scrubbed... URL: From p.gaur at ulster.ac.uk Mon Nov 19 19:19:55 2018 From: p.gaur at ulster.ac.uk (Gaur, Pramod) Date: Mon, 19 Nov 2018 18:19:55 +0000 Subject: [FieldTrip] Combine multiple blocks Message-ID: Dear Team, I have a quick concern, I have a cognitive task and recorded three blocks. Is there any way in which I can combine all the three blocks before the preprocessing. I have alternate to preprocess the blocks separately and then combining them. In this case, I lose the data_MEG_filt .grad.chanpos location. cfg = []; cfg.dataset = filename; % % cfg = ft_definetrial(cfg); cfg.trialdef.eventtype = 'STI101'; %cfg.trialdef.eventtype = 'gui'; cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own cfg.trialdef.prestim = prestim; cfg.trialdef.poststim = poststim; % cfg.length = 1; cfg = ft_definetrial(cfg); % read in the data from the magnetometer % cfg.channel = {'MEGMAG','STI101'}; cfg.channel = {'MEG'};%,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};% % cfg.lpfilter = 'yes'; % cfg.lpfreq = 40; % cfg.hpfilter = 'yes'; % cfg.hpfreq = 1; cfg.continuous = 'yes'; cfg.detrend = 'no'; cfg.demean = 'yes'; cfg.dftfilter = 'yes'; % cfg.dftfreq =[50 100 150];% power line noise cfg.bpfreq = [1 48]; cfg.metric = 'zvalue'; cfg.layout = 'neuromag306all.lay'; cfg.baselinewindow = [-0.5 0]; data_MEG_filt = ft_preprocessing(cfg); if isempty(data_MEG_filt) data_MEG_filt=data; grad = data.grad; else cfg = []; data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data); end Any advice will be highly appreciated. [Ulster University] Dr Pramod Gaur Research Assistant in Neuro-Imaging Technology School of Computing, Engineering and Intelligent Systems Magee Campus E: p.gaur at ulster.ac.uk W: www.ulster.ac.uk Social: Twitter: @SceisUni Facebook: @UlsterUniComputingEngineeringIntelligentSystems [cid:image004.jpg at 01D3EDDB.BF0D58A0] This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image001.jpg Type: image/jpeg Size: 37467 bytes Desc: image001.jpg URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image003.jpg Type: image/jpeg Size: 3389 bytes Desc: image003.jpg URL: From jan.schoffelen at donders.ru.nl Mon Nov 19 20:50:58 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Mon, 19 Nov 2018 19:50:58 +0000 Subject: [FieldTrip] Combine multiple blocks In-Reply-To: References: Message-ID: Pramod, I don’t understand how ‘combining all the three blocks before the preprocessing’ would help you to preserve the channel positions. Apparently, these are different for each of the blocks in your case. It’s also not clear to me, why this is causes problem in your specific case. Anyway, ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs. If you still want to obtain some kind of ‘average’ representation of the channel position, you can use ft_average_sens. If the positions are not altogether too different, it might be OK to average the positions across blocks, although technically it’s of course incorrect to do so. Yet, if for each of the blocks you have applied block-specific spatial transformations (e.g. maxfilter) then it’s a different story altogether, and things will become hairy very rapidly. Best wishes, Jan-Mathijs On 19 Nov 2018, at 19:19, Gaur, Pramod > wrote: Dear Team, I have a quick concern, I have a cognitive task and recorded three blocks. Is there any way in which I can combine all the three blocks before the preprocessing. I have alternate to preprocess the blocks separately and then combining them. In this case, I lose the data_MEG_filt .grad.chanpos location. cfg = []; cfg.dataset = filename; % % cfg = ft_definetrial(cfg); cfg.trialdef.eventtype = 'STI101'; %cfg.trialdef.eventtype = 'gui'; cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own cfg.trialdef.prestim = prestim; cfg.trialdef.poststim = poststim; % cfg.length = 1; cfg = ft_definetrial(cfg); % read in the data from the magnetometer % cfg.channel = {'MEGMAG','STI101'}; cfg.channel = {'MEG'};%,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};% % cfg.lpfilter = 'yes'; % cfg.lpfreq = 40; % cfg.hpfilter = 'yes'; % cfg.hpfreq = 1; cfg.continuous = 'yes'; cfg.detrend = 'no'; cfg.demean = 'yes'; cfg.dftfilter = 'yes'; % cfg.dftfreq =[50 100 150];% power line noise cfg.bpfreq = [1 48]; cfg.metric = 'zvalue'; cfg.layout = 'neuromag306all.lay'; cfg.baselinewindow = [-0.5 0]; data_MEG_filt = ft_preprocessing(cfg); if isempty(data_MEG_filt) data_MEG_filt=data; grad = data.grad; else cfg = []; data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data); end Any advice will be highly appreciated. Dr Pramod Gaur Research Assistant in Neuro-Imaging Technology School of Computing, Engineering and Intelligent Systems Magee Campus E: p.gaur at ulster.ac.uk W: www.ulster.ac.uk Social: Twitter: @SceisUni Facebook: @UlsterUniComputingEngineeringIntelligentSystems This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From p.gaur at ulster.ac.uk Mon Nov 19 23:04:29 2018 From: p.gaur at ulster.ac.uk (Gaur, Pramod) Date: Mon, 19 Nov 2018 22:04:29 +0000 Subject: [FieldTrip] Combine multiple blocks In-Reply-To: References: Message-ID: Hi Jan-Mathijs, I want to do source analysis. I have 3 blocks with 40 trials each for one subject and want to do the source analysis. Yes, you pointed it correctly “ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs.” Please advise me how to do the source analysis on them. Best regards, Pramod From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Schoffelen, J.M. (Jan Mathijs) Sent: 19 November 2018 19:51 To: FieldTrip discussion list Subject: Re: [FieldTrip] Combine multiple blocks Pramod, I don’t understand how ‘combining all the three blocks before the preprocessing’ would help you to preserve the channel positions. Apparently, these are different for each of the blocks in your case. It’s also not clear to me, why this is causes problem in your specific case. Anyway, ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs. If you still want to obtain some kind of ‘average’ representation of the channel position, you can use ft_average_sens. If the positions are not altogether too different, it might be OK to average the positions across blocks, although technically it’s of course incorrect to do so. Yet, if for each of the blocks you have applied block-specific spatial transformations (e.g. maxfilter) then it’s a different story altogether, and things will become hairy very rapidly. Best wishes, Jan-Mathijs On 19 Nov 2018, at 19:19, Gaur, Pramod > wrote: Dear Team, I have a quick concern, I have a cognitive task and recorded three blocks. Is there any way in which I can combine all the three blocks before the preprocessing. I have alternate to preprocess the blocks separately and then combining them. In this case, I lose the data_MEG_filt .grad.chanpos location. cfg = []; cfg.dataset = filename; % % cfg = ft_definetrial(cfg); cfg.trialdef.eventtype = 'STI101'; %cfg.trialdef.eventtype = 'gui'; cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own cfg.trialdef.prestim = prestim; cfg.trialdef.poststim = poststim; % cfg.length = 1; cfg = ft_definetrial(cfg); % read in the data from the magnetometer % cfg.channel = {'MEGMAG','STI101'}; cfg.channel = {'MEG'};%,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};% % cfg.lpfilter = 'yes'; % cfg.lpfreq = 40; % cfg.hpfilter = 'yes'; % cfg.hpfreq = 1; cfg.continuous = 'yes'; cfg.detrend = 'no'; cfg.demean = 'yes'; cfg.dftfilter = 'yes'; % cfg.dftfreq =[50 100 150];% power line noise cfg.bpfreq = [1 48]; cfg.metric = 'zvalue'; cfg.layout = 'neuromag306all.lay'; cfg.baselinewindow = [-0.5 0]; data_MEG_filt = ft_preprocessing(cfg); if isempty(data_MEG_filt) data_MEG_filt=data; grad = data.grad; else cfg = []; data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data); end Any advice will be highly appreciated. Dr Pramod Gaur Research Assistant in Neuro-Imaging Technology School of Computing, Engineering and Intelligent Systems Magee Campus E: p.gaur at ulster.ac.uk W: www.ulster.ac.uk Social: Twitter: @SceisUni Facebook: @UlsterUniComputingEngineeringIntelligentSystems This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Tue Nov 20 09:07:58 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Tue, 20 Nov 2018 08:07:58 +0000 Subject: [FieldTrip] Combine multiple blocks In-Reply-To: References: Message-ID: <1F1F8765-C52F-4B0C-A7E0-9AFC30E1ED69@donders.ru.nl> Pramod, Your queries are lacking detail: i.e. there is no detail as to what you actually want to do (e.g. type of source reconstruction etc.), nor any detail about what you have tried yourself so far. Therefore, it is hard to give constructive feedback/directions. I suggest to first try and get a running pipeline to do source reconstruction using data from a single block. Then it’s relatively straightforward to extend this to a multiple block setting, where the exact sensible directions to take depend on the source reconstruction algorithm and the quality of the data per block. One possibility would be, as I already mentioned, to recover a more or less useable grad structure, using ft_average_sens. Alternatively, you could do the source reconstruction per block, and combine afterwards. Good luck, Jan-Mathijs J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands On 19 Nov 2018, at 23:04, Gaur, Pramod > wrote: Hi Jan-Mathijs, I want to do source analysis. I have 3 blocks with 40 trials each for one subject and want to do the source analysis. Yes, you pointed it correctly “ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs.” Please advise me how to do the source analysis on them. Best regards, Pramod From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Schoffelen, J.M. (Jan Mathijs) Sent: 19 November 2018 19:51 To: FieldTrip discussion list > Subject: Re: [FieldTrip] Combine multiple blocks Pramod, I don’t understand how ‘combining all the three blocks before the preprocessing’ would help you to preserve the channel positions. Apparently, these are different for each of the blocks in your case. It’s also not clear to me, why this is causes problem in your specific case. Anyway, ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs. If you still want to obtain some kind of ‘average’ representation of the channel position, you can use ft_average_sens. If the positions are not altogether too different, it might be OK to average the positions across blocks, although technically it’s of course incorrect to do so. Yet, if for each of the blocks you have applied block-specific spatial transformations (e.g. maxfilter) then it’s a different story altogether, and things will become hairy very rapidly. Best wishes, Jan-Mathijs On 19 Nov 2018, at 19:19, Gaur, Pramod > wrote: Dear Team, I have a quick concern, I have a cognitive task and recorded three blocks. Is there any way in which I can combine all the three blocks before the preprocessing. I have alternate to preprocess the blocks separately and then combining them. In this case, I lose the data_MEG_filt .grad.chanpos location. cfg = []; cfg.dataset = filename; % % cfg = ft_definetrial(cfg); cfg.trialdef.eventtype = 'STI101'; %cfg.trialdef.eventtype = 'gui'; cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own cfg.trialdef.prestim = prestim; cfg.trialdef.poststim = poststim; % cfg.length = 1; cfg = ft_definetrial(cfg); % read in the data from the magnetometer % cfg.channel = {'MEGMAG','STI101'}; cfg.channel = {'MEG'};%,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};% % cfg.lpfilter = 'yes'; % cfg.lpfreq = 40; % cfg.hpfilter = 'yes'; % cfg.hpfreq = 1; cfg.continuous = 'yes'; cfg.detrend = 'no'; cfg.demean = 'yes'; cfg.dftfilter = 'yes'; % cfg.dftfreq =[50 100 150];% power line noise cfg.bpfreq = [1 48]; cfg.metric = 'zvalue'; cfg.layout = 'neuromag306all.lay'; cfg.baselinewindow = [-0.5 0]; data_MEG_filt = ft_preprocessing(cfg); if isempty(data_MEG_filt) data_MEG_filt=data; grad = data.grad; else cfg = []; data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data); end Any advice will be highly appreciated. Dr Pramod Gaur Research Assistant in Neuro-Imaging Technology School of Computing, Engineering and Intelligent Systems Magee Campus E: p.gaur at ulster.ac.uk W: www.ulster.ac.uk Social: Twitter: @SceisUni Facebook: @UlsterUniComputingEngineeringIntelligentSystems This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster at ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From stephen.whitmarsh at gmail.com Tue Nov 20 09:43:48 2018 From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh) Date: Tue, 20 Nov 2018 09:43:48 +0100 Subject: [FieldTrip] Combine multiple blocks In-Reply-To: <1F1F8765-C52F-4B0C-A7E0-9AFC30E1ED69@donders.ru.nl> References: <1F1F8765-C52F-4B0C-A7E0-9AFC30E1ED69@donders.ru.nl> Message-ID: Pramod, *If *you can use MaxFilter (on NeuroMag system), you can also consider using MaxFilter to artificially align sensors locations to sensor position of e.g. the first recording. See their manual for that. However, I agree with Jan-Mathijs that that's technically questionable and probably unnecessary, and that combining blocks e.g. on source level, if data quality permits, would be a more proper approach. Cheers, Stephen On Tue, 20 Nov 2018 at 09:36, Schoffelen, J.M. (Jan Mathijs) < jan.schoffelen at donders.ru.nl> wrote: > Pramod, > > Your queries are lacking detail: i.e. there is no detail as to what you > actually want to do (e.g. type of source reconstruction etc.), nor any > detail about what you have tried yourself so far. Therefore, it is hard to > give constructive feedback/directions. > > I suggest to first try and get a running pipeline to do source > reconstruction using data from a single block. Then it’s relatively > straightforward to extend this to a multiple block setting, where the exact > sensible directions to take depend on the source reconstruction algorithm > and the quality of the data per block. One possibility would be, as I > already mentioned, to recover a more or less useable grad structure, using > ft_average_sens. Alternatively, you could do the source reconstruction per > block, and combine afterwards. > > Good luck, > Jan-Mathijs > > > > J.M.Schoffelen, MD PhD > Senior Researcher, VIDI-fellow - PI, language in interaction > Telephone: +31-24-3614793 > Physical location: room 00.028 > Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands > > On 19 Nov 2018, at 23:04, Gaur, Pramod wrote: > > Hi Jan-Mathijs, > > I want to do source analysis. I have 3 blocks with 40 trials each for one > subject and want to do the source analysis. Yes, you pointed it correctly “*ft_appenddata > throws the grad-field away, since it recognizes that the channel (and coil) > positions are different in each of the runs.*” Please advise me how to do > the source analysis on them. > > Best regards, > Pramod > > *From:* fieldtrip [mailto:fieldtrip-bounces at science.ru.nl > ] *On Behalf Of *Schoffelen, J.M. (Jan > Mathijs) > *Sent:* 19 November 2018 19:51 > *To:* FieldTrip discussion list > *Subject:* Re: [FieldTrip] Combine multiple blocks > > Pramod, > > I don’t understand how ‘combining all the three blocks before the > preprocessing’ would help you to preserve the channel positions. > Apparently, these are different for each of the blocks in your case. It’s > also not clear to me, why this is causes problem in your specific case. > > Anyway, ft_appenddata throws the grad-field away, since it recognizes that > the channel (and coil) positions are different in each of the runs. > If you still want to obtain some kind of ‘average’ representation of the > channel position, you can use ft_average_sens. If the positions are not > altogether too different, it might be OK to average the positions across > blocks, although technically it’s of course incorrect to do so. Yet, if for > each of the blocks you have applied block-specific spatial transformations > (e.g. maxfilter) then it’s a different story altogether, and things will > become hairy very rapidly. > > Best wishes, > > Jan-Mathijs > > > > On 19 Nov 2018, at 19:19, Gaur, Pramod wrote: > > Dear Team, > > I have a quick concern, I have a cognitive task and recorded three blocks. > Is there any way in which I can combine all the three blocks before the > preprocessing. I have alternate to preprocess the blocks separately and > then combining them. In this case, I lose the data_MEG_filt > .grad.chanpos location. > > cfg = []; > cfg.dataset = filename; > % % cfg = ft_definetrial(cfg); > cfg.trialdef.eventtype = 'STI101'; > %cfg.trialdef.eventtype = 'gui'; > cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own > cfg.trialdef.prestim = prestim; > cfg.trialdef.poststim = poststim; > % cfg.length = 1; > cfg = ft_definetrial(cfg); > > % read in the data from the magnetometer > % cfg.channel = {'MEGMAG','STI101'}; > cfg.channel = {'MEG'}; > %,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};% > % cfg.lpfilter = 'yes'; > % cfg.lpfreq = 40; > % cfg.hpfilter = 'yes'; > % cfg.hpfreq = 1; > cfg.continuous = 'yes'; > cfg.detrend = 'no'; > cfg.demean = 'yes'; > cfg.dftfilter = 'yes'; > % cfg.dftfreq =[50 100 150];% power line noise > cfg.bpfreq = [1 48]; > cfg.metric = 'zvalue'; > cfg.layout = 'neuromag306all.lay'; > cfg.baselinewindow = [-0.5 0]; > data_MEG_filt = ft_preprocessing(cfg); > > if isempty(data_MEG_filt) > data_MEG_filt=data; > grad = data.grad; > else > cfg = []; > data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data); > end > > > Any advice will be highly appreciated. > > > > > *Dr Pramod Gaur* > Research Assistant in Neuro-Imaging Technology > > School of Computing, Engineering and Intelligent Systems > > Magee Campus > > *E:* p.gaur at ulster.ac.uk *W:* www.ulster.ac.uk > *Social:* Twitter: @SceisUni > Facebook: @UlsterUniComputingEngineeringIntelligentSystems > > > > > > > > > > > > > This email and any attachments are confidential and intended solely for > the use of the addressee and may contain information which is covered by > legal, professional or other privilege. If you have received this email in > error please notify the system manager at postmaster at ulster.ac.uk and > delete this email immediately. Any views or opinions expressed are solely > those of the author and do not necessarily represent those of Ulster > University. > The University's computer systems may be monitored and communications > carried out on them may be recorded to secure the effective operation of > the system and for other lawful purposes. Ulster University does not > guarantee that this email or any attachments are free from viruses or 100% > secure. Unless expressly stated in the body of a separate attachment, the > text of email is not intended to form a binding contract. Correspondence to > and from the University may be subject to requests for disclosure by 3rd > parties under relevant legislation. > *The Ulster University was founded by Royal Charter in 1984 and is > registered with company number RC000726 and VAT registered number > GB672390524.The primary contact address for Ulster University in Northern > Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA* > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > > > This email and any attachments are confidential and intended solely for > the use of the addressee and may contain information which is covered by > legal, professional or other privilege. If you have received this email in > error please notify the system manager at postmaster at ulster.ac.uk and > delete this email immediately. Any views or opinions expressed are solely > those of the author and do not necessarily represent those of Ulster > University. > The University's computer systems may be monitored and communications > carried out on them may be recorded to secure the effective operation of > the system and for other lawful purposes. Ulster University does not > guarantee that this email or any attachments are free from viruses or 100% > secure. Unless expressly stated in the body of a separate attachment, the > text of email is not intended to form a binding contract. Correspondence to > and from the University may be subject to requests for disclosure by 3rd > parties under relevant legislation. > *The Ulster University was founded by Royal Charter in 1984 and is > registered with company number RC000726 and VAT registered number > GB672390524.The primary contact address for Ulster University in Northern > Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA* > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jan.schoffelen at donders.ru.nl Wed Nov 21 14:47:07 2018 From: jan.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs)) Date: Wed, 21 Nov 2018 13:47:07 +0000 Subject: [FieldTrip] code update: ft_timelockanalysis Message-ID: <5032C771-6BB8-4379-ADD3-C2557FD6F2C9@donders.ru.nl> Dear community, As you know, the FieldTrip development model is based on continuous incremental improvements, where changes are made to the code repository on github, sometimes multiple times per day. In our implicit code of conduct, one of our core values is to maintain backward compatibility. This means that we not only aim not to break properly functioning user scripts that have been written with a (not too outdated) slightly older version of the FieldTrip code, but also that we aim at function behavior being stable over time (unless we find a bug in the code), both in terms of settings of the default parameters, and in terms of numerical output. Occasionally, we decide to slightly sacrifice backward compatibility at the benefit of overall code consistency and maintainability. Usually, we don’t write an e-mail to the list when this happens, because everything is properly documented on github or bugzilla, and we don’t want to bother you with these mundane issues, but today I make an exception. The reason for this is that I re-implemented ft_timelockanalysis, which is one of the oldest FieldTrip functions and a loyal companion in many an analysis project over the past 15 years. Since I assume that this is a function that many of you use, I just want to make you aware of this. The most noticeable (if at all) changes are: 1) the option cfg.vartrllength has been deprecated. 2) the default behavior has changed from expecting the trials of the input data to have fixed length (and throwing an error otherwise) into full support of variable trial lengths, representing missing data as NaNs. 3) if you want to explicitly use the old option cfg.vartrllength=0 you should now specify cfg.latency = ‘minperiod’ 4) with the cfg.keeptrials you will either get an output structure with a single trial representation or with an average representation, not with both. This means that structures with both a ‘trial’, and ‘avg’ field will not be generated anymore. Probably, you won’t need to change anything in your scripts, but since it is difficult to foresee all possible scenarios you might want to think this over yourself. Happy computing, Jan-Mathijs J.M.Schoffelen, MD PhD Senior Researcher, VIDI-fellow - PI, language in interaction Telephone: +31-24-3614793 Physical location: room 00.028 Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands -------------- next part -------------- An HTML attachment was scrubbed... URL: From martin.rosenfelder at uni-ulm.de Wed Nov 21 16:14:39 2018 From: martin.rosenfelder at uni-ulm.de (Martin Rosenfelder) Date: Wed, 21 Nov 2018 16:14:39 +0100 Subject: [FieldTrip] Support vector machine Message-ID: <3b26-5bf57680-1-3f44a540@39160932> Dear fieldtrip community, I am using the DMLT toolbox' svm in order to classify EEG data in two different conditions. More precisely, the SVM is trained on trigger-based trials for a mental motor imagery condition and a resting state condition. The classifier works fine with accuracy rates between 50-80 %. In order to boost the performance of the classifier I would now like to look into the parameterization of the classifier, e.g. how the classifier is build. This includes decision rules the classifier uses in order to analyze the data I am feeding in. I was able to detect the SVM behind the ft_crossvalidate function but not how it works or how I can customize it. Is there a possibility to 'personalize' the SVM in the DMLT Toolbox? Thank you very much in advance for your help and effort! Best, Martin -- M.Sc.-Psych. Martin Rosenfelder Wissenschaftlicher Mitarbeiter Klinische und Biologische Psychologie Universität Ulm Raum 47.2.259 +49 731-50 26592 martin.rosenfelder at uni-ulm.de From S.Arana at donders.ru.nl Wed Nov 21 18:10:21 2018 From: S.Arana at donders.ru.nl (Arana, S.L. (Sophie)) Date: Wed, 21 Nov 2018 17:10:21 +0000 Subject: [FieldTrip] Support vector machine In-Reply-To: <3b26-5bf57680-1-3f44a540@39160932> References: <3b26-5bf57680-1-3f44a540@39160932> Message-ID: <1542820221153.94026@donders.ru.nl> Hi Martin, the options for svm as supported by the dml toolbox are limited to the properties of the svm class, that is weights, regularization, precomputed kernels and output (see fieldtrip/external/dmlt/+dml/svm.m) You can adjust those via the config you pass to ft_timelockstatistics, such as for example: cfg = []; cfg.method = 'crossvalidate'; cfg.type = 'nfold' ... cfg.mva = dml.analysis({dml.svm('C',lambda,'anyotheroption',xx}) out = ft_timelockstatistics(cfg...) Concerning the implementation, I am not using the svm bit of the toolbox myself, so I am by no means an expert but let me give it a shot. From what I see you can either by option 'native'=true use the svm as implemented in the Bioinformatics toolbox, otherwise the toolbox will compute a linear kernel and compute the classifier with quadratic loss as specified in fieldtrip/external/dmlt/external/svm/l2svm_cg. It seems there are more options implemented there for finding the weights but I'm afraid in order to get at those you would have to adjust the input arguments yourself in the svm.m code (i.e. line 70 & 75). Hope this helps a bit. Best, Sophie ___________________ M.Sc. Sophie L. Arana Doctoral researcher Neurobiology of Language - MPI for Psycholinguistics Max Planck Institute for Psycholinguistics PO Box 310, 6500 AH Nijmegen Netherlands T +31 24-3610887 E sophie.arana at mpi.nl ________________________________________ From: fieldtrip on behalf of Martin Rosenfelder Sent: Wednesday, November 21, 2018 4:14 PM To: fieldtrip at science.ru.nl Subject: [FieldTrip] Support vector machine Dear fieldtrip community, I am using the DMLT toolbox' svm in order to classify EEG data in two different conditions. More precisely, the SVM is trained on trigger-based trials for a mental motor imagery condition and a resting state condition. The classifier works fine with accuracy rates between 50-80 %. In order to boost the performance of the classifier I would now like to look into the parameterization of the classifier, e.g. how the classifier is build. This includes decision rules the classifier uses in order to analyze the data I am feeding in. I was able to detect the SVM behind the ft_crossvalidate function but not how it works or how I can customize it. Is there a possibility to 'personalize' the SVM in the DMLT Toolbox? Thank you very much in advance for your help and effort! Best, Martin -- M.Sc.-Psych. Martin Rosenfelder Wissenschaftlicher Mitarbeiter Klinische und Biologische Psychologie Universität Ulm Raum 47.2.259 +49 731-50 26592 martin.rosenfelder at uni-ulm.de _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 From nemethd at gmail.com Fri Nov 23 11:13:16 2018 From: nemethd at gmail.com (Dezso Nemeth) Date: Fri, 23 Nov 2018 11:13:16 +0100 Subject: [FieldTrip] Posdoc in Lyon, France In-Reply-To: References: Message-ID: *Postdoc in Cognitive Neuroscience* · CRNL – Center for Research in Neuroscience in Lyon · Published: 25-10-2018 · Closing date: 01-12-2018 · Contract: Fixed-term, 2,5 years · Start date: March 01, 2019 (flexible) *Job description* Applications are invited for a highly motivated, enthusiastic postdoctoral researcher with a PhD in cognitive neuroscience (or related field) to join a well-supported, friendly research team, based in the internationally renowned Center for Research in Neuroscience in Lyon (University of Lyon, CRNS, INSERM). The postdoctoral position is part of a research project named REWIRING that is funded by IDEXLYON Fellowship. The postdoc will be embedded in the IDEXLYON team (PI: Dezso Nemeth) at CRNL, Lyon. Using methods of M/EEG, fMRI and non-invasive brain stimulation (e.g., TMS), the project aims to investigate how memory representations can be updated ('rewired') in the human brain. More specifically, we will investigate the entire process of how statistical and sequential regularities are extracted from the environment (memory formation), how the extracted knowledge is consolidated and how it can be rewired. For more details see the publications of Dezso Nemeth and Karolina Janacsek at http://nemethlab.com/publications/, and particularly the following paper: Szegedi-Hallgató, E., Janacsek, K., Vékony, T., Tasi, L. A., Kerepes, L., Hompoth, E. A., ... & Németh, D. (2017). Explicit instructions and consolidation promote rewiring of automatic behaviors in the human mind. Scientific Reports, 7(1), 4365. The overall aim of Project REWIRING is to improve human learning and memory performance and boost rewiring of automatic behaviors. Within this project, the post-holder will be responsible for designing and carrying out experiments, analyzing data, and writing up manuscripts. Additionally, the postdoc will be closely involved in daily supervision of PhD and MSc students who work on the project. *Profile (Person specification)* Candidates who only partially meet the following profile are nonetheless strongly encouraged to apply! · PhD in cognitive neuroscience or an adjacent field (psychological, biological, biomedical, or computer sciences, also physics and mathematics); · A strong academic track record including publications in leading (inter)disciplinary journals; · A strong interest for fundamental research in cognitive neurosciences; · Advanced computational and/or programming skills (Matlab, Python, or other languages); · Experience in functional connectivity analysis (EEG, MEG or MRI); · Experience and interest in training and supervising junior scientists; · Capacity to participate in an interdisciplinary and international research environment; · Excellent interpersonal and communication skills to effectively collaborate and communicate in academia; · A proactive and goal-directed attitude, good organizational skills; · Fluency in written and spoken English and motivation to learn French. *Organization* The project is embedded in the unique and excellent infrastructure of the CRNL - Center for Research in Neuroscience in Lyon. Researchers working on this theme jointly organize regular discussion meetings and lectures to promote integration of research conducted within systems, behavioral, and cognitive neurosciences. Read more about what it means *to work at * CRNL. *Employment conditions* Salary will be in accordance with the relevant national labor agreement and based on research experience and qualifications. The earliest start date for this position is March 2018 (later start possible upon agreement). *Comments and contact information* *Application* We request applicants to send the following documents: 1) A cover letter briefly describing how their skills and experience meet the profile as set out in the person specification (max 1 page) 2) A research statement explaining their research interests in relation to Project REWIRING or to the PI’s publications (max 2 pages) (optional) 3) A recent CV and publication list 4) Two writing samples of the applicant's most significant work (published or unpublished manuscripts). 5) Contact information of three professional references. *Information* All additional information about the vacancy can be obtained from Dezso Nemeth, Principal Investigator, via nemeth at nemethlab.com. Submit your application to the following email address: hr at nemethlab.com *Please apply before December 1 (23:59 GMT).* *We are committed to building a diverse, equitable and inclusive scientific community.* *For this post, we particularly welcome applications by women and ethnic minorities.* *Recruitment agencies are asked not to respond to this job posting.* -------------------------------------- NEMETH, Dezso (PhD, DSc) Brain, Memory and Language Lab: http://www.memory-and-language.com Phone: +36-1-4614500/3565, +36-1-4614500/3519 -------------- next part -------------- An HTML attachment was scrubbed... URL: From aitor.martinezegurcegui at uzh.ch Mon Nov 26 12:00:08 2018 From: aitor.martinezegurcegui at uzh.ch (Aitor Egurtzegi) Date: Mon, 26 Nov 2018 12:00:08 +0100 Subject: [FieldTrip] segmenting and merging trial data Message-ID: <95bab51c-4397-b725-79e1-d2860100ccca@uzh.ch> Dear researchers at Fieldtrip, I have defined a very long time window for each trial with ft_preprocessing. I'm actually only interested in the very beginning and the end of the trial, so now I'm trying to segment my trials by using ft_redefinetrial and specifying the time window of interest with cfg.toilim, which I run in two steps, one for the beginning and the other for the end of the trial. My problem arises when I try to merge both segments together, because I tried the various ft_append functions but they do not merge my trials the way I want, so I would like to know if there's a way to put the data together again, or on the other hand if I can use a different approach, such as slicing and throwing away the part of the trial I'm not interested in. Hence, instead of taking two slices of data for the trials and merging them, discarding what's in between my time window of interest for each trial. Thanks in advance, Aitor From jonas at obleser.de Mon Nov 26 12:36:59 2018 From: jonas at obleser.de (Jonas Obleser) Date: Mon, 26 Nov 2018 12:36:59 +0100 Subject: [FieldTrip] =?utf-8?q?4-y_postdoc_opportunity_in_L=C3=BCbeck=2C_?= =?utf-8?q?Germany?= Message-ID: <3C4AADDB-DB6E-46E0-88AA-D3E17127E356@obleser.de> Dear colleagues, New Postdoc opportunity, starting April 1: Come do a 4-y Postdoc with us in University of Lübeck! Modellers and Causal-inference-folks should feel especially targeted. Besides our own EEG lab, a shared research MR Scanner, we have ample data to play with (and a few undergrads to teach stats to now and then). Link: https://tinyurl.com/obleserlab-postdoc-EN Best wishes, Jonas Jonas Obleser Professor Chair in Physiological Psychology and Research Methods University of Lübeck Department of Psychology MFC 8, Maria-Goeppert-Straße 9a 23562 Lübeck, Germany Phone +49 (0)451 3101 3620 Mobile +49 (0)171 6993337 jonas.obleser at uni-luebeck.de http://jonasobleser.com From Silvia.Formica at UGent.be Mon Nov 26 14:19:22 2018 From: Silvia.Formica at UGent.be (Silvia Formica) Date: Mon, 26 Nov 2018 13:19:22 +0000 Subject: [FieldTrip] ft_redefinedata after pre-processing Message-ID: <1543238361133.67318@UGent.be> ?Dear all, I'm in need of suggestions on the correct analyses strategy for my data. I have an experiment with a cue followed (after ~2 seconds) by a target. I epoched the data cue-locked and pre-processed my dataset. In my preprocessing I'm downsampling, re-referencing, rejecting some trials, running ICA, etc etc. My problem is that I would want to also epoch my data target-locked, but I want to do it after pre-processing the cue-locked dataset. This is because I want to exclude the same trials and apply exactly the same corrections to the two different datasets. I tried to use the function ft_redefinetrial, creating the new probe-locked trial definition and applying it to the cue-locked dataset. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) But I do not obtain what I wished for, because I get a data_target dataset that doesn't account for the trial rejection I performed while pre-processing the cue-locked dataset. So my questions are: 1) is it possible to achieve my goal? 2) If yes, what is the right strategy to do it? Thanks for any input and suggestion! Best, Silvia -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Mon Nov 26 15:43:27 2018 From: christine.blume at sbg.ac.at (Blume Christine) Date: Mon, 26 Nov 2018 14:43:27 +0000 Subject: [FieldTrip] ft_redefinedata after pre-processing In-Reply-To: <1543238361133.67318@UGent.be> References: <1543238361133.67318@UGent.be> Message-ID: <257c087109ac4b95a365d036a6971f15@sbg.ac.at> Dear Silvia, Do I understand correctly that data_cue is epoched, but not pre-processed? You can apply ft_rejectartifact after ft_redefinetrial. We usually pre-process continuous datasets and save the cfg of this pre-processing (in the example below cfg_myartifactrejection). Only then we epoch the data and apply this artifactrejection. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) cfg = []; data_target_clean = ft_rejectartifact(cfg_myartifactrejection, data_target); I hope this helps… Best, Christine Von: fieldtrip Im Auftrag von Silvia Formica Gesendet: Montag, 26. November 2018 14:19 An: fieldtrip at science.ru.nl Betreff: [FieldTrip] ft_redefinedata after pre-processing ​Dear all, I'm in need of suggestions on the correct analyses strategy for my data. I have an experiment with a cue followed (after ~2 seconds) by a target. I epoched the data cue-locked and pre-processed my dataset. In my preprocessing I'm downsampling, re-referencing, rejecting some trials, running ICA, etc etc. My problem is that I would want to also epoch my data target-locked, but I want to do it after pre-processing the cue-locked dataset. This is because I want to exclude the same trials and apply exactly the same corrections to the two different datasets. I tried to use the function ft_redefinetrial, creating the new probe-locked trial definition and applying it to the cue-locked dataset. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) But I do not obtain what I wished for, because I get a data_target dataset that doesn't account for the trial rejection I performed while pre-processing the cue-locked dataset. So my questions are: 1) is it possible to achieve my goal? 2) If yes, what is the right strategy to do it? Thanks for any input and suggestion! Best, Silvia -------------- next part -------------- An HTML attachment was scrubbed... URL: From Silvia.Formica at UGent.be Mon Nov 26 16:11:48 2018 From: Silvia.Formica at UGent.be (Silvia Formica) Date: Mon, 26 Nov 2018 15:11:48 +0000 Subject: [FieldTrip] ft_redefinedata after pre-processing In-Reply-To: <257c087109ac4b95a365d036a6971f15@sbg.ac.at> References: <1543238361133.67318@UGent.be>, <257c087109ac4b95a365d036a6971f15@sbg.ac.at> Message-ID: <1543245106825.84804@UGent.be> Dear Christine, thank you for your quick response! In my example data_cue is epoched and pre-processed. Nevertheless, I think the approach you suggested might work also in my case. I also downsample, re-reference and filter the continuous data. After these first steps, I epoch the cue-locked dataset. After epoching, I perform artifact rejection and ICA. I guess I can apply your approach to my data by running this exact same pipeline and saving the "cfg_artifactrejection" and my ICA components. Then I would go back to the filtered continuous data, epoch them target-locked and apply to this target-locked dataset the "cfg_artifactrejection"? saved before and removing the same ICA components selected before. Do you think this would be an acceptable approach? Thanks a lot! Silvia ________________________________ From: fieldtrip on behalf of Blume Christine Sent: 26 November 2018 15:43 To: fieldtrip at science.ru.nl Subject: Re: [FieldTrip] ft_redefinedata after pre-processing Dear Silvia, Do I understand correctly that data_cue is epoched, but not pre-processed? You can apply ft_rejectartifact after ft_redefinetrial. We usually pre-process continuous datasets and save the cfg of this pre-processing (in the example below cfg_myartifactrejection). Only then we epoch the data and apply this artifactrejection. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) cfg = []; data_target_clean = ft_rejectartifact(cfg_myartifactrejection, data_target); I hope this helps... Best, Christine Von: fieldtrip Im Auftrag von Silvia Formica Gesendet: Montag, 26. November 2018 14:19 An: fieldtrip at science.ru.nl Betreff: [FieldTrip] ft_redefinedata after pre-processing ?Dear all, I'm in need of suggestions on the correct analyses strategy for my data. I have an experiment with a cue followed (after ~2 seconds) by a target. I epoched the data cue-locked and pre-processed my dataset. In my preprocessing I'm downsampling, re-referencing, rejecting some trials, running ICA, etc etc. My problem is that I would want to also epoch my data target-locked, but I want to do it after pre-processing the cue-locked dataset. This is because I want to exclude the same trials and apply exactly the same corrections to the two different datasets. I tried to use the function ft_redefinetrial, creating the new probe-locked trial definition and applying it to the cue-locked dataset. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) But I do not obtain what I wished for, because I get a data_target dataset that doesn't account for the trial rejection I performed while pre-processing the cue-locked dataset. So my questions are: 1) is it possible to achieve my goal? 2) If yes, what is the right strategy to do it? Thanks for any input and suggestion! Best, Silvia -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Mon Nov 26 18:30:47 2018 From: christine.blume at sbg.ac.at (Blume Christine) Date: Mon, 26 Nov 2018 17:30:47 +0000 Subject: [FieldTrip] ft_redefinedata after pre-processing In-Reply-To: <1543245106825.84804@UGent.be> References: <1543238361133.67318@UGent.be>, <257c087109ac4b95a365d036a6971f15@sbg.ac.at> <1543245106825.84804@UGent.be> Message-ID: Dear Silvia, What you could do is do your downsampling, re-referencing, filtering and ICA on continuous data and save the resulting dataset as data1. Then, you do the artefact rejection on continuous data (unless you have long episodes that are of no interest, then you may reconsider) and save this to cfg_artifactrejection. That way, you obtain an artefact rejection file that is independent from epoching, sometimes not the worst idea. Then you go back and load data1, do the epoching, and then do the artefact rejection. Best, Christine Von: fieldtrip Im Auftrag von Silvia Formica Gesendet: Montag, 26. November 2018 16:12 An: FieldTrip discussion list Betreff: Re: [FieldTrip] ft_redefinedata after pre-processing Dear Christine, thank you for your quick response! In my example data_cue is epoched and pre-processed. Nevertheless, I think the approach you suggested might work also in my case. I also downsample, re-reference and filter the continuous data. After these first steps, I epoch the cue-locked dataset. After epoching, I perform artifact rejection and ICA. I guess I can apply your approach to my data by running this exact same pipeline and saving the "cfg_artifactrejection" and my ICA components. Then I would go back to the filtered continuous data, epoch them target-locked and apply to this target-locked dataset the "cfg_artifactrejection"​ saved before and removing the same ICA components selected before. Do you think this would be an acceptable approach? Thanks a lot! Silvia ________________________________ From: fieldtrip > on behalf of Blume Christine > Sent: 26 November 2018 15:43 To: fieldtrip at science.ru.nl Subject: Re: [FieldTrip] ft_redefinedata after pre-processing Dear Silvia, Do I understand correctly that data_cue is epoched, but not pre-processed? You can apply ft_rejectartifact after ft_redefinetrial. We usually pre-process continuous datasets and save the cfg of this pre-processing (in the example below cfg_myartifactrejection). Only then we epoch the data and apply this artifactrejection. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) cfg = []; data_target_clean = ft_rejectartifact(cfg_myartifactrejection, data_target); I hope this helps… Best, Christine Von: fieldtrip > Im Auftrag von Silvia Formica Gesendet: Montag, 26. November 2018 14:19 An: fieldtrip at science.ru.nl Betreff: [FieldTrip] ft_redefinedata after pre-processing ​Dear all, I'm in need of suggestions on the correct analyses strategy for my data. I have an experiment with a cue followed (after ~2 seconds) by a target. I epoched the data cue-locked and pre-processed my dataset. In my preprocessing I'm downsampling, re-referencing, rejecting some trials, running ICA, etc etc. My problem is that I would want to also epoch my data target-locked, but I want to do it after pre-processing the cue-locked dataset. This is because I want to exclude the same trials and apply exactly the same corrections to the two different datasets. I tried to use the function ft_redefinetrial, creating the new probe-locked trial definition and applying it to the cue-locked dataset. cfg=[]; cfg.trl=trl; # trl contains the trial definition of target-locked epochs data_target = ft_redefinetrial (cfg, data_cue) But I do not obtain what I wished for, because I get a data_target dataset that doesn't account for the trial rejection I performed while pre-processing the cue-locked dataset. So my questions are: 1) is it possible to achieve my goal? 2) If yes, what is the right strategy to do it? Thanks for any input and suggestion! Best, Silvia -------------- next part -------------- An HTML attachment was scrubbed... URL: From junho0525 at gmail.com Tue Nov 27 13:18:27 2018 From: junho0525 at gmail.com (=?UTF-8?B?7IaQ7KSA7Zi4?=) Date: Tue, 27 Nov 2018 21:18:27 +0900 Subject: [FieldTrip] Predefined Source Orientations for Source Localization In-Reply-To: References: Message-ID: Dear fieldtrip community, I am currently trying to get source time series form my MEG data using beamformer LCMV. I have run ft_sourceanalysis with fixed orientation option, and I found that the estimated source orientations do not perpendicular to source surface. I thought that dendrites of pyramidal cells point perpendicular directions to the cortical surface, but the result was different from what I expected. Then I have tried to provide predefined orientations so that I use the provided orientations rather than estimate them. However, I also failed to get orientations that I expected. So, here is my question, 1) Why estimated source orientation does not perpendicular to source surface? 2) Are there any methods or options that use predefined source orientations for source localization? 3) If it is natural to get sources that are not perpendicular to the cortical surface, how can I explain those source activities in terms of neuronal structure and neuronal activity? Thank you, Junho -------------- next part -------------- An HTML attachment was scrubbed... URL: From aitor.martinezegurcegui at uzh.ch Wed Nov 28 11:49:06 2018 From: aitor.martinezegurcegui at uzh.ch (Aitor Egurtzegi) Date: Wed, 28 Nov 2018 11:49:06 +0100 Subject: [FieldTrip] automatic IC rejection Message-ID: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> Dear researchers at Fieldtrip, In order to make my work more reproducible, I would like to automatically reject ICs instead of doing visual inspection and rejection of the components. Unfortunately, I haven't found any documentation for such thing. Is there a way to do it in Fieldtrip? Best, Aitor From julian.keil at gmail.com Wed Nov 28 12:52:26 2018 From: julian.keil at gmail.com (Julian Keil) Date: Wed, 28 Nov 2018 12:52:26 +0100 Subject: [FieldTrip] automatic IC rejection In-Reply-To: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> References: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> Message-ID: Dear Aitor, irrespective of whether that is a good idea, I can suggest the EEGLab Toolbox SASICA (https://github.com/dnacombo/SASICA/ ). This toolbox can identify ICs based on a number of criteria and automatically reject these. So, if you manage to transform your FT-data into an EEGLab-like structure, this might work for you. Best, Julian > Am 28.11.2018 um 11:49 schrieb Aitor Egurtzegi : > > Dear researchers at Fieldtrip, > > > In order to make my work more reproducible, I would like to automatically reject ICs instead of doing visual inspection and rejection of the components. Unfortunately, I haven't found any documentation for such thing. Is there a way to do it in Fieldtrip? > > Best, > Aitor > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From david.schubring at uni-konstanz.de Wed Nov 28 13:47:00 2018 From: david.schubring at uni-konstanz.de (David Schubring) Date: Wed, 28 Nov 2018 12:47:00 +0000 Subject: [FieldTrip] automatic IC rejection In-Reply-To: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> References: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> Message-ID: <9b277ef4-067a-9a61-a11b-da7d504b9717@uni-konstanz.de> Dear Aitor, the closest thing I know of for a data-driven approach of selecting independent components is COMPASS, quote: "COMPASS is a MATLAB and EEGLAB based algorithm with the purpose of providing the user with a convenient technique for automatic Independent Component (IC) selection with respect to the contributions of the ICs to a certain ERP." Link to the toolbox: http://53450283.de.strato-hosting.eu/jrw/lab/e_compass.htm Paper: Wessel, J. R., & Ullsperger, M. (2011). Selection of independent components representing event-related brain potentials: a data-driven approach for greater objectivity. Neuroimage, 54(3), 2105-2115. https://doi.org/10.1016/j.neuroimage.2010.10.033 I have only theoretical experience with the toolbox as I only learned about it in a workshop and did not yet have the time to test and implement it in my personal FieldTrip workflow (even though it is on my ever growing to-do list). So far it looked like a useful thing to try out to me, especially as code can better be reproduced than "personal judgement". Best, David Am 28.11.2018 um 10:49 schrieb Aitor Egurtzegi: > Dear researchers at Fieldtrip, > > > In order to make my work more reproducible, I would like to > automatically reject ICs instead of doing visual inspection and > rejection of the components. Unfortunately, I haven't found any > documentation for such thing. Is there a way to do it in Fieldtrip? > > Best, > Aitor > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -- Dr. David Schubring General & Biological Psychology University of Konstanz Room C524 P.O. Box 36 78457 Konstanz Phone: +49-(0)7531-88-5350 Homepage: https://gpbp.uni-konstanz.de/people-page/david-schubring From MatthiasCMoeller at gmx.de Wed Nov 28 15:15:41 2018 From: MatthiasCMoeller at gmx.de (=?UTF-8?Q?=22Matthias_M=C3=B6ller=22?=) Date: Wed, 28 Nov 2018 15:15:41 +0100 Subject: [FieldTrip] Spike artifacts in EEG, 7-12Hz Message-ID: An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: 9 Hz Artefact.JPG Type: image/jpeg Size: 492043 bytes Desc: not available URL: From christine.blume at sbg.ac.at Wed Nov 28 16:04:32 2018 From: christine.blume at sbg.ac.at (Blume Christine) Date: Wed, 28 Nov 2018 15:04:32 +0000 Subject: [FieldTrip] Spike artifacts in EEG, 7-12Hz In-Reply-To: References: Message-ID: <07979b73a31a4cb5bd02398211e36ec6@sbg.ac.at> Dear Matthias, Admittedly, I do not know what this could be. While the first step should of course be to find the source and eliminate this (any devices in the EEG lab, artefact from the acoustic stimulation/headphones, …), in case you are unable to find it, you could remove the component(s) that correspond to the artefact. But as I said, the first goal should always be to record clean data… Best, Christine Von: fieldtrip Im Auftrag von "Matthias Möller" Gesendet: Mittwoch, 28. November 2018 15:16 An: fieldtrip at science.ru.nl Betreff: [FieldTrip] Spike artifacts in EEG, 7-12Hz Dear all, my name is Matthias and I am relatively new to the field of EEG analysis. I'm currently carrying out a study on the effects of natural sounds on the quantitative EEG in patients with Parkinson's disease at the universities of Vanvouver and Marburg. Right now I'm experiencing these weird artifacts as seen in the screenshot. It's sharp spikes, looking similar to ECG artifacts, but in frequencies of 7-12Hz. They are also showing up in the independent components after ICA as well. The recording was done at a sampling rate of 500 Hz, I've applied a Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz (recording took place in Canada). Has anyone seem similar artifacts before and maybe even knows how to get rid of them? Many thanks in advance and all the best, Matthias -------------- next part -------------- An HTML attachment was scrubbed... URL: From koculak.marcin at gmail.com Thu Nov 29 00:19:13 2018 From: koculak.marcin at gmail.com (Marcin Koculak) Date: Thu, 29 Nov 2018 00:19:13 +0100 Subject: [FieldTrip] Spike artifacts in EEG, 7-12Hz In-Reply-To: <07979b73a31a4cb5bd02398211e36ec6@sbg.ac.at> References: <07979b73a31a4cb5bd02398211e36ec6@sbg.ac.at> Message-ID: Dear Matthias, I have never seen such artifacts, but if you are working with patients with Parkinson's, have you checked if they have deep brain stimulation devices? Maybe that is causing the artifacts in the data? best, Marcin śr., 28 lis 2018 o 16:11 Blume Christine napisał(a): > Dear Matthias, > > > > Admittedly, I do not know what this could be. While the first step should > of course be to find the source and eliminate this (any devices in the EEG > lab, artefact from the acoustic stimulation/headphones, …), in case you are > unable to find it, you could remove the component(s) that correspond to the > artefact. But as I said, the first goal should always be to record clean > data… > > > > Best, > > Christine > > > > > > *Von:* fieldtrip *Im Auftrag von *"Matthias > Möller" > *Gesendet:* Mittwoch, 28. November 2018 15:16 > *An:* fieldtrip at science.ru.nl > *Betreff:* [FieldTrip] Spike artifacts in EEG, 7-12Hz > > > > Dear all, > > > > my name is Matthias and I am relatively new to the field of EEG analysis. > I'm currently carrying out a study on the effects of natural sounds on the > quantitative EEG in patients with Parkinson's disease at the universities > of Vanvouver and Marburg. > > > > Right now I'm experiencing these weird artifacts as seen in the > screenshot. It's sharp spikes, looking similar to ECG artifacts, but in > frequencies of 7-12Hz. > > They are also showing up in the independent components after ICA as well. > > > > The recording was done at a sampling rate of 500 Hz, I've applied a > Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz > (recording took place in Canada). > > > > Has anyone seem similar artifacts before and maybe even knows how to get > rid of them? > > > > Many thanks in advance and all the best, > > > > Matthias > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From anne.hauswald at me.com Thu Nov 29 09:26:20 2018 From: anne.hauswald at me.com (anne Hauswald) Date: Thu, 29 Nov 2018 09:26:20 +0100 Subject: [FieldTrip] Spike artifacts in EEG, 7-12Hz In-Reply-To: References: Message-ID: Dear Matthias, I don’t know where your artifacts come from, but I have some suggestions that might help you getting close to the source. - is it transient or do you find it throughout the whole recording? - do you find it in more than one recording? - does your choice of reference or filtering influence it? Not sure it will lead to something, but at least you will have a better understanding of this artifact. Best Anne > Am 28.11.2018 um 15:15 schrieb Matthias Möller : > > Dear all, > > my name is Matthias and I am relatively new to the field of EEG analysis. I'm currently carrying out a study on the effects of natural sounds on the quantitative EEG in patients with Parkinson's disease at the universities of Vanvouver and Marburg. > > Right now I'm experiencing these weird artifacts as seen in the screenshot. It's sharp spikes, looking similar to ECG artifacts, but in frequencies of 7-12Hz. > They are also showing up in the independent components after ICA as well. > > The recording was done at a sampling rate of 500 Hz, I've applied a Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz (recording took place in Canada). > > Has anyone seem similar artifacts before and maybe even knows how to get rid of them? > > Many thanks in advance and all the best, > > Matthias > <9 Hz Artefact.JPG>_______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From uwe.graichen at tu-ilmenau.de Thu Nov 29 10:08:29 2018 From: uwe.graichen at tu-ilmenau.de (Uwe Graichen) Date: Thu, 29 Nov 2018 10:08:29 +0100 Subject: [FieldTrip] =?utf-8?q?15_PhD_positions_in_Marie_Slodowskwa-Curie?= =?utf-8?q?_Innovative_Training_Network_=E2=80=9CINFANS=22?= In-Reply-To: <36f6a451-912e-ecfe-8796-7bde4fb9ff8e@tu-ilmenau.de> References: <36f6a451-912e-ecfe-8796-7bde4fb9ff8e@tu-ilmenau.de> Message-ID: As part of the Marie Skłodowska-Curie Innovative Training Network “INFANS - INtegrating Functional Assessment measures for Neonatal Safeguard" http://www.infansproject.eu , funded by the European Union’s Horizon 2020 Research and Innovation Programme, we advertise 15 PhD positions. The goal of INFANS is to develop a new neonatal brain monitoring system, designed to overcome the severe shortage of clinically viable means to high quality monitor the brain function in infancy, crucial to prevent later life neurological, cognitive and motor impairment. To accomplish this goal, INFANS established a structured European PhD training programme in biomedical engineering, signal processing and clinical procedures to train a new generation of creative and entrepreneurial young researchers. The individual research projects of the early stage researchers (ESR) encompass the topics: technological innovation, industrial development, clinical validation, identification of neonatal healthcare needs. As part of their research the INFANS ESRs will develop a novel platform for high quality, clinically-viable EEG-NIRS monitoring accessible worldwide. Well-targeted visits and secondments, soft skills and dynamic training activities, an Open Science strategy, extensive involvement of ESRs in the network events organization, extensive contacts with other research, training and industrial European networks, dissemination activities and the award of Double doctoral degrees are further assets offered to INFANS ESRs. Excellent science, industrial leadership and societal challenge are merged in the INFANS network. The INFANS consortium includes 6 academic and 4 non-academic partners from 6 EU countries, among which leading universities, companies and clinical institutions. The partners involved in INFANS share complementary expertise and facilities to provide international, interdisciplinary and intersectoral research training and mobility that will complement local doctoral training. The INFANS ESRs will become independent researchers with improved career prospects in both the academic and non-academic sectors, and will advance the EU capacity for innovation in biomedical engineering. The institution and the place where the different ESR projects will be carried out, the scientific supervisor(s), individual research project titles, and contact person for each available position can be found specified in the attached document. -------------- next part -------------- A non-text attachment was scrubbed... Name: ITN_INFANS Open_Position_20181129.pdf Type: application/pdf Size: 125155 bytes Desc: not available URL: From ablenkmann at gmail.com Thu Nov 29 15:30:27 2018 From: ablenkmann at gmail.com (Alejandro Blenkmann) Date: Thu, 29 Nov 2018 15:30:27 +0100 Subject: [FieldTrip] Fwd: FW: [all] 5 PhDs and 5 Postdoc fellowships (University of Oslo) In-Reply-To: References: Message-ID: Dear all, A new call for postdocs and PhDs is open at RITMO Center in Oslo. See information below, Best, Alejandro *From:* Alexander Refsum Jensenius *Sent:* Wednesday, November 21, 2018 10:01 PM *To:* all at ritmo.uio.no *Subject:* [all] 5 PhDs and 5 Postdoc fellowships (University of Oslo) Dear all, We are happy to announce a total of 10 recruit positions at RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion at the University of Oslo, Norway. 5 PhD fellowships in: · Rhythmic Robotics · Rhythm and Temporality in Audiovisual Narrative Media · Cognitive Neuroscience · Cross-modal Rhythms · Entrainment and Pleasure 5 postdoc fellowships in · Rhythmic Robotics · Eye Tracking and Motion Capture of rhythm-related behavior · Music, Time, and Consciousness · Electrophysiological basis of rhythm perception and production · fMRI RITMO is a Centre of Excellence funded by the Research Council of Norway, and focuses on rhythm as a structuring mechanism for temporal dimensions of human life. RITMO researchers work in a unique interdisciplinary constellation, with world-leading competence in musicology, psychology and informatics. The researchers have access to state-of-the-art facilities in sound/video recording, motion capture, eye tracking, physiological measurements, various types of brain imaging (EEG, fMRI), and rapid prototyping and robotics laboratories. · Application deadlines: 15 January / 15 March 2019 (check position) · Start-dates: August 2019 Please forward to relevant candidates. Apologies for cross-posting. *In addition: we are happy to host Marie Curie fellowship applications. Please get in touch if you are interested. * Best, -- Alexander Refsum Jensenius, Ph.D. Associate Professor, Department of Musicology, University of Oslo http://people.uio.no/alexanje/ Deputy Director, RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion http://www.uio.no/ritmo/ New book: "A NIME Reader" http://link.springer.com/book/10.1007/978-3-319-47214-0 New master's programme: " Music, Communication & Technology" http://www.uio.no/mct-master/ -- Alejandro Blenkmann, PhD Postdoctoral Fellow Front Neurolab Department of Psychology University of Oslo -- -------------- next part -------------- An HTML attachment was scrubbed... URL: From MatthiasCMoeller at gmx.de Thu Nov 29 16:55:26 2018 From: MatthiasCMoeller at gmx.de (=?UTF-8?Q?=22Matthias_M=C3=B6ller=22?=) Date: Thu, 29 Nov 2018 16:55:26 +0100 Subject: [FieldTrip] Spike artifacts 7-12Hz In-Reply-To: References: Message-ID: An HTML attachment was scrubbed... URL: From Hoang.Truong at colorado.edu Thu Nov 29 18:22:11 2018 From: Hoang.Truong at colorado.edu (Hoang Truong) Date: Thu, 29 Nov 2018 10:22:11 -0700 Subject: [FieldTrip] HELP: import simple data from txt file - non supported format, no header Message-ID: Dear community, This is Tony, from CS Department, University of Colorado Boulder. I am new to eeg analysis and fieldtrip. Currently I work on recording eeg and emg data using our own hardware with 2 eeg and 2 emg channels respectively. Since we use our custom hardware, the data are saved in a simple format of txt file with 7 columns (example data on Dropbox: link ): 3 first columns are data from environment sensors, the next 4 are eeg and emg data (250Hz sampling rate, 42s recording) I would like to ask what will be a proper way for me to import this data into fieldtrip. I checked the faq about importing custom data but I do not understand how to make custom readheader, readdata and readevent function. Any help would be appreciated (suggestions, code examples, etc). Thank you so much. Sincerely, Tony -------------- next part -------------- An HTML attachment was scrubbed... URL: From stephen.whitmarsh at gmail.com Thu Nov 29 19:37:11 2018 From: stephen.whitmarsh at gmail.com (Stephen Whitmarsh) Date: Thu, 29 Nov 2018 19:37:11 +0100 Subject: [FieldTrip] HELP: import simple data from txt file - non supported format, no header In-Reply-To: References: Message-ID: Hi Tony, As long as you are able to load the data into MATLAB, you should be able to put it into a MATLAB structure that is in accordance with how FieldTrip expect data to be organized. See the second paragraph "Circumvent the FieldTrip reading functions" of the FAQ you referred to, and this FAQ on the data structures. It will be something like (syntax is probably wrong): % read from file mydatafromfile = tdfread('mydata.txt'); % check the help of the read function, not sure how this goes exactly % datastructure of raw data, i.e. not epoched mydata = []; mydata.label = {'env1','env2','env3','eeg1','eeg2','emg'}; % label for every column of data mydata.trial{1} = mydatafromfile; % data in columns, channels in rows mydata.time{1} = (1:size(mydatafromfile,1)) * 1/250; % create fake time-axis, should be same length as data If you are not able to program in MATLAB (yet), FieldTrip might not (yet) be for you. In that case you can take a look at other MATLAB based software such as e.g. EEGlab or Brainstorm , which use GUIs for importing data that might help (I'm not familiar with them though). Out of curiosity, what hardware are you using? Build it yourself? Good luck, Stephen On Thu, 29 Nov 2018 at 18:44, Hoang Truong wrote: > Dear community, > > This is Tony, from CS Department, University of Colorado Boulder. I am new > to eeg analysis and fieldtrip. Currently I work on recording eeg and emg > data using our own hardware with 2 eeg and 2 emg channels respectively. > Since we use our custom hardware, the data are saved in a simple format of > txt file with 7 columns (example data on Dropbox: link > ): 3 > first columns are data from environment sensors, the next 4 are eeg and emg > data (250Hz sampling rate, 42s recording) > I would like to ask what will be a proper way for me to import this data > into fieldtrip. I checked the faq about importing custom data but I do not > understand how to make custom readheader, readdata and readevent function. > Any help would be appreciated (suggestions, code examples, etc). > Thank you so much. > > Sincerely, > Tony > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From christine.blume at sbg.ac.at Thu Nov 29 21:56:27 2018 From: christine.blume at sbg.ac.at (Blume Christine) Date: Thu, 29 Nov 2018 20:56:27 +0000 Subject: [FieldTrip] Spike artifacts 7-12Hz In-Reply-To: References: , Message-ID: Dear Matthias, I would guess that if the ICA nicely removes this component that might be the best approach, it is also commonly used to remove eye blinks or ECG artefacts from data. You can also try to design your own filter if the artefact is really centered around 9 Hz, although filtering is an issue on its own. Hope this helps! Christine ________________________________ Von: fieldtrip im Auftrag von "Matthias Möller" Gesendet: Donnerstag, 29. November 2018 16:55:26 An: fieldtrip at science.ru.nl Betreff: Re: [FieldTrip] Spike artifacts 7-12Hz Dear all, thanks a lot for your prompt answers, that is really supportive, thank you! Testing is already done, so I can't test for devices anymore whether they produce artifacts or not. To be honest I'm running out of ideas which devices might have been responsible. I used bluetooth headphones where the frequency is a lot higher, and in some sets I don't have the artifacts although subjects were stimulated using the same headphones. It's not in all recordings but in some. If it's in the recording, then it is throughout the whole one. It's not influenced by referencing or filtering. Deep brain stimulation seems to be to high in frequency as well. For now the only thing I can do is to remove the respective components indeed. Does anyone else maybe have an idea about how to filter out/get rid of those artifacts? Best, Matthias Gesendet: Donnerstag, 29. November 2018 um 10:08 Uhr Von: fieldtrip-request at science.ru.nl An: fieldtrip at science.ru.nl Betreff: fieldtrip Digest, Vol 96, Issue 22 Send fieldtrip mailing list submissions to fieldtrip at science.ru.nl To subscribe or unsubscribe via the World Wide Web, visit https://mailman.science.ru.nl/mailman/listinfo/fieldtrip or, via email, send a message with subject or body 'help' to fieldtrip-request at science.ru.nl You can reach the person managing the list at fieldtrip-owner at science.ru.nl When replying, please edit your Subject line so it is more specific than "Re: Contents of fieldtrip digest..." Today's Topics: 1. Re: Spike artifacts in EEG, 7-12Hz (Blume Christine) 2. Re: Spike artifacts in EEG, 7-12Hz (Marcin Koculak) 3. Re: Spike artifacts in EEG, 7-12Hz (anne Hauswald) 4. 15 PhD positions in Marie Slodowskwa-Curie Innovative Training Network “INFANS" (Uwe Graichen) ---------------------------------------------------------------------- Message: 1 Date: Wed, 28 Nov 2018 15:04:32 +0000 From: Blume Christine To: "fieldtrip at science.ru.nl" Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz Message-ID: <07979b73a31a4cb5bd02398211e36ec6 at sbg.ac.at> Content-Type: text/plain; charset="utf-8" Dear Matthias, Admittedly, I do not know what this could be. While the first step should of course be to find the source and eliminate this (any devices in the EEG lab, artefact from the acoustic stimulation/headphones, …), in case you are unable to find it, you could remove the component(s) that correspond to the artefact. But as I said, the first goal should always be to record clean data… Best, Christine Von: fieldtrip Im Auftrag von "Matthias Möller" Gesendet: Mittwoch, 28. November 2018 15:16 An: fieldtrip at science.ru.nl Betreff: [FieldTrip] Spike artifacts in EEG, 7-12Hz Dear all, my name is Matthias and I am relatively new to the field of EEG analysis. I'm currently carrying out a study on the effects of natural sounds on the quantitative EEG in patients with Parkinson's disease at the universities of Vanvouver and Marburg. Right now I'm experiencing these weird artifacts as seen in the screenshot. It's sharp spikes, looking similar to ECG artifacts, but in frequencies of 7-12Hz. They are also showing up in the independent components after ICA as well. The recording was done at a sampling rate of 500 Hz, I've applied a Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz (recording took place in Canada). Has anyone seem similar artifacts before and maybe even knows how to get rid of them? Many thanks in advance and all the best, Matthias -------------- next part -------------- An HTML attachment was scrubbed... URL: ------------------------------ Message: 2 Date: Thu, 29 Nov 2018 00:19:13 +0100 From: Marcin Koculak To: fieldtrip at science.ru.nl Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz Message-ID: Content-Type: text/plain; charset="utf-8" Dear Matthias, I have never seen such artifacts, but if you are working with patients with Parkinson's, have you checked if they have deep brain stimulation devices? Maybe that is causing the artifacts in the data? best, Marcin śr., 28 lis 2018 o 16:11 Blume Christine napisał(a): > Dear Matthias, > > > > Admittedly, I do not know what this could be. While the first step should > of course be to find the source and eliminate this (any devices in the EEG > lab, artefact from the acoustic stimulation/headphones, …), in case you are > unable to find it, you could remove the component(s) that correspond to the > artefact. But as I said, the first goal should always be to record clean > data… > > > > Best, > > Christine > > > > > > *Von:* fieldtrip *Im Auftrag von *"Matthias > Möller" > *Gesendet:* Mittwoch, 28. November 2018 15:16 > *An:* fieldtrip at science.ru.nl > *Betreff:* [FieldTrip] Spike artifacts in EEG, 7-12Hz > > > > Dear all, > > > > my name is Matthias and I am relatively new to the field of EEG analysis. > I'm currently carrying out a study on the effects of natural sounds on the > quantitative EEG in patients with Parkinson's disease at the universities > of Vanvouver and Marburg. > > > > Right now I'm experiencing these weird artifacts as seen in the > screenshot. It's sharp spikes, looking similar to ECG artifacts, but in > frequencies of 7-12Hz. > > They are also showing up in the independent components after ICA as well. > > > > The recording was done at a sampling rate of 500 Hz, I've applied a > Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz > (recording took place in Canada). > > > > Has anyone seem similar artifacts before and maybe even knows how to get > rid of them? > > > > Many thanks in advance and all the best, > > > > Matthias > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: ------------------------------ Message: 3 Date: Thu, 29 Nov 2018 09:26:20 +0100 From: anne Hauswald To: FieldTrip discussion list Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz Message-ID: Content-Type: text/plain; charset="utf-8" Dear Matthias, I don’t know where your artifacts come from, but I have some suggestions that might help you getting close to the source. - is it transient or do you find it throughout the whole recording? - do you find it in more than one recording? - does your choice of reference or filtering influence it? Not sure it will lead to something, but at least you will have a better understanding of this artifact. Best Anne > Am 28.11.2018 um 15:15 schrieb Matthias Möller : > > Dear all, > > my name is Matthias and I am relatively new to the field of EEG analysis. I'm currently carrying out a study on the effects of natural sounds on the quantitative EEG in patients with Parkinson's disease at the universities of Vanvouver and Marburg. > > Right now I'm experiencing these weird artifacts as seen in the screenshot. It's sharp spikes, looking similar to ECG artifacts, but in frequencies of 7-12Hz. > They are also showing up in the independent components after ICA as well. > > The recording was done at a sampling rate of 500 Hz, I've applied a Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz (recording took place in Canada). > > Has anyone seem similar artifacts before and maybe even knows how to get rid of them? > > Many thanks in advance and all the best, > > Matthias > <9 Hz Artefact.JPG>_______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: ------------------------------ Message: 4 Date: Thu, 29 Nov 2018 10:08:29 +0100 From: Uwe Graichen To: fieldtrip at science.ru.nl Subject: [FieldTrip] 15 PhD positions in Marie Slodowskwa-Curie Innovative Training Network “INFANS" Message-ID: Content-Type: text/plain; charset="utf-8" As part of the Marie Skłodowska-Curie Innovative Training Network “INFANS - INtegrating Functional Assessment measures for Neonatal Safeguard" http://www.infansproject.eu , funded by the European Union’s Horizon 2020 Research and Innovation Programme, we advertise 15 PhD positions. The goal of INFANS is to develop a new neonatal brain monitoring system, designed to overcome the severe shortage of clinically viable means to high quality monitor the brain function in infancy, crucial to prevent later life neurological, cognitive and motor impairment. To accomplish this goal, INFANS established a structured European PhD training programme in biomedical engineering, signal processing and clinical procedures to train a new generation of creative and entrepreneurial young researchers. The individual research projects of the early stage researchers (ESR) encompass the topics: technological innovation, industrial development, clinical validation, identification of neonatal healthcare needs. As part of their research the INFANS ESRs will develop a novel platform for high quality, clinically-viable EEG-NIRS monitoring accessible worldwide. Well-targeted visits and secondments, soft skills and dynamic training activities, an Open Science strategy, extensive involvement of ESRs in the network events organization, extensive contacts with other research, training and industrial European networks, dissemination activities and the award of Double doctoral degrees are further assets offered to INFANS ESRs. Excellent science, industrial leadership and societal challenge are merged in the INFANS network. The INFANS consortium includes 6 academic and 4 non-academic partners from 6 EU countries, among which leading universities, companies and clinical institutions. The partners involved in INFANS share complementary expertise and facilities to provide international, interdisciplinary and intersectoral research training and mobility that will complement local doctoral training. The INFANS ESRs will become independent researchers with improved career prospects in both the academic and non-academic sectors, and will advance the EU capacity for innovation in biomedical engineering. The institution and the place where the different ESR projects will be carried out, the scientific supervisor(s), individual research project titles, and contact person for each available position can be found specified in the attached document. -------------- next part -------------- A non-text attachment was scrubbed... Name: ITN_INFANS Open_Position_20181129.pdf Type: application/pdf Size: 125155 bytes Desc: not available URL: ------------------------------ Subject: Digest Footer _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 ------------------------------ End of fieldtrip Digest, Vol 96, Issue 22 ***************************************** -------------- next part -------------- An HTML attachment was scrubbed... URL: From rmontefusco at med.uchile.cl Thu Nov 29 22:31:40 2018 From: rmontefusco at med.uchile.cl (Rodrigo Montefusco) Date: Thu, 29 Nov 2018 18:31:40 -0300 Subject: [FieldTrip] Spike artifacts 7-12Hz In-Reply-To: References: Message-ID: Dear Matthias, I use to have something like that. Have you noticed if that happened at some particular time of the day? In my case it was solved after turning some equipment off in a neighbor lab, or by doing the recordings early in the morning, late in the night or during weekends. Good luck! Rodrigo On Thu, Nov 29, 2018 at 5:56 PM Blume Christine wrote: > Dear Matthias, > > > I would guess that if the ICA nicely removes this component that might be > the best approach, it is also commonly used to remove eye blinks or ECG > artefacts from data. You can also try to design your own filter if the > artefact is really centered around 9 Hz, although filtering is an issue on > its own. > > > Hope this helps! > > Christine > > > ------------------------------ > *Von:* fieldtrip im Auftrag von > "Matthias Möller" > *Gesendet:* Donnerstag, 29. November 2018 16:55:26 > *An:* fieldtrip at science.ru.nl > *Betreff:* Re: [FieldTrip] Spike artifacts 7-12Hz > > Dear all, > > thanks a lot for your prompt answers, that is really supportive, thank > you! > > > Testing is already done, so I can't test for devices anymore whether they > produce artifacts or not. > > To be honest I'm running out of ideas which devices might have been > responsible. I used bluetooth headphones where the frequency is a lot > higher, and in some sets I don't have the artifacts although subjects were > stimulated using the same headphones. It's not in all recordings but in > some. If it's in the recording, then it is throughout the whole one. > It's not influenced by referencing or filtering. > > Deep brain stimulation seems to be to high in frequency as well. > > For now the only thing I can do is to remove the respective components > indeed. > Does anyone else maybe have an idea about how to filter out/get rid of > those artifacts? > > Best, > > Matthias > > *Gesendet:* Donnerstag, 29. November 2018 um 10:08 Uhr > *Von:* fieldtrip-request at science.ru.nl > *An:* fieldtrip at science.ru.nl > *Betreff:* fieldtrip Digest, Vol 96, Issue 22 > Send fieldtrip mailing list submissions to > fieldtrip at science.ru.nl > > To subscribe or unsubscribe via the World Wide Web, visit > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > or, via email, send a message with subject or body 'help' to > fieldtrip-request at science.ru.nl > > You can reach the person managing the list at > fieldtrip-owner at science.ru.nl > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of fieldtrip digest..." > > > Today's Topics: > > 1. Re: Spike artifacts in EEG, 7-12Hz (Blume Christine) > 2. Re: Spike artifacts in EEG, 7-12Hz (Marcin Koculak) > 3. Re: Spike artifacts in EEG, 7-12Hz (anne Hauswald) > 4. 15 PhD positions in Marie Slodowskwa-Curie Innovative > Training Network “INFANS" (Uwe Graichen) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Wed, 28 Nov 2018 15:04:32 +0000 > From: Blume Christine > To: "fieldtrip at science.ru.nl" > Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz > Message-ID: <07979b73a31a4cb5bd02398211e36ec6 at sbg.ac.at> > Content-Type: text/plain; charset="utf-8" > > Dear Matthias, > > Admittedly, I do not know what this could be. While the first step should > of course be to find the source and eliminate this (any devices in the EEG > lab, artefact from the acoustic stimulation/headphones, …), in case you are > unable to find it, you could remove the component(s) that correspond to the > artefact. But as I said, the first goal should always be to record clean > data… > > Best, > Christine > > > Von: fieldtrip Im Auftrag von "Matthias > Möller" > Gesendet: Mittwoch, 28. November 2018 15:16 > An: fieldtrip at science.ru.nl > Betreff: [FieldTrip] Spike artifacts in EEG, 7-12Hz > > Dear all, > > my name is Matthias and I am relatively new to the field of EEG analysis. > I'm currently carrying out a study on the effects of natural sounds on the > quantitative EEG in patients with Parkinson's disease at the universities > of Vanvouver and Marburg. > > Right now I'm experiencing these weird artifacts as seen in the > screenshot. It's sharp spikes, looking similar to ECG artifacts, but in > frequencies of 7-12Hz. > They are also showing up in the independent components after ICA as well. > > The recording was done at a sampling rate of 500 Hz, I've applied a > Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz > (recording took place in Canada). > > Has anyone seem similar artifacts before and maybe even knows how to get > rid of them? > > Many thanks in advance and all the best, > > Matthias > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: < > http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20181128/ccb3622e/attachment-0001.html > > > > ------------------------------ > > Message: 2 > Date: Thu, 29 Nov 2018 00:19:13 +0100 > From: Marcin Koculak > To: fieldtrip at science.ru.nl > Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz > Message-ID: > > Content-Type: text/plain; charset="utf-8" > > Dear Matthias, > I have never seen such artifacts, but if you are working with patients with > Parkinson's, have you checked if they have deep brain stimulation devices? > Maybe that is causing the artifacts in the data? > best, > Marcin > > śr., 28 lis 2018 o 16:11 Blume Christine > napisał(a): > > > Dear Matthias, > > > > > > > > Admittedly, I do not know what this could be. While the first step should > > of course be to find the source and eliminate this (any devices in the > EEG > > lab, artefact from the acoustic stimulation/headphones, …), in case you > are > > unable to find it, you could remove the component(s) that correspond to > the > > artefact. But as I said, the first goal should always be to record clean > > data… > > > > > > > > Best, > > > > Christine > > > > > > > > > > > > *Von:* fieldtrip *Im Auftrag von > *"Matthias > > Möller" > > *Gesendet:* Mittwoch, 28. November 2018 15:16 > > *An:* fieldtrip at science.ru.nl > > *Betreff:* [FieldTrip] Spike artifacts in EEG, 7-12Hz > > > > > > > > Dear all, > > > > > > > > my name is Matthias and I am relatively new to the field of EEG analysis. > > I'm currently carrying out a study on the effects of natural sounds on > the > > quantitative EEG in patients with Parkinson's disease at the universities > > of Vanvouver and Marburg. > > > > > > > > Right now I'm experiencing these weird artifacts as seen in the > > screenshot. It's sharp spikes, looking similar to ECG artifacts, but in > > frequencies of 7-12Hz. > > > > They are also showing up in the independent components after ICA as well. > > > > > > > > The recording was done at a sampling rate of 500 Hz, I've applied a > > Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz > > (recording took place in Canada). > > > > > > > > Has anyone seem similar artifacts before and maybe even knows how to get > > rid of them? > > > > > > > > Many thanks in advance and all the best, > > > > > > > > Matthias > > _______________________________________________ > > fieldtrip mailing list > > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > > https://doi.org/10.1371/journal.pcbi.1002202 > > > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: < > http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20181129/ee2745dc/attachment-0001.html > > > > ------------------------------ > > Message: 3 > Date: Thu, 29 Nov 2018 09:26:20 +0100 > From: anne Hauswald > To: FieldTrip discussion list > Subject: Re: [FieldTrip] Spike artifacts in EEG, 7-12Hz > Message-ID: > Content-Type: text/plain; charset="utf-8" > > Dear Matthias, > > I don’t know where your artifacts come from, but I have some suggestions > that might help you getting close to the source. > - is it transient or do you find it throughout the whole recording? > - do you find it in more than one recording? > - does your choice of reference or filtering influence it? > > Not sure it will lead to something, but at least you will have a better > understanding of this artifact. > > Best Anne > > > > Am 28.11.2018 um 15:15 schrieb Matthias Möller >: > > > > Dear all, > > > > my name is Matthias and I am relatively new to the field of EEG > analysis. I'm currently carrying out a study on the effects of natural > sounds on the quantitative EEG in patients with Parkinson's disease at the > universities of Vanvouver and Marburg. > > > > Right now I'm experiencing these weird artifacts as seen in the > screenshot. It's sharp spikes, looking similar to ECG artifacts, but in > frequencies of 7-12Hz. > > They are also showing up in the independent components after ICA as well. > > > > The recording was done at a sampling rate of 500 Hz, I've applied a > Band-Pass filter from 1-249Hz and two notch filters, 60 and 120 Hz > (recording took place in Canada). > > > > Has anyone seem similar artifacts before and maybe even knows how to get > rid of them? > > > > Many thanks in advance and all the best, > > > > Matthias > > <9 Hz Artefact.JPG>_______________________________________________ > > fieldtrip mailing list > > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > > https://doi.org/10.1371/journal.pcbi.1002202 > > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: < > http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20181129/87d8fe88/attachment-0001.html > > > > ------------------------------ > > Message: 4 > Date: Thu, 29 Nov 2018 10:08:29 +0100 > From: Uwe Graichen > To: fieldtrip at science.ru.nl > Subject: [FieldTrip] 15 PhD positions in Marie Slodowskwa-Curie > Innovative Training Network “INFANS" > Message-ID: > Content-Type: text/plain; charset="utf-8" > > As part of the Marie Skłodowska-Curie Innovative Training Network > “INFANS - INtegrating Functional Assessment measures for Neonatal > Safeguard" http://www.infansproject.eu , funded by the European Union’s > Horizon 2020 Research and Innovation Programme, we advertise 15 PhD > positions. > > The goal of INFANS is to develop a new neonatal brain monitoring system, > designed to overcome the severe shortage of clinically viable means to > high quality monitor the brain function in infancy, crucial to prevent > later life neurological, cognitive and motor impairment. To accomplish > this goal, INFANS established a structured European PhD training > programme in biomedical engineering, signal processing and clinical > procedures to train a new generation of creative and entrepreneurial > young researchers. > > The individual research projects of the early stage researchers (ESR) > encompass the topics: technological innovation, industrial development, > clinical validation, identification of neonatal healthcare needs. As > part of their research the INFANS ESRs will develop a novel platform for > high quality, clinically-viable EEG-NIRS monitoring accessible > worldwide. Well-targeted visits and secondments, soft skills and dynamic > training activities, an Open Science strategy, extensive involvement of > ESRs in the network events organization, extensive contacts with other > research, training and industrial European networks, dissemination > activities and the award of Double doctoral degrees are further assets > offered to INFANS ESRs. > > Excellent science, industrial leadership and societal challenge are > merged in the INFANS network. The INFANS consortium includes 6 academic > and 4 non-academic partners from 6 EU countries, among which leading > universities, companies and clinical institutions. The partners involved > in INFANS share complementary expertise and facilities to provide > international, interdisciplinary and intersectoral research training and > mobility that will complement local doctoral training. The INFANS ESRs > will become independent researchers with improved career prospects in > both the academic and non-academic sectors, and will advance the EU > capacity for innovation in biomedical engineering. > > The institution and the place where the different ESR projects will be > carried out, the scientific supervisor(s), individual research project > titles, and contact person for each available position can be found > specified in the attached document. > > -------------- next part -------------- > A non-text attachment was scrubbed... > Name: ITN_INFANS Open_Position_20181129.pdf > Type: application/pdf > Size: 125155 bytes > Desc: not available > URL: < > http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20181129/928f2253/attachment.pdf > > > > ------------------------------ > > Subject: Digest Footer > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > > ------------------------------ > > End of fieldtrip Digest, Vol 96, Issue 22 > ***************************************** > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > -------------- next part -------------- An HTML attachment was scrubbed... URL: From Pranish.Kantak at UTSouthwestern.edu Thu Nov 29 22:47:42 2018 From: Pranish.Kantak at UTSouthwestern.edu (Pranish Kantak) Date: Thu, 29 Nov 2018 21:47:42 +0000 Subject: [FieldTrip] Remove from list serve Message-ID: <6D018139-18FD-44FB-808B-09268E09BE99@UTSouthwestern.edu> Hi! Could you please remove me from the field trip list serve? Thank you! Sent from my iPhone ________________________________ UT Southwestern Medical Center The future of medicine, today. From jason.taylor at manchester.ac.uk Fri Nov 30 02:05:05 2018 From: jason.taylor at manchester.ac.uk (Jason Taylor) Date: Fri, 30 Nov 2018 01:05:05 +0000 Subject: [FieldTrip] automatic IC rejection In-Reply-To: <9b277ef4-067a-9a61-a11b-da7d504b9717@uni-konstanz.de> References: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> <9b277ef4-067a-9a61-a11b-da7d504b9717@uni-konstanz.de> Message-ID: <48E6E95C4AB29E4BAD7908051F5C17EF016E1E31BC@MBXP07.ds.man.ac.uk> Hi Aitor, If you have an 'objective' measure of the artefact you're trying to remove (e.g., VEOG for blinks), a relatively straightforward method is to run a temporal correlation between each IC's activation time-course and the artefact channel's time-course. You can then reject any IC with a correlation higher than some threshold, or with a Z-score (r value relative to the distribution of IC r values) above some threshold. This tends to work very well for identifying blinks, and fairly well for eye-movements (*EOG), and can work for pulse artefact if you have recorded ECG. To avoid spurious correlations due to high-frequency noise, you can filter (e.g., 1 to 30 Hz) the component and artefact signals before correlating them (but obviously go back to the original unfiltered signals to continue with your analysis). Best wishes, Jason -----Original Message----- From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of David Schubring Sent: 28 November 2018 12:47 To: fieldtrip at science.ru.nl; aitor.martinezegurcegui at uzh.ch Subject: Re: [FieldTrip] automatic IC rejection Dear Aitor, the closest thing I know of for a data-driven approach of selecting independent components is COMPASS, quote: "COMPASS is a MATLAB and EEGLAB based algorithm with the purpose of providing the user with a convenient technique for automatic Independent Component (IC) selection with respect to the contributions of the ICs to a certain ERP." Link to the toolbox: http://53450283.de.strato-hosting.eu/jrw/lab/e_compass.htm Paper: Wessel, J. R., & Ullsperger, M. (2011). Selection of independent components representing event-related brain potentials: a data-driven approach for greater objectivity. Neuroimage, 54(3), 2105-2115. https://doi.org/10.1016/j.neuroimage.2010.10.033 I have only theoretical experience with the toolbox as I only learned about it in a workshop and did not yet have the time to test and implement it in my personal FieldTrip workflow (even though it is on my ever growing to-do list). So far it looked like a useful thing to try out to me, especially as code can better be reproduced than "personal judgement". Best, David Am 28.11.2018 um 10:49 schrieb Aitor Egurtzegi: > Dear researchers at Fieldtrip, > > > In order to make my work more reproducible, I would like to > automatically reject ICs instead of doing visual inspection and > rejection of the components. Unfortunately, I haven't found any > documentation for such thing. Is there a way to do it in Fieldtrip? > > Best, > Aitor > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 -- Dr. David Schubring General & Biological Psychology University of Konstanz Room C524 P.O. Box 36 78457 Konstanz Phone: +49-(0)7531-88-5350 Homepage: https://gpbp.uni-konstanz.de/people-page/david-schubring _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 From y.visser at hotmail.com Fri Nov 30 10:47:03 2018 From: y.visser at hotmail.com (Yvonne Visser) Date: Fri, 30 Nov 2018 09:47:03 +0000 Subject: [FieldTrip] Cluster based permutation test interpretation Message-ID: Dear all, Thank you for welcoming me to the discussion list, my name is Yvonne Visser and I currently work as a research assistant with dr. Aaron Schurger at Neurospin. During my masters program I learned about cluster based permutation tests for electrophysiological data and distinctly remember how from this type of test one can not conclude that a particular cluster is significant (in line with what is said on the fieldtrip website here, http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test) We are currently using the cluster based permutation test in the analysis of our experiment, but we are a bit confused on how to interpret the results from our test. To give you a short introduction to our experiment: we are looking for a relationship between a behavioural variable and our collected EEG data. So we computed the grand average time frequency spectrum in a single channel of the time bins of interest. Then, we correlated each time/frequency point in this 2d matrix with the behavioural variable in that trial. This resulted in a correlation matrix like you can see in attachment1_correlationmatrix. As you can see, we also computed clusters of time/frequency points with p<0.05. After computing the permutations, we found that the biggest "real" cluster is bigger than any of the permuted clusters. Now, we would like to conclude something from this result about which frequency band at what time is correlated to our behavioural variable. We found a fieldtrip function called ft_clusterplot that does seem to suggest that you can highlight a specific cluster it if it survives the test, but isn't that exactly what my lectures and the webpage say we should not do? Can we say that activity in the alpha band around -0.75 to 0 (where the biggest cluster is located) is correlated to the size of the movement? Or should we not conclude something about which cluster is significant and can we only say that some time frequency power is correlated to our behavioural variable? If the second is true, do you have any advice for us to make the interpretation more specific? Thank you so much in advance, and please let us know if anything is unclear. Kind regards, Yvonne & Aaron. -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: attachment1_correlationmatrix.jpg Type: image/jpeg Size: 61990 bytes Desc: attachment1_correlationmatrix.jpg URL: From aitor.martinezegurcegui at uzh.ch Fri Nov 30 14:42:39 2018 From: aitor.martinezegurcegui at uzh.ch (Aitor Egurtzegi) Date: Fri, 30 Nov 2018 14:42:39 +0100 Subject: [FieldTrip] automatic IC rejection In-Reply-To: <48E6E95C4AB29E4BAD7908051F5C17EF016E1E31BC@MBXP07.ds.man.ac.uk> References: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> <9b277ef4-067a-9a61-a11b-da7d504b9717@uni-konstanz.de> <48E6E95C4AB29E4BAD7908051F5C17EF016E1E31BC@MBXP07.ds.man.ac.uk> Message-ID: <6630afcc-003d-3ecf-2d53-d0174a75ea09@uzh.ch> Dear Jason, Thanks a lot for your reply. Is there a Fieldtrip method already implemented to run such temporal correlation? or would I have to do the implementation in raw Matlab? Thanks in advance, Aitor On 11/30/18 2:05 AM, Jason Taylor wrote: > Hi Aitor, > > If you have an 'objective' measure of the artefact you're trying to remove (e.g., VEOG for blinks), a relatively straightforward method is to run a temporal correlation between each IC's activation time-course and the artefact channel's time-course. You can then reject any IC with a correlation higher than some threshold, or with a Z-score (r value relative to the distribution of IC r values) above some threshold. This tends to work very well for identifying blinks, and fairly well for eye-movements (*EOG), and can work for pulse artefact if you have recorded ECG. To avoid spurious correlations due to high-frequency noise, you can filter (e.g., 1 to 30 Hz) the component and artefact signals before correlating them (but obviously go back to the original unfiltered signals to continue with your analysis). > > Best wishes, > Jason > > > -----Original Message----- > From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of David Schubring > Sent: 28 November 2018 12:47 > To: fieldtrip at science.ru.nl; aitor.martinezegurcegui at uzh.ch > Subject: Re: [FieldTrip] automatic IC rejection > > Dear Aitor, > > the closest thing I know of for a data-driven approach of selecting > independent components is COMPASS, quote: > > "COMPASS is a MATLAB and EEGLAB based algorithm with the purpose of > providing the user with a convenient technique for automatic Independent > Component (IC) selection with respect to the contributions of the ICs to > a certain ERP." > > Link to the toolbox: > > http://53450283.de.strato-hosting.eu/jrw/lab/e_compass.htm > > Paper: > > Wessel, J. R., & Ullsperger, M. (2011). Selection of independent > components representing event-related brain potentials: a data-driven > approach for greater objectivity. Neuroimage, 54(3), 2105-2115. > https://doi.org/10.1016/j.neuroimage.2010.10.033 > > I have only theoretical experience with the toolbox as I only learned > about it in a workshop and did not yet have the time to test and > implement it in my personal FieldTrip workflow (even though it is on my > ever growing to-do list). So far it looked like a useful thing to try > out to me, especially as code can better be reproduced than "personal > judgement". > > Best, > David > > Am 28.11.2018 um 10:49 schrieb Aitor Egurtzegi: >> Dear researchers at Fieldtrip, >> >> >> In order to make my work more reproducible, I would like to >> automatically reject ICs instead of doing visual inspection and >> rejection of the components. Unfortunately, I haven't found any >> documentation for such thing. Is there a way to do it in Fieldtrip? >> >> Best, >> Aitor >> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 From jason.taylor at manchester.ac.uk Fri Nov 30 18:08:22 2018 From: jason.taylor at manchester.ac.uk (Jason Taylor) Date: Fri, 30 Nov 2018 17:08:22 +0000 Subject: [FieldTrip] automatic IC rejection In-Reply-To: <6630afcc-003d-3ecf-2d53-d0174a75ea09@uzh.ch> References: <5e257bfd-121e-d151-08d0-3c31ff0ada5a@uzh.ch> <9b277ef4-067a-9a61-a11b-da7d504b9717@uni-konstanz.de> <48E6E95C4AB29E4BAD7908051F5C17EF016E1E31BC@MBXP07.ds.man.ac.uk> <6630afcc-003d-3ecf-2d53-d0174a75ea09@uzh.ch> Message-ID: <48E6E95C4AB29E4BAD7908051F5C17EF016E1E3CBD@MBXP07.ds.man.ac.uk> Hi Aitor, No, not that I know of. I generally use a hacky combination of SPM, fieldtrip, and EEGLAB functions, but if you've already run ICA, you could accomplish what I suggested with some standard matlab functions. Best wishes, Jason -----Original Message----- From: Aitor Egurtzegi [mailto:aitor.martinezegurcegui at uzh.ch] Sent: 30 November 2018 13:43 To: Jason Taylor; FieldTrip discussion list Subject: Re: [FieldTrip] automatic IC rejection Dear Jason, Thanks a lot for your reply. Is there a Fieldtrip method already implemented to run such temporal correlation? or would I have to do the implementation in raw Matlab? Thanks in advance, Aitor On 11/30/18 2:05 AM, Jason Taylor wrote: > Hi Aitor, > > If you have an 'objective' measure of the artefact you're trying to remove (e.g., VEOG for blinks), a relatively straightforward method is to run a temporal correlation between each IC's activation time-course and the artefact channel's time-course. You can then reject any IC with a correlation higher than some threshold, or with a Z-score (r value relative to the distribution of IC r values) above some threshold. This tends to work very well for identifying blinks, and fairly well for eye-movements (*EOG), and can work for pulse artefact if you have recorded ECG. To avoid spurious correlations due to high-frequency noise, you can filter (e.g., 1 to 30 Hz) the component and artefact signals before correlating them (but obviously go back to the original unfiltered signals to continue with your analysis). > > Best wishes, > Jason > > > -----Original Message----- > From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of David Schubring > Sent: 28 November 2018 12:47 > To: fieldtrip at science.ru.nl; aitor.martinezegurcegui at uzh.ch > Subject: Re: [FieldTrip] automatic IC rejection > > Dear Aitor, > > the closest thing I know of for a data-driven approach of selecting > independent components is COMPASS, quote: > > "COMPASS is a MATLAB and EEGLAB based algorithm with the purpose of > providing the user with a convenient technique for automatic Independent > Component (IC) selection with respect to the contributions of the ICs to > a certain ERP." > > Link to the toolbox: > > http://53450283.de.strato-hosting.eu/jrw/lab/e_compass.htm > > Paper: > > Wessel, J. R., & Ullsperger, M. (2011). Selection of independent > components representing event-related brain potentials: a data-driven > approach for greater objectivity. Neuroimage, 54(3), 2105-2115. > https://doi.org/10.1016/j.neuroimage.2010.10.033 > > I have only theoretical experience with the toolbox as I only learned > about it in a workshop and did not yet have the time to test and > implement it in my personal FieldTrip workflow (even though it is on my > ever growing to-do list). So far it looked like a useful thing to try > out to me, especially as code can better be reproduced than "personal > judgement". > > Best, > David > > Am 28.11.2018 um 10:49 schrieb Aitor Egurtzegi: >> Dear researchers at Fieldtrip, >> >> >> In order to make my work more reproducible, I would like to >> automatically reject ICs instead of doing visual inspection and >> rejection of the components. Unfortunately, I haven't found any >> documentation for such thing. Is there a way to do it in Fieldtrip? >> >> Best, >> Aitor >> >> _______________________________________________ >> fieldtrip mailing list >> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip >> https://doi.org/10.1371/journal.pcbi.1002202 From abela.eugenio at gmail.com Fri Nov 30 18:25:51 2018 From: abela.eugenio at gmail.com (Eugenio Abela) Date: Fri, 30 Nov 2018 17:25:51 +0000 Subject: [FieldTrip] HELP: import simple data from txt file - non supported format, no header In-Reply-To: References: Message-ID: <666ECAD5-0675-42B3-AF4F-205F1747A735@gmail.com> Hi Tony Here’s a pretty quick and dirty fix - have a careful look to make sure it does what you want. You need FieldTrip installed and in the path; it works on my MATLAB2016a. Others on the list might have better ideas, or spot obvious errors. Good luck eugenio % Import you data D = importdata('Pressure_3_6.txt',' '); % Fill in FieldTrip data structure (check out ft_datatype_raw) % I assume columns 4, 5 are EEG, 6,7 EMG. % Channels go in rows, time in columns. data = struct(); data.label = {'EEG-1';'EEG-2';'EMG-1';'EMG-2'}; data.fsample = 250; data.time = {1:length(D)}; data.trial = {D(:,4:7)’}; % Check out how it looks cfg = []; cfg.viemode = 'vertical'; ft_databrowser(cfg,data); On 29 Nov 2018, at 17:22, Hoang Truong wrote: Dear community, This is Tony, from CS Department, University of Colorado Boulder. I am new to eeg analysis and fieldtrip. Currently I work on recording eeg and emg data using our own hardware with 2 eeg and 2 emg channels respectively. Since we use our custom hardware, the data are saved in a simple format of txt file with 7 columns (example data on Dropbox: link ): 3 first columns are data from environment sensors, the next 4 are eeg and emg data (250Hz sampling rate, 42s recording) I would like to ask what will be a proper way for me to import this data into fieldtrip. I checked the faq about importing custom data but I do not understand how to make custom readheader, readdata and readevent function. Any help would be appreciated (suggestions, code examples, etc). Thank you so much. Sincerely, Tony _______________________________________________ fieldtrip mailing list https://mailman.science.ru.nl/mailman/listinfo/fieldtrip https://doi.org/10.1371/journal.pcbi.1002202 -------------- next part -------------- An HTML attachment was scrubbed... URL: From Hoang.Truong at colorado.edu Fri Nov 30 18:47:10 2018 From: Hoang.Truong at colorado.edu (Hoang Truong) Date: Fri, 30 Nov 2018 10:47:10 -0700 Subject: [FieldTrip] HELP: import simple data from txt file - non supported format, no header In-Reply-To: <666ECAD5-0675-42B3-AF4F-205F1747A735@gmail.com> References: <666ECAD5-0675-42B3-AF4F-205F1747A735@gmail.com> Message-ID: Thanks for your prompt help, Eugenio and Stephen !!! I can view the data with Eugenio's code w MATLAB2018a. I'll continue to work from there. @Stephen: I build a small hardware piece that can capture multimodal biosignal and integrate this into some wearable prototypes. Sincerely, Tony On Fri, Nov 30, 2018 at 10:25 AM Eugenio Abela wrote: > Hi Tony > > Here’s a pretty quick and dirty fix - have a careful look to make sure it > does what you want. You need FieldTrip installed and in the path; it works > on my MATLAB2016a. Others on the list might have better ideas, or spot > obvious errors. > > Good luck > eugenio > > % Import you data > D = importdata('Pressure_3_6.txt',' '); > > > % Fill in FieldTrip data structure (check out ft_datatype_raw) > % I assume columns 4, 5 are EEG, 6,7 EMG. > % Channels go in rows, time in columns. > data = struct(); > data.label = {'EEG-1';'EEG-2';'EMG-1';'EMG-2'}; > data.fsample = 250; > data.time = {1:length(D)}; > data.trial = {D(:,4:7)’}; > > > % Check out how it looks > cfg = []; > cfg.viemode = 'vertical'; > ft_databrowser(cfg,data); > > > > On 29 Nov 2018, at 17:22, Hoang Truong wrote: > > Dear community, > > This is Tony, from CS Department, University of Colorado Boulder. I am new > to eeg analysis and fieldtrip. Currently I work on recording eeg and emg > data using our own hardware with 2 eeg and 2 emg channels respectively. > Since we use our custom hardware, the data are saved in a simple format of > txt file with 7 columns (example data on Dropbox: link > ): 3 > first columns are data from environment sensors, the next 4 are eeg and emg > data (250Hz sampling rate, 42s recording) > I would like to ask what will be a proper way for me to import this data > into fieldtrip. I checked the faq about importing custom data but I do not > understand how to make custom readheader, readdata and readevent function. > Any help would be appreciated (suggestions, code examples, etc). > Thank you so much. > > Sincerely, > Tony > > _______________________________________________ > fieldtrip mailing list > https://mailman.science.ru.nl/mailman/listinfo/fieldtrip > https://doi.org/10.1371/journal.pcbi.1002202 > > -------------- next part -------------- An HTML attachment was scrubbed... URL: