From Sebastian.Neudek at med.uni-duesseldorf.de Sat Oct 1 08:48:56 2022
From: Sebastian.Neudek at med.uni-duesseldorf.de (Sebastian.Neudek at med.uni-duesseldorf.de)
Date: Sat, 1 Oct 2022 06:48:56 +0000
Subject: [FieldTrip] [Extern] - Re: Calculating the variance of a group
with given variances of each subject
In-Reply-To: <6254A492-7A5C-4EF8-AA76-ADCF4AC39C68@donders.ru.nl>
References: <2ff41439ebe54ba19b82b2cbcdd82bae@med.uni-duesseldorf.de>,
<6254A492-7A5C-4EF8-AA76-ADCF4AC39C68@donders.ru.nl>
Message-ID:
Hi Jan-Mathijs,
thank you for your suggestion of mixed-effect models. Probably those are the perfect fit for my case.
But I still think the variance implementation in fieldtrip in ft_timelockgrandaverage is not correct (only calculating the variance of means of subjects as group variance). I looked a little bit deeper into the implementations and it seems the degress of freedom are the number of trials. Therefore the implemented mean in ft_timelockgrandaverage already is the mean from a pooled variance.
Here you have an example matlab script of which I think the variance of ft_timelockgrandaverage leads to wrong statistics. There are only four subjects who belong to 2 groups (Subject 1 and 2 in group A, Subject 3 and 4 in group B). The trials from all subjects are drawn from the same distribution. All have same mean and same variance. Therefore the real group means are the same and don't differ. Here a t-test is evaluated, to test whether the means of the groups differ.
rng(0,'twister');
% all people have the same number of trials and the same(!) mean
N = 100;
n1 = N; % Person 1 belongs to group A
mu1 = 0;
sigma1 = 1;
n2 = N; % Person 2 belongs to group A
mu2 = 0;
sigma2 = 1;
n3 = N; % Person 3 belongs to group B
mu3 = 0;
sigma3 = 1;
n4 = N; % Person 4 belongs to group B
mu4 = 0;
sigma4 = 1;
% normal distributed random numbers as trials for each person
trials_1 = mu1 + randn([1 n1])*sigma1;
trials_2 = mu2 + randn([1 n2])*sigma2;
trials_3 = mu3 + randn([1 n3])*sigma3;
trials_4= mu4 + randn([1 n4])*sigma4;
% calculate mean and variance for each person
avg1 = mean(trials_1);
avg2 = mean(trials_2);
avg3 = mean(trials_3);
avg4 = mean(trials_4);
var1 = var(trials_1);
var2 = var(trials_2);
var3 = var(trials_3);
var4 = var(trials_4);
% real mean and variance
avg_A = mean([trials_1 trials_2]); % avg for group A
avg_B = mean([trials_3 trials_4]); % avg for group B
var_A = var([trials_1 trials_2]);
var_B = var([trials_3 trials_4]);
% mean and variance of fieldtrip
avg_A_ft = mean([avg1 avg2]);
avg_B_ft = mean([avg3 avg4]);
var_A_ft = var([avg1 avg2]);
var_B_ft = var([avg3 avg4]);
% now a t-test, to check if the mean of group A: {person 1 and person 2}
% and group B: {person 3 and person 4} are different
% H0: both groups share the same mean
% H1: both groups have different mean
% https://en.wikipedia.org/wiki/Student%27s_t-test
alpha = 0.05;
t = avg_A-avg_B/(sqrt((var_A+var_B)/2)*sqrt(2/(n1+n2))); % this formula is only correct for same group sizes
%p = 1-tcdf(t,n1+n2+n3+n4-4) % right tailed t-test
%p = tcdf(t,n1+n2+n3+n4-4) % left tailed t-test
p = 2*tcdf(-abs(t),n1+n2+n3+n4-4); % two tailed t-test
fprintf('Result:\n');
if p < alpha
fprintf('H0 rejected. Significant difference of means between Group A and B (P = %.4f, T = %.4f)\n\n',p ,t);
else
fprintf('H0 not rejected. No significant difference of means between Group A and B (P = %.4f, T = %.4f)\n\n',p ,t);
end
% t value with fieldtrips variance
t_ft = avg_A_ft-avg_B_ft/(sqrt((var_A_ft+var_B_ft)/2)*sqrt(2/(n1+n2))); % this formula is only correct for same group sizes
% p_ft = 1-tcdf(t_ft,n1+n2+n3+n4-4) % right tailed t-test
% p_ft = tcdf(t_ft,n1+n2+n3+n4-4) % left tailed t-test
p_ft = 2*tcdf(-abs(t_ft),n1+n2+n3+n4-4); % two tailed t-test
fprintf('Result using variance of ft_timelockgrandaverage:\n');
if p_ft < alpha
fprintf('H0 rejected. Significant difference of means between Group A and B (P = %.4f, T = %.4f)\n\n',p_ft ,t_ft);
else
fprintf('H0 not rejected. No significant difference of means between Group A and B (P = %.4f, T = %.4f)\n\n',p_ft ,t_ft);
end
Best,
Sebastian
________________________________
Von: fieldtrip im Auftrag von Schoffelen, J.M. (Jan Mathijs) via fieldtrip
Gesendet: Freitag, 30. September 2022 13:46:40
An: FieldTrip discussion list
Cc: Schoffelen, J.M. (Jan Mathijs)
Betreff: [Extern] - Re: [FieldTrip] Calculating the variance of a group with given variances of each subject
Hi Sebastian,
I would say that for visualization purposes (and for traditional parametric second-level statistics) you are interested in the variance across your units-of-observation. In this case across subjects. This is indeed what ft_timelockgrandaverage gives you. If you are into more sophisticated estimates, it smells a bit as if you want to be moving into the direction of mixed-effect models, which are gaining quite a bit of momentum these days. Since I am a bit old-school I have not eaten any cheese from that (http://www.dwotd.nl/2008/02/360-daar-heb-ik-geen-kaas-van-gegeten.html), so perhaps somebody else wants to chime in here.
Best wishes,
Jan-Mathijs
On 26 Sep 2022, at 21:36, Sebastian Neudek via fieldtrip > wrote:
Hi all,
I am currently writing my master's thesis using FieldTrip and might need a bit of your expertise for a little hurdle I encountered.
First a few words about what I want to do:
I have data from a MEG-go/nogo study: There are multiple subjects and multiple trials for each condition. I now want to plot the ERF for each condition averaged across all subjects. But together with the ERF I also want to plot the standard error or variance or confidence interval or standard deviation. Something like this(Figure 3a):
https://www.researchgate.net/figure/ERP-results-including-P3-amplitudes-a-Grand-average-and-standard-errors-of-ERP_fig2_327651209
Independent which of these measures I finally use, I need to calculate the variance of the ERF at each timepoint.
To do this I first used ft_timelockanalysis to calculate the averages of each subject. ft_timelockanalysis returns not only the average, but also the variance of the ERF. Afterwards I use ft_timelockgrandaverage to calculate the group average of the ERF.
And this is where my troubles begin. I get how ft_timelockgrandaverage calulates the new average: It averages over the mean values of the subjects (it also accounts for the degrees of freedom). But for the variance it just calculates the variance of the means. I thought, it will also account for the variances of each subject.
In the extreme case it leads to the following:
Imagine you have two subjects and you measure very close averages avg(sub1)=1 and avg(sub2)=1.0001. But their variances are extremly high: var(sub1)=var(sub2)=100. Then the variance calculated by ft_timelockgrandaverage is almost zero, when in reality it should be something like 100.
Maybe this should be noted in ft_timelockgrandaverage, because it can lead to wrong statistics if the user isn't aware of this effect.
Back to my task:
This is surely not the variance I want to use for my ERF plots and therefore I searched for a different calculation.
I found on wikipedia a formula for so called 'pooled variance' (at the bottom of the page for sample-based statistics):
https://en.wikipedia.org/wiki/Pooled_variance
But I am not 100% happy with this pooled variance.
First of all the deegrees of freedom is not accounted for. Then there is a different mean calculated than in ft_timelockgrandaverage: The mean is accounting for the number of trials for each subject. I don't know if the different mean is a pro or a con (On the one hand, means with a higher precisision because of a higher number of trials are weighted higher than means with a lower trial number. On the other hand, each subject on the group should be weighted same).
I also found a second formula on stackexchange, which also calculates a variances of variances:
https://stats.stackexchange.com/questions/300392/calculate-the-variance-from-variances
Therefore I am uncertain which formula to use, because as it seems, they differ.
I hope somebody of you is a little more experienced in statistics than I am and can help me out finding the correct or best calculation for this variance. Best case would be if it is already implemented in fieldtrip and I only missed it, but I can also implement it myself.
Best,
Sebastian
_______________________________________________
fieldtrip mailing list
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
https://urldefense.com/v3/__https://doi.org/10.1371/journal.pcbi.1002202__;!!HJOPV4FYYWzcc1jazlU!4AKDCxWgDNOVGlWwimPV3Sc-5suVMj59sPhwC-eHwI7h0FQr10-YxE5fzliZsQft3R0I3s5Swpei12UfNPYgMdNHwshEvUdau-41SA$
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From janmathijs.schoffelen at donders.ru.nl Sat Oct 1 17:21:46 2022
From: janmathijs.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Sat, 1 Oct 2022 15:21:46 +0000
Subject: [FieldTrip] Calculating the variance of a group with given
variances of each subject
In-Reply-To:
References: <2ff41439ebe54ba19b82b2cbcdd82bae@med.uni-duesseldorf.de>
<6254A492-7A5C-4EF8-AA76-ADCF4AC39C68@donders.ru.nl>
Message-ID: <7A9049D6-7B43-495D-97B4-7370E458ED60@donders.ru.nl>
Hi Sebastian,
I don’t understand why you think that the variance of ft_timelockgrandaverage leads to wrong statistics. As I said in an earlier e-mail, the traditional way to do second-level statistics is to compute the variance of the mean-per-subject, and not to compute the variance of the individual-trials-concatenated-across-subjects.
The computation that you call ‘real mean and variance’ assume the unit of observation to be the trials, while the unit-of-observation that ft_timelockgrandaverage uses for the mean and variance computation is the subject. While the mean is not going to change (assuming the same number of trials per subject), the variance will turn out differently, because that results from a non-linear operation (squaring).
In your numeric example, the 'FT-code' leads to a rejection of the null-hypothesis due to the fact that you use the incorrect n1 and n2 in the numerator of the t-statistic, these should be 2 and 2, rather than 100 and 100.
Best wishes,
Jan-Mathijs
On 1 Oct 2022, at 08:48, Sebastian.Neudek at med.uni-duesseldorf.de wrote:
Hi Jan-Mathijs,
thank you for your suggestion of mixed-effect models. Probably those are the perfect fit for my case.
But I still think the variance implementation in fieldtrip in ft_timelockgrandaverage is not correct (only calculating the variance of means of subjects as group variance). I looked a little bit deeper into the implementations and it seems the degress of freedom are the number of trials. Therefore the implemented mean in ft_timelockgrandaverage already is the mean from a pooled variance.
Here you have an example matlab script of which I think the variance of ft_timelockgrandaverage leads to wrong statistics. There are only four subjects who belong to 2 groups (Subject 1 and 2 in group A, Subject 3 and 4 in group B). The trials from all subjects are drawn from the same distribution. All have same mean and same variance. Therefore the real group means are the same and don't differ. Here a t-test is evaluated, to test whether the means of the groups differ.
rng(0,'twister');
% all people have the same number of trials and the same(!) mean
N = 100;
n1 = N; % Person 1 belongs to group A
mu1 = 0;
sigma1 = 1;
n2 = N; % Person 2 belongs to group A
mu2 = 0;
sigma2 = 1;
n3 = N; % Person 3 belongs to group B
mu3 = 0;
sigma3 = 1;
n4 = N; % Person 4 belongs to group B
mu4 = 0;
sigma4 = 1;
% normal distributed random numbers as trials for each person
trials_1 = mu1 + randn([1 n1])*sigma1;
trials_2 = mu2 + randn([1 n2])*sigma2;
trials_3 = mu3 + randn([1 n3])*sigma3;
trials_4= mu4 + randn([1 n4])*sigma4;
% calculate mean and variance for each person
avg1 = mean(trials_1);
avg2 = mean(trials_2);
avg3 = mean(trials_3);
avg4 = mean(trials_4);
var1 = var(trials_1);
var2 = var(trials_2);
var3 = var(trials_3);
var4 = var(trials_4);
% real mean and variance
avg_A = mean([trials_1 trials_2]); % avg for group A
avg_B = mean([trials_3 trials_4]); % avg for group B
var_A = var([trials_1 trials_2]);
var_B = var([trials_3 trials_4]);
% mean and variance of fieldtrip
avg_A_ft = mean([avg1 avg2]);
avg_B_ft = mean([avg3 avg4]);
var_A_ft = var([avg1 avg2]);
var_B_ft = var([avg3 avg4]);
% now a t-test, to check if the mean of group A: {person 1 and person 2}
% and group B: {person 3 and person 4} are different
% H0: both groups share the same mean
% H1: both groups have different mean
% https://en.wikipedia.org/wiki/Student%27s_t-test
alpha = 0.05;
t = avg_A-avg_B/(sqrt((var_A+var_B)/2)*sqrt(2/(n1+n2))); % this formula is only correct for same group sizes
%p = 1-tcdf(t,n1+n2+n3+n4-4) % right tailed t-test
%p = tcdf(t,n1+n2+n3+n4-4) % left tailed t-test
p = 2*tcdf(-abs(t),n1+n2+n3+n4-4); % two tailed t-test
fprintf('Result:\n');
if p < alpha
fprintf('H0 rejected. Significant difference of means between Group A and B (P = %.4f, T = %.4f)\n\n',p ,t);
else
fprintf('H0 not rejected. No significant difference of means between Group A and B (P = %.4f, T = %.4f)\n\n',p ,t);
end
% t value with fieldtrips variance
t_ft = avg_A_ft-avg_B_ft/(sqrt((var_A_ft+var_B_ft)/2)*sqrt(2/(n1+n2))); % this formula is only correct for same group sizes
% p_ft = 1-tcdf(t_ft,n1+n2+n3+n4-4) % right tailed t-test
% p_ft = tcdf(t_ft,n1+n2+n3+n4-4) % left tailed t-test
p_ft = 2*tcdf(-abs(t_ft),n1+n2+n3+n4-4); % two tailed t-test
fprintf('Result using variance of ft_timelockgrandaverage:\n');
if p_ft < alpha
fprintf('H0 rejected. Significant difference of means between Group A and B (P = %.4f, T = %.4f)\n\n',p_ft ,t_ft);
else
fprintf('H0 not rejected. No significant difference of means between Group A and B (P = %.4f, T = %.4f)\n\n',p_ft ,t_ft);
end
Best,
Sebastian
________________________________
Von: fieldtrip > im Auftrag von Schoffelen, J.M. (Jan Mathijs) via fieldtrip >
Gesendet: Freitag, 30. September 2022 13:46:40
An: FieldTrip discussion list
Cc: Schoffelen, J.M. (Jan Mathijs)
Betreff: [Extern] - Re: [FieldTrip] Calculating the variance of a group with given variances of each subject
Hi Sebastian,
I would say that for visualization purposes (and for traditional parametric second-level statistics) you are interested in the variance across your units-of-observation. In this case across subjects. This is indeed what ft_timelockgrandaverage gives you. If you are into more sophisticated estimates, it smells a bit as if you want to be moving into the direction of mixed-effect models, which are gaining quite a bit of momentum these days. Since I am a bit old-school I have not eaten any cheese from that (http://www.dwotd.nl/2008/02/360-daar-heb-ik-geen-kaas-van-gegeten.html), so perhaps somebody else wants to chime in here.
Best wishes,
Jan-Mathijs
On 26 Sep 2022, at 21:36, Sebastian Neudek via fieldtrip > wrote:
Hi all,
I am currently writing my master's thesis using FieldTrip and might need a bit of your expertise for a little hurdle I encountered.
First a few words about what I want to do:
I have data from a MEG-go/nogo study: There are multiple subjects and multiple trials for each condition. I now want to plot the ERF for each condition averaged across all subjects. But together with the ERF I also want to plot the standard error or variance or confidence interval or standard deviation. Something like this(Figure 3a):
https://www.researchgate.net/figure/ERP-results-including-P3-amplitudes-a-Grand-average-and-standard-errors-of-ERP_fig2_327651209
Independent which of these measures I finally use, I need to calculate the variance of the ERF at each timepoint.
To do this I first used ft_timelockanalysis to calculate the averages of each subject. ft_timelockanalysis returns not only the average, but also the variance of the ERF. Afterwards I use ft_timelockgrandaverage to calculate the group average of the ERF.
And this is where my troubles begin. I get how ft_timelockgrandaverage calulates the new average: It averages over the mean values of the subjects (it also accounts for the degrees of freedom). But for the variance it just calculates the variance of the means. I thought, it will also account for the variances of each subject.
In the extreme case it leads to the following:
Imagine you have two subjects and you measure very close averages avg(sub1)=1 and avg(sub2)=1.0001. But their variances are extremly high: var(sub1)=var(sub2)=100. Then the variance calculated by ft_timelockgrandaverage is almost zero, when in reality it should be something like 100.
Maybe this should be noted in ft_timelockgrandaverage, because it can lead to wrong statistics if the user isn't aware of this effect.
Back to my task:
This is surely not the variance I want to use for my ERF plots and therefore I searched for a different calculation.
I found on wikipedia a formula for so called 'pooled variance' (at the bottom of the page for sample-based statistics):
https://en.wikipedia.org/wiki/Pooled_variance
But I am not 100% happy with this pooled variance.
First of all the deegrees of freedom is not accounted for. Then there is a different mean calculated than in ft_timelockgrandaverage: The mean is accounting for the number of trials for each subject. I don't know if the different mean is a pro or a con (On the one hand, means with a higher precisision because of a higher number of trials are weighted higher than means with a lower trial number. On the other hand, each subject on the group should be weighted same).
I also found a second formula on stackexchange, which also calculates a variances of variances:
https://stats.stackexchange.com/questions/300392/calculate-the-variance-from-variances
Therefore I am uncertain which formula to use, because as it seems, they differ.
I hope somebody of you is a little more experienced in statistics than I am and can help me out finding the correct or best calculation for this variance. Best case would be if it is already implemented in fieldtrip and I only missed it, but I can also implement it myself.
Best,
Sebastian
_______________________________________________
fieldtrip mailing list
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
https://urldefense.com/v3/__https://doi.org/10.1371/journal.pcbi.1002202__;!!HJOPV4FYYWzcc1jazlU!4AKDCxWgDNOVGlWwimPV3Sc-5suVMj59sPhwC-eHwI7h0FQr10-YxE5fzliZsQft3R0I3s5Swpei12UfNPYgMdNHwshEvUdau-41SA$
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From fewaster at gmail.com Tue Oct 4 02:59:41 2022
From: fewaster at gmail.com (fewaster at gmail.com)
Date: Tue, 4 Oct 2022 09:59:41 +0900
Subject: [FieldTrip] Calculating band power or power spectral density
with ft_freqanalysis
References: <66CD4134-CA40-464D-9E10-511FBDA5B28B@hxcore.ol>,
Message-ID: <2022100409594074344911@gmail.com>
Thank you, Jan-Mathijs!
I still have two questions:
1. about average bandpower.
Is your method for calculating average bandpower different from that by integral of PSD?
2. about total band power.
How to calculate total band power instead of average band power in fieldtrip?
Best,
Zhiwei
fewaster at gmail.com
From: Schoffelen, J.M. \(Jan Mathijs\) via fieldtrip
Date: 2022-09-06 22:03
To: FieldTrip discussion list
CC: Schoffelen, J.M. \(Jan Mathijs\)
Subject: Re: [FieldTrip] Calculating band power or power spectral density with ft_freqanalysis
Hi Zhiwei Fan,
Averaging across a spectral band these days is done with the ft_selectdata function:
cfg = [];
cfg.frequency = [low high]
cfg.avgoverfreq = ‘yes’;
dataout = ft_freqanalysis(cfg, datain);
Good luck,
Jan-Mathijs
On 2 Sep 2022, at 05:21, Francis (Zhiwei Fan) via fieldtrip wrote:
Dear All, I am attempting to calculate band power or power spectral density (then I can use bandpower() in Matlab to calculate band power) withft_freqanalysis but it seems does provide this function. I have found a similar question in https://mailman.science.ru.nl/pipermail/fieldtrip/2020-February/039790.html However, no one has yet answered this question.I want to know how to calculate band power or power spectral density in Fieldtrip. Thanks in advance for your help,Francis Zhiwei Fan, Ph.D.Post-doc | Abe labhttps://www.u.tsukuba.ac.jp/~abe.takashi.gp/
_______________________________________________
fieldtrip mailing list
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
https://urldefense.com/v3/__https://doi.org/10.1371/journal.pcbi.1002202__;!!HJOPV4FYYWzcc1jazlU!7fKm1v4hrZvcmNtCLJkiTTxFyctgS4iO5j_7yhGZHoryGlNv83zTXIjWs27mINTVdr-oPqYruYR7w8QHqUiUD4hKaafzhZ0pCSAEuA$
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From janmathijs.schoffelen at donders.ru.nl Tue Oct 4 09:13:16 2022
From: janmathijs.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Tue, 4 Oct 2022 07:13:16 +0000
Subject: [FieldTrip] Calculating band power or power spectral density
with ft_freqanalysis
In-Reply-To: <2022100409594074344911@gmail.com>
References: <66CD4134-CA40-464D-9E10-511FBDA5B28B@hxcore.ol>
<2022100409594074344911@gmail.com>
Message-ID:
Hi Zhiwei,
On 4 Oct 2022, at 02:59, fewaster at gmail.com wrote:
Thank you, Jan-Mathijs!
I still have two questions:
1. about average bandpower.
Is your method for calculating average bandpower different from that by integral of PSD?
Yes this is different. An integral is a sum, and ‘my method’ is -as the name suggests- an average.
2. about total band power.
How to calculate total band power instead of average band power in fieldtrip?
What about multiplying the average with the number of frequency bins, assuming that with ’total band power’ you mean the same as ‘integral of PSD'?
Best wishes and happy computing,
Jan-Mathijs
Best,
Zhiwei
________________________________
fewaster at gmail.com
From: Schoffelen, J.M. \(Jan Mathijs\) via fieldtrip
Date: 2022-09-06 22:03
To: FieldTrip discussion list
CC: Schoffelen, J.M. \(Jan Mathijs\)
Subject: Re: [FieldTrip] Calculating band power or power spectral density with ft_freqanalysis
Hi Zhiwei Fan,
Averaging across a spectral band these days is done with the ft_selectdata function:
cfg = [];
cfg.frequency = [low high]
cfg.avgoverfreq = ‘yes’;
dataout = ft_freqanalysis(cfg, datain);
Good luck,
Jan-Mathijs
On 2 Sep 2022, at 05:21, Francis (Zhiwei Fan) via fieldtrip > wrote:
Dear All,
I am attempting to calculate band power or power spectral density (then I can use bandpower() in Matlab to calculate band power) with
ft_freqanalysis but it seems does provide this function. I have found a similar question in
https://mailman.science.ru.nl/pipermail/fieldtrip/2020-February/039790.html
However, no one has yet answered this question.
I want to know how to calculate band power or power spectral density in Fieldtrip.
Thanks in advance for your help,
Francis
Zhiwei Fan, Ph.D.
Post-doc | Abe lab
https://www.u.tsukuba.ac.jp/~abe.takashi.gp/
_______________________________________________
fieldtrip mailing list
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
https://urldefense.com/v3/__https://doi.org/10.1371/journal.pcbi.1002202__;!!HJOPV4FYYWzcc1jazlU!7fKm1v4hrZvcmNtCLJkiTTxFyctgS4iO5j_7yhGZHoryGlNv83zTXIjWs27mINTVdr-oPqYruYR7w8QHqUiUD4hKaafzhZ0pCSAEuA$
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From Sebastian.Neudek at med.uni-duesseldorf.de Tue Oct 4 11:01:25 2022
From: Sebastian.Neudek at med.uni-duesseldorf.de (Sebastian.Neudek at med.uni-duesseldorf.de)
Date: Tue, 4 Oct 2022 09:01:25 +0000
Subject: [FieldTrip] [Extern] - Re: Calculating the variance of a group
with given variances of each subject
In-Reply-To: <7A9049D6-7B43-495D-97B4-7370E458ED60@donders.ru.nl>
References: <2ff41439ebe54ba19b82b2cbcdd82bae@med.uni-duesseldorf.de>
<6254A492-7A5C-4EF8-AA76-ADCF4AC39C68@donders.ru.nl>
,
<7A9049D6-7B43-495D-97B4-7370E458ED60@donders.ru.nl>
Message-ID: <452f59ade9714c52b6455078ab30654d@med.uni-duesseldorf.de>
Hi Jan-Mathijs,
thanks for your response. Apparently my example is bad. It will also lead to different p-values if the bugs are corrected, because it violates the independece of samples. That is the reason, why the pooled variance can't be used for statistics.
I totally agree with you with the general procedure, but I had some thoughts about it, which I want to share. I will call the outcomes of this method group-mean, group-variance and the groups standard error of the mean. Also will call the average of one person a 'measurement'. Therefore the groups mean is just the mean of our measurements.
But I think there is an uncertainty to each of these measurements, which in my opinion cannot be neglegted. We can calculate these uncertainties of each measurement by calculating the standard error for each person. The uncertainty of these measurements 'propagates' if they are averaged. In our case the uncertainty for the end measurement is the average of all subjects uncertainties (I found this propagation only on the german wiki-page for propagation of uncertainty. Maybe this should be further checked).
Following the group-mean itself isn't just a value, it is smeared around the value, because we couldn't measure it any more precise. If we plot our ERF, we could change the thickness of our line according to this uncertainty. We now have calculated two errors: Once the groups standard error of the mean and also the uncertainty of our measurement (which is also an error). Therefore we can calculate a total error of our mean if we add both errors together (adding together errors is allowed according to the german wiki-page for propagation of uncertainty). This total error is the error of our ERFs. If we want to compare the ERFs of two groups, we therefore need to calculate back the variance.
With the standard procedure of only calculating the group-variance you don't really get a benefit of measuring multiple trials. With this procedure you reduce the uncertainty of your measurement by using multiple trials per subject and following the total variance of the ERF.
A suggestion would be to rename the variance field from the output ft_timelockgrandaverage to group variance and add a field for the measurement uncertainty. The old variance field would then represent the total variance of the ERF.
Is it already implemented in fieldtrip and I missed it? If not, what are your thoughts about it?
Best,
Sebastian
________________________________
Von: fieldtrip im Auftrag von Schoffelen, J.M. (Jan Mathijs) via fieldtrip
Gesendet: Samstag, 1. Oktober 2022 17:21:46
An: FieldTrip discussion list
Cc: Schoffelen, J.M. (Jan Mathijs)
Betreff: [Extern] - Re: [FieldTrip] Calculating the variance of a group with given variances of each subject
Hi Sebastian,
I don’t understand why you think that the variance of ft_timelockgrandaverage leads to wrong statistics. As I said in an earlier e-mail, the traditional way to do second-level statistics is to compute the variance of the mean-per-subject, and not to compute the variance of the individual-trials-concatenated-across-subjects.
The computation that you call ‘real mean and variance’ assume the unit of observation to be the trials, while the unit-of-observation that ft_timelockgrandaverage uses for the mean and variance computation is the subject. While the mean is not going to change (assuming the same number of trials per subject), the variance will turn out differently, because that results from a non-linear operation (squaring).
In your numeric example, the 'FT-code' leads to a rejection of the null-hypothesis due to the fact that you use the incorrect n1 and n2 in the numerator of the t-statistic, these should be 2 and 2, rather than 100 and 100.
Best wishes,
Jan-Mathijs
On 1 Oct 2022, at 08:48, Sebastian.Neudek at med.uni-duesseldorf.de wrote:
Hi Jan-Mathijs,
thank you for your suggestion of mixed-effect models. Probably those are the perfect fit for my case.
But I still think the variance implementation in fieldtrip in ft_timelockgrandaverage is not correct (only calculating the variance of means of subjects as group variance). I looked a little bit deeper into the implementations and it seems the degress of freedom are the number of trials. Therefore the implemented mean in ft_timelockgrandaverage already is the mean from a pooled variance.
Here you have an example matlab script of which I think the variance of ft_timelockgrandaverage leads to wrong statistics. There are only four subjects who belong to 2 groups (Subject 1 and 2 in group A, Subject 3 and 4 in group B). The trials from all subjects are drawn from the same distribution. All have same mean and same variance. Therefore the real group means are the same and don't differ. Here a t-test is evaluated, to test whether the means of the groups differ.
rng(0,'twister');
% all people have the same number of trials and the same(!) mean
N = 100;
n1 = N; % Person 1 belongs to group A
mu1 = 0;
sigma1 = 1;
n2 = N; % Person 2 belongs to group A
mu2 = 0;
sigma2 = 1;
n3 = N; % Person 3 belongs to group B
mu3 = 0;
sigma3 = 1;
n4 = N; % Person 4 belongs to group B
mu4 = 0;
sigma4 = 1;
% normal distributed random numbers as trials for each person
trials_1 = mu1 + randn([1 n1])*sigma1;
trials_2 = mu2 + randn([1 n2])*sigma2;
trials_3 = mu3 + randn([1 n3])*sigma3;
trials_4= mu4 + randn([1 n4])*sigma4;
% calculate mean and variance for each person
avg1 = mean(trials_1);
avg2 = mean(trials_2);
avg3 = mean(trials_3);
avg4 = mean(trials_4);
var1 = var(trials_1);
var2 = var(trials_2);
var3 = var(trials_3);
var4 = var(trials_4);
% real mean and variance
avg_A = mean([trials_1 trials_2]); % avg for group A
avg_B = mean([trials_3 trials_4]); % avg for group B
var_A = var([trials_1 trials_2]);
var_B = var([trials_3 trials_4]);
% mean and variance of fieldtrip
avg_A_ft = mean([avg1 avg2]);
avg_B_ft = mean([avg3 avg4]);
var_A_ft = var([avg1 avg2]);
var_B_ft = var([avg3 avg4]);
% now a t-test, to check if the mean of group A: {person 1 and person 2}
% and group B: {person 3 and person 4} are different
% H0: both groups share the same mean
% H1: both groups have different mean
% https://en.wikipedia.org/wiki/Student%27s_t-test
alpha = 0.05;
t = avg_A-avg_B/(sqrt((var_A+var_B)/2)*sqrt(2/(n1+n2))); % this formula is only correct for same group sizes
%p = 1-tcdf(t,n1+n2+n3+n4-4) % right tailed t-test
%p = tcdf(t,n1+n2+n3+n4-4) % left tailed t-test
p = 2*tcdf(-abs(t),n1+n2+n3+n4-4); % two tailed t-test
fprintf('Result:\n');
if p < alpha
fprintf('H0 rejected. Significant difference of means between Group A and B (P = %.4f, T = %.4f)\n\n',p ,t);
else
fprintf('H0 not rejected. No significant difference of means between Group A and B (P = %.4f, T = %.4f)\n\n',p ,t);
end
% t value with fieldtrips variance
t_ft = avg_A_ft-avg_B_ft/(sqrt((var_A_ft+var_B_ft)/2)*sqrt(2/(n1+n2))); % this formula is only correct for same group sizes
% p_ft = 1-tcdf(t_ft,n1+n2+n3+n4-4) % right tailed t-test
% p_ft = tcdf(t_ft,n1+n2+n3+n4-4) % left tailed t-test
p_ft = 2*tcdf(-abs(t_ft),n1+n2+n3+n4-4); % two tailed t-test
fprintf('Result using variance of ft_timelockgrandaverage:\n');
if p_ft < alpha
fprintf('H0 rejected. Significant difference of means between Group A and B (P = %.4f, T = %.4f)\n\n',p_ft ,t_ft);
else
fprintf('H0 not rejected. No significant difference of means between Group A and B (P = %.4f, T = %.4f)\n\n',p_ft ,t_ft);
end
Best,
Sebastian
________________________________
Von: fieldtrip > im Auftrag von Schoffelen, J.M. (Jan Mathijs) via fieldtrip >
Gesendet: Freitag, 30. September 2022 13:46:40
An: FieldTrip discussion list
Cc: Schoffelen, J.M. (Jan Mathijs)
Betreff: [Extern] - Re: [FieldTrip] Calculating the variance of a group with given variances of each subject
Hi Sebastian,
I would say that for visualization purposes (and for traditional parametric second-level statistics) you are interested in the variance across your units-of-observation. In this case across subjects. This is indeed what ft_timelockgrandaverage gives you. If you are into more sophisticated estimates, it smells a bit as if you want to be moving into the direction of mixed-effect models, which are gaining quite a bit of momentum these days. Since I am a bit old-school I have not eaten any cheese from that (http://www.dwotd.nl/2008/02/360-daar-heb-ik-geen-kaas-van-gegeten.html), so perhaps somebody else wants to chime in here.
Best wishes,
Jan-Mathijs
On 26 Sep 2022, at 21:36, Sebastian Neudek via fieldtrip > wrote:
Hi all,
I am currently writing my master's thesis using FieldTrip and might need a bit of your expertise for a little hurdle I encountered.
First a few words about what I want to do:
I have data from a MEG-go/nogo study: There are multiple subjects and multiple trials for each condition. I now want to plot the ERF for each condition averaged across all subjects. But together with the ERF I also want to plot the standard error or variance or confidence interval or standard deviation. Something like this(Figure 3a):
https://www.researchgate.net/figure/ERP-results-including-P3-amplitudes-a-Grand-average-and-standard-errors-of-ERP_fig2_327651209
Independent which of these measures I finally use, I need to calculate the variance of the ERF at each timepoint.
To do this I first used ft_timelockanalysis to calculate the averages of each subject. ft_timelockanalysis returns not only the average, but also the variance of the ERF. Afterwards I use ft_timelockgrandaverage to calculate the group average of the ERF.
And this is where my troubles begin. I get how ft_timelockgrandaverage calulates the new average: It averages over the mean values of the subjects (it also accounts for the degrees of freedom). But for the variance it just calculates the variance of the means. I thought, it will also account for the variances of each subject.
In the extreme case it leads to the following:
Imagine you have two subjects and you measure very close averages avg(sub1)=1 and avg(sub2)=1.0001. But their variances are extremly high: var(sub1)=var(sub2)=100. Then the variance calculated by ft_timelockgrandaverage is almost zero, when in reality it should be something like 100.
Maybe this should be noted in ft_timelockgrandaverage, because it can lead to wrong statistics if the user isn't aware of this effect.
Back to my task:
This is surely not the variance I want to use for my ERF plots and therefore I searched for a different calculation.
I found on wikipedia a formula for so called 'pooled variance' (at the bottom of the page for sample-based statistics):
https://en.wikipedia.org/wiki/Pooled_variance
But I am not 100% happy with this pooled variance.
First of all the deegrees of freedom is not accounted for. Then there is a different mean calculated than in ft_timelockgrandaverage: The mean is accounting for the number of trials for each subject. I don't know if the different mean is a pro or a con (On the one hand, means with a higher precisision because of a higher number of trials are weighted higher than means with a lower trial number. On the other hand, each subject on the group should be weighted same).
I also found a second formula on stackexchange, which also calculates a variances of variances:
https://stats.stackexchange.com/questions/300392/calculate-the-variance-from-variances
Therefore I am uncertain which formula to use, because as it seems, they differ.
I hope somebody of you is a little more experienced in statistics than I am and can help me out finding the correct or best calculation for this variance. Best case would be if it is already implemented in fieldtrip and I only missed it, but I can also implement it myself.
Best,
Sebastian
_______________________________________________
fieldtrip mailing list
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From janmathijs.schoffelen at donders.ru.nl Tue Oct 4 15:34:05 2022
From: janmathijs.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Tue, 4 Oct 2022 13:34:05 +0000
Subject: [FieldTrip] alpha one is looking for a senior EEG scientist
Message-ID: <6FEBA459-6D3B-4822-B626-CCC19A8B6DB2@donders.ru.nl>
Dear all,
On behalf of Ingrid Nieuwenhuis, a FieldTrip alumnus, I would like to bring the below job ad to your attention. It might be of interest to some of you.
Best wishes,
Jan-Mathijs
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Looking for Senior EEG scientist - (close to) PhD level - Location Amsterdam
Are you a methodologically strong neuroscientist with a lot of experience in M/EEG? Do you enjoy translating scientific insights into practical recommendations that customers can use? Then you might be our new Senior EEG Scientist!
About the company, Alpha.One
Alpha.One is a young Dutch company that operates in the domain where neuroscience, marketing and machine learning (AI) meet.
The Senior EEG scientist will be part of the Alpha.One science team. This team conducts neuroscience lab research using EEG and eye tracking. With these tools we generate actionable insights for customers to develop better advertising and communication. In addition, the science team is responsible for the scientific development of the AI-powered ad testing platforms expoze.io and Junbi.ai. These platforms allow users to test the effectiveness of advertisements without the need for participants.
The core values of Alpha.One are rooted-in-science, actionable-results, and what-you-see-is-what-you-get. In addition to developing marketing products, we also develop applications and products to increase social impact and diversity.
About the role
As a Senior EEG scientist you stand with one leg firmly in science. You manage the team of junior researchers and carry out the EEG and eye tracking research with them. You analyze the data according to our standardized methods, and work on optimizing the analysis pipeline. You contribute to the development of new applications based on machine learning and you are jointly responsible for supervising master students who do an internship in the science team. With the other leg you stand on the customer side in marketing. We don't expect you to have experience with this part, but you are interested in it. You will be trained in this and grow and develop this skill on the job. You learn to translate the client’s research questions into a robust research proposal. You will also work on reports and presentations in which the research findings are translated into practical recommendations for our clients.
Profile
*
You are a methodologically strong neuroscientist and enjoy translating scientific insights into actionable recommendations that customers can use.
*
You have at least three years of full-time experience in independently developing, performing and analyzing M/EEG experiments.
*
Preferably you have a PhD. Fields of study: neuroscience, cognitive psychology or a related field. But if you discovered during your PhD research that the academic system isn't for you, and didn't finish your PhD, we get it.
*
You have a strong proficiency in programming and data analysis, in Python and/or Matlab.
*
You have an excellent command of the Dutch and English language.
*
You are proactive, solution-oriented, able to plan well and set priorities.
*
You are good at communicating, both verbally, in writing and visually. You can translate complex scientific findings into useful insights.
*
You are able to work approximately 50% of the time in our Neurolab in Amsterdam (Marnixkade 109F).
We offer:
*
Working in a team with passionate, intelligent and supportive colleagues from different scientific fields.
*
Working on bringing together the two most interesting scientific fields, neuroscience and AI, into practical applications.
*
A hybrid work environment with flexible working hours.
*
A flat organizational structure in which you have intellectual freedom and the opportunity to implement your ideas.
*
An intellectually challenging environment, where we’re always working on something new, and where it is never boring.
*
Good supervision, so that you can continue to grow and develop.
*
A salary comparable to the salary level at universities (at PhD or postdoc level depending on experience).
*
An office/lab on the Marnixkade in Amsterdam
At Alpha.One we understand that every person is different, with their own talents, dreams and ambitions. That is why we strive to help you create a career that matches your ambitions.
Practical information and applying
*
Desired start date: as soon as possible.
*
Email applications, including CV, to ingrid at alpha.one
*
Also see https://www.alpha.one/
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From samuel.cosper at tu-dresden.de Wed Oct 5 10:30:10 2022
From: samuel.cosper at tu-dresden.de (Samuel Harris Cosper)
Date: Wed, 5 Oct 2022 08:30:10 +0000
Subject: [FieldTrip] Mex file issue
Message-ID:
Dear all,
I am having an issue, as seems to be a common occurrence, with mex files and fieldtrip. I have double checked that "mex -setup" is working correctly and have also installed the MATLAB Support for MinGW-w64 C/C++ Compiler (version 22.1.0) already (in Matlab 2022a). I have also installed the most recent version of Fieldtrip on the Website/github. Another important factor: I have ran this exact script before on many computers with no issue. Only on my new laptop (windows 10) am I running into this issue. I have made sure that all updates are installed.
Now to my issue. When running ft_timelockstatistics with all of the proper cfg configurations, I receive an error message "Invalid MEX-file 'C:\Program Files\MATLAB\fieldtrip-20220104\private\combineClusters.mexw64': The specified module could not be found."
I then ran "ft_compile_mex(true)". Everything seemed to be working until "Compiling MEX file src/mxSerialize_cpp ... Building with 'MinGW64 Compiler (C++)'." And I received the following error:
Error using ft_compile_mex>compile_mex_list
Error using mex
C:\Program Files\MATLAB\fieldtrip-20220104\src\mxSerialize_cpp.cpp:10:1: error: 'EXTERN_C' does not name a type
EXTERN_C mxArray* mxSerialize(mxArray const *);
^~~~~~~~
C:\Program Files\MATLAB\fieldtrip-20220104\src\mxSerialize_cpp.cpp:11:1: error: 'EXTERN_C' does not name a type
EXTERN_C mxArray* mxDeserialize(const void *, size_t);
^~~~~~~~
C:\Program Files\MATLAB\fieldtrip-20220104\src\mxSerialize_cpp.cpp: In function 'void mexFunction(int, mxArray**, int, const mxArray**)':
C:\Program Files\MATLAB\fieldtrip-20220104\src\mxSerialize_cpp.cpp:22:50: error: 'mxSerialize' was not declared in this scope
plhs[0] = (mxArray *) mxSerialize(prhs[0]);
^
Error in ft_compile_mex (line 114)
compile_mex_list(L, ftpath, force);
Is there a solution to this issue? I have looked online, but was unsuccessful, particularly for Matlab 2022a and with the newest version of Fieldtrip.
Thanks in advance!
Best wishes,
Sam
---
Dr.rer.nat. Samuel H. Cosper
Postdoc
Entwicklingspsychologie und Neurowissenschaft der Lebensspanne
(Lifespan Developmental Neuroscience)
Fakultät Psychologie (Faculty of Psychology)
Zellescher Weg 17, Raum A229
01062 Dresden
Email: samuel.cosper at tu-dresden.de
Technische Universität Dresden
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From Sebastian.Neudek at med.uni-duesseldorf.de Wed Oct 5 13:40:34 2022
From: Sebastian.Neudek at med.uni-duesseldorf.de (Sebastian.Neudek at med.uni-duesseldorf.de)
Date: Wed, 5 Oct 2022 11:40:34 +0000
Subject: [FieldTrip] Comparing ERPs/ERFs with measurement errors factored in
Message-ID: <89ef3ab0fec94900b05b51de1b346e5b@med.uni-duesseldorf.de>
Hi all,
I spent some time thinking about the error calculation for an ERF/ERP, which probably is neglected by many. The method I thought of is maybe not 100 percent correct, so hopefully you can help me perfect this, but I think it is at least going into the right direction.
tldr: first calculate the measurement error (uncertainty) of your device. This measurement error propergates when averaging over trials. The measurement error and the error of the subjects mean propagates to the groups mean when averaging over subjects.
First, lets start with a single subject. You have multiple trials, which hopefully are now artifact free, because you applied some preprocessing and filtering. Also there is an empty room recording. Maybe you haven't thought about it, but to each value of your ERP/ERF trials, there is an uncertainty, which results from random or systematic uncertainties.
What uncertainties? Most prominantly there are errors from your measuring device (it can't measure infinetly accurate), your preprocessing method, some other factors....
How can I calculate these uncertainties? Let's take the empty room recording. Split it into trials with the same length like your other trials (if the length varies, take the lenght of longest trial) and apply the same preprocessing as for your subject. Now we can average these empty room trials and calculate the standard deviation. What do we expect? In a perfect condition, we expect the average to be 0 across the whole trial (if you de-meaned the data). But it can happen, that because of the preprocessing or systematic errors the average isn't 0 everywhere. That means: If you measure your Subject, all of the trials encounter this systematic error. To correct it, you can substract this systematic uncertainty from each ouf your subject's trials.
The standard deviation (not the standard error!) on the other hand estimates your random uncertainty at each timpoint of your measuring method. If you detrended your data, the standard deviation should be approximately the same at each timepoint. If not, your uncertainty will grow with time, as a few sensor drifts occur.
Back to our subjects data. This far, we calculated the random uncertainty for each timepoint and applied the systematic uncertainty correction (also at each timepoint) to our trials. We need to keep in mind, that the random uncertainty applies to each trial. What happens if we want to average over our trials?
First we average over all trials like we did before. What we get is our subjects mean and the variance of the subject. By this we can calculate the standard error of the subjects mean (This error is only because of the subjects variance but does not factor in the uncertainty of the measurements). The tricky part is to factor in, that all of the previously used values aren't measured infidently accurate, but are subject to the measurement uncertainty. This is done using the 'Propagation of uncertainty' (also called 'Propagation of error').
The uncertainties of two (and all other) trials are independent. If we add two trials, the resulting uncertainty is dz= sqrt(dx^2+dy^2) (dz is total uncertainty of both trials added, dx is uncertainty of trial 1 and dy is uncertainty of trial 2). Because we want the average of both measurements, the uncertainty is devided by the number of measurements.
(Source: https://www.physics.upenn.edu/sites/default/files/Managing%20Errors%20and%20Uncertainty.pdf)
Therefore if we average over all trials, the uncertainties are as follow:
d_avg = 1/n *sqrt(d_1^2+d_2^2+...+d_n^2). In case of a single subject the uncertainties are all same and therefore: d_avg = sqrt(n)/n * d (d is the uncertainty of one trial).
What did we calculate with this formula? We calculated the uncertainty of our average because of our measureing method and measuring device. Our previously calculated mean value of our subject is therefore not a perfect measurement, but 'smeared' by our uncertainty of measurement. With other words: we couldn't calculate it more precise, because of our device and methods.
We now have two errors (uncertainties are also errors): The error of our subjects mean (EOS), because of the subjects variability, and the error of our measurement (EOM). How do these add together?
Because both errors are independent error_total = sqrt(EOS^2+EOM^2).
This is now the error of the mean of the ERP/ERF (or total error, if you want to call it like that).
To get the standard deviation of the ERP/ERF you need to multiply this error with the square root of number of trials, and by squaring this standard deviation you get the variance of the ERP/ERF (your actually measured variance), which factors in your measurement errors.
What do we need to do to compare the ERP/ERF of two subjects?
The now calulated total variance for subject 1 and subject 2 factors in the measurement error and the variance of the subjects. Therefore it compares it by with the variances we actually measured and which we for certain know. As a result, the variances are always bigger, than just using the subjects variances. Therefore you will get worse p-values.
Last but not least: Now for all subjects the variances and means are calculated. How to calculate the variance of the group?
Again you start as normal by calculating the groups average and variance with the average values of the subjects. But again, for all these average values of the subjects, there is an uncertainty. We already calulated thess uncertainty with the total error for each subjects mean. And again the error will propagate with averaging:
The measurement error for the groups average is again err_measurement = 1/n *sqrt(err_total_1^2+err_total_2^2+...+err_total_n^2), because the errors of all subjects are independent. You now need the again the standard error of the mean. Your total error of the grous is then: error_total_group = sqrt(EOS^2+EOM^2). Calculating back to the variance you will get your measured variance for the group, on which you can do your statistics.
What do you think of it? This procedure is only for ERF/ERP and not for TFR. For TFR I don't know how the error propagates in case of a fourier transformation.
Best,
Sebastian
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From yiye at eln.upv.es Wed Oct 5 18:49:17 2022
From: yiye at eln.upv.es (Yiyao Ye Lin)
Date: Wed, 5 Oct 2022 18:49:17 +0200
Subject: [FieldTrip] RV: sloreta doest not get unity source variance
In-Reply-To: <001601d8d8da$1036b300$30a41900$@eln.upv.es>
References: <001601d8d8da$1036b300$30a41900$@eln.upv.es>
Message-ID: <004801d8d8da$68d99630$3a8cc290$@eln.upv.es>
Dear researchers,
After reading the sLoreta theory, I attempted to understand the sloreta
implementation detail. I attempted to obtain the source covariance matrix,
and I could not get the expected unit variance. To do this, I attempted to
calculate by two ways:
a) lf'*invG*lf after line 268 of ft_inverse_sloreta
b) lf(:,ii)'*invG*lf(:,ii) at line 267 of ft_inverse_sloreta
For this, I used EEG signal with average reference.
What's wrong? How can the unity variance of source variance matrix?
Best regards
*******************
Yiyao Ye Lin
C/Camino de Vera SN
Universidad Politècnica de València
Departamento de Ingeniería Electrónica, Ed. 7F
46022, València
València
Ext. 76026
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From sxb2391 at mavs.uta.edu Wed Oct 5 19:03:15 2022
From: sxb2391 at mavs.uta.edu (Bastola, Subrat)
Date: Wed, 5 Oct 2022 17:03:15 +0000
Subject: [FieldTrip] Trying out different optimization methods for dipole
fitting
Message-ID:
Dear Researchers,
I am currently working on Dipole fitting problems with Brainstorm. Going through the scripts, I realized that Brainstorm uses Fieldtrip's ft_dipolefitting function to fit dipoles. The script on the ft_dipolefitting says we use either of fmincon or fminunc as the optimization function to fit dipoles.
My question is: Would it be possible to try a different optimization algorithm by changing the optimization function to let's say Genetic Algorithm or simulannealbnd? Or will that not work?
With regards,
Subrat
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From janmathijs.schoffelen at donders.ru.nl Thu Oct 6 08:08:34 2022
From: janmathijs.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Thu, 6 Oct 2022 06:08:34 +0000
Subject: [FieldTrip] sloreta doest not get unity source variance
In-Reply-To: <004801d8d8da$68d99630$3a8cc290$@eln.upv.es>
References: <001601d8d8da$1036b300$30a41900$@eln.upv.es>
<004801d8d8da$68d99630$3a8cc290$@eln.upv.es>
Message-ID:
Hi Yiyao,
To be honest, I don’t understand your question. And it is not clear what the problem is.
Can you please have a look at the documentation below and reformulate your question, so that the readership of the list can constructively think along?
https://www.fieldtriptoolbox.org/faq/how_to_ask_good_questions_to_the_community/
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002202
http://www.catb.org/esr/faqs/smart-questions.html
Best wishes,
Jan-Mathijs
On 5 Oct 2022, at 18:49, Yiyao Ye Lin via fieldtrip > wrote:
Dear researchers,
After reading the sLoreta theory, I attempted to understand the sloreta implementation detail. I attempted to obtain the source covariance matrix, and I could not get the expected unit variance. To do this, I attempted to calculate by two ways:
a) lf'*invG*lf after line 268 of ft_inverse_sloreta
b) lf(:,ii)'*invG*lf(:,ii) at line 267 of ft_inverse_sloreta
For this, I used EEG signal with average reference.
What's wrong? How can the unity variance of source variance matrix?
Best regards
*******************
Yiyao Ye Lin
C/Camino de Vera SN
Universidad Politècnica de València
Departamento de Ingeniería Electrónica, Ed. 7F
46022, València
València
Ext. 76026
_______________________________________________
fieldtrip mailing list
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
https://doi.org/10.1371/journal.pcbi.1002202
-------------- next part --------------
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From a.milne at ucl.ac.uk Thu Oct 6 16:59:26 2022
From: a.milne at ucl.ac.uk (Milne, Alice)
Date: Thu, 6 Oct 2022 14:59:26 +0000
Subject: [FieldTrip] Cluster Analysis - electrodes not clustering
Message-ID:
Hello!
I am running cluster analysis on a really clear effect in fronto-central electrodes when you plot data.
The cluster analysis identifies these electrodes but is identifying each one as a separate cluster. No two electrodes, even when neighbours are part of the same cluster.
This is a within subjects effect – 22 subjects and two conditions. I am not sure how this issue could to the cluster analysis settings as they seem quite standard ( and have been used previously in the lab).
I can also plot the topographies etc and the neighbours are all where they should be.
Has anyone come across this issue before?
Many thanks for your time 😊
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From mikkelcv at drcmr.dk Fri Oct 7 09:15:34 2022
From: mikkelcv at drcmr.dk (mikkelcv at drcmr.dk)
Date: Fri, 7 Oct 2022 07:15:34 +0000
Subject: [FieldTrip] Cluster Analysis - electrodes not clustering
In-Reply-To:
References:
Message-ID:
Hi Alice
This often happen if you have a sparse EEG layout so the neighbouring electrodes a too far away when you define which electrodes are “neighbours”. The solution is to make sure the frontal electrodes are connected to neighbouring electrodes in the output from ft_prepare_neighbours. You can easily inspect this with ft_neighbourplot (sse example here: https://github.com/mcvinding/cluster-stat-workshop/blob/main/tutorial.md#non-parametric-cluster-based-permutation-tests-on-all-channel-data).
To change the neighbour definition, see the options in documentation for ft_prepare_neighbours.
Best regards
Mikkel
Fra: fieldtrip på vegne af Milne, Alice via fieldtrip
Dato: torsdag, 6. oktober 2022 kl. 17.00
Til: fieldtrip at science.ru.nl
Cc: Milne, Alice
Emne: [FieldTrip] Cluster Analysis - electrodes not clustering
Hello!
I am running cluster analysis on a really clear effect in fronto-central electrodes when you plot data.
The cluster analysis identifies these electrodes but is identifying each one as a separate cluster. No two electrodes, even when neighbours are part of the same cluster.
This is a within subjects effect – 22 subjects and two conditions. I am not sure how this issue could to the cluster analysis settings as they seem quite standard ( and have been used previously in the lab).
I can also plot the topographies etc and the neighbours are all where they should be.
Has anyone come across this issue before?
Many thanks for your time 😊
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From yiye at eln.upv.es Sat Oct 8 16:56:46 2022
From: yiye at eln.upv.es (Yiyao Ye Lin)
Date: Sat, 8 Oct 2022 16:56:46 +0200
Subject: [FieldTrip] sloreta doest not get unity source variance
In-Reply-To:
References: <001601d8d8da$1036b300$30a41900$@eln.upv.es>
<004801d8d8da$68d99630$3a8cc290$@eln.upv.es>
Message-ID: <006a01d8db26$305e3470$911a9d50$@eln.upv.es>
Dear researchers,
I am sorry to not formulate correctly the question. In the attached file I attempted to explain my doubt. Hope that you can understand my question this time and can help me to better understand sLortea.
What I can not understand is that sLoreta is standardized minimum norm solution, the source covariance matrix should be identity matrix. However, I could not check this. I think that there be something that I did not understand correctly.
Thank you very much for your attention
Best regards
*******************
Yiyao Ye Lin
C/Camino de Vera SN
Universidad Politècnica de València
Departamento de Ingeniería Electrónica, Ed. 7F
46022, València
València
Ext. 76026
De: fieldtrip En nombre de Schoffelen, J.M. (Jan Mathijs) via fieldtrip
Enviado el: jueves, 6 de octubre de 2022 8:09
Para: FieldTrip discussion list
CC: Schoffelen, J.M. (Jan Mathijs)
Asunto: Re: [FieldTrip] sloreta doest not get unity source variance
Hi Yiyao,
To be honest, I don’t understand your question. And it is not clear what the problem is.
Can you please have a look at the documentation below and reformulate your question, so that the readership of the list can constructively think along?
https://www.fieldtriptoolbox.org/faq/how_to_ask_good_questions_to_the_community/
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002202
http://www.catb.org/esr/faqs/smart-questions.html
Best wishes,
Jan-Mathijs
On 5 Oct 2022, at 18:49, Yiyao Ye Lin via fieldtrip > wrote:
Dear researchers,
After reading the sLoreta theory, I attempted to understand the sloreta implementation detail. I attempted to obtain the source covariance matrix, and I could not get the expected unit variance. To do this, I attempted to calculate by two ways:
a) lf'*invG*lf after line 268 of ft_inverse_sloreta
b) lf(:,ii)'*invG*lf(:,ii) at line 267 of ft_inverse_sloreta
For this, I used EEG signal with average reference.
What's wrong? How can the unity variance of source variance matrix?
Best regards
*******************
Yiyao Ye Lin
C/Camino de Vera SN
Universidad Politècnica de València
Departamento de Ingeniería Electrónica, Ed. 7F
46022, València
València
Ext. 76026
_______________________________________________
fieldtrip mailing list
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
https://doi.org/10.1371/journal.pcbi.1002202
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From t.donner at uke.de Sun Oct 9 15:38:12 2022
From: t.donner at uke.de (Tobias Donner)
Date: Sun, 9 Oct 2022 15:38:12 +0200
Subject: [FieldTrip] Postdoc position in human decision-making
Message-ID: <68573AF8-DDD2-43E0-8002-BCCF43E1600B@uke.de>
A postdoc position is available in the Computational Perception and Cognition Laboratory of Alan Stocker at the University of Pennsylvania, USA.
The position is part of the ongoing NSF-funded project ‘Choice-induced biases in human decision-making’ in collaboration with the laboratory of Tobias Donner at the University Medical Center Hamburg, Germany. The goal of the project is to understand how decisions influence the memory of past (consistency bias) but also the evaluation of future evidence (confirmation bias) in human decision-making. The project employs a highly interdisciplinary approach that combines psychophysical and functional neuroimaging (MEG) experiments with theory and computational modeling.
The position focuses on the development of computational and neural models of subjects’ behavior in such decision-tasks and their validation against psychophysical and neural data. The project provides the opportunity to visit the partner lab in Hamburg and gain first-hand experience with MEG data acquisition and analysis.
Candidates from all backgrounds are considered. However, a good theoretical/ computational background and experience in working with computer models and simulations is expected. Experience with the technical aspects of running psychophysical experiments is a plus. Above all, however, we are looking for candidates who are deeply curious about the theoretical principles underlying human decision- behavior.
The position is funded for up two years with the possibility for extensions. The lab laboratory is embedded in the University of Pennsylvania’s strong and vibrant neuroscience and cognitive science community (mindCORE , CNI ), providing the new lab member with a very interactive research environment.
Please send any questions and submit applications (CV, publications, brief statement of research interests and skills, and names of 2-3 references) to Alan Stocker (astocker at psych.upenn.edu ).
—
Prof. Dr. Tobias H. Donner
Professor of Integrative Neuroscience
Head of Section Computational Cognitive Neuroscience
Department of Neurophysiology and Pathophysiology
University Medical Center Hamburg-Eppendorf
Lab: www.tobiasdonner.net
--
_____________________________________________________________________
Universitätsklinikum Hamburg-Eppendorf; Körperschaft des öffentlichen Rechts; Gerichtsstand: Hamburg | www.uke.de
Vorstandsmitglieder: Prof. Dr. Burkhard Göke (Vorsitzender), Joachim Prölß, Prof. Dr. Blanche Schwappach-Pignataro, Marya Verdel
_____________________________________________________________________
SAVE PAPER - THINK BEFORE PRINTING
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From cecilia.mazzetti at braincredible.ch Mon Oct 10 16:25:06 2022
From: cecilia.mazzetti at braincredible.ch (cecilia.mazzetti at braincredible.ch)
Date: Mon, 10 Oct 2022 14:25:06 +0000
Subject: [FieldTrip] ANT Neuromeeting - Berlin, 3-5 November
Message-ID:
Dear all,
I wanted to share this interesting conference happening 3-5 November:
The 31st edition of ANT Neuromeeting offers a unique opportunity for the development of professional connections that can lead to exciting partnerships across the globe. In addition to the rich social program, participants will be able to attend different sessions, exhibits and demonstrations of ANT's product portfolio.
This annual meeting is well suited for novice and experienced researchers and clinicians alike. Together, we will learn about the latest advances in neuroscience, neurology, neonatology, artificial intelligence, new technologies, and mental health research, diagnostics and treatment.
Fore registration:
https://lnkd.in/exdw58hc
More info:
https://lnkd.in/eChV4J8b
[signature_1613921905]
Dr. Cecilia Mazzetti
Business Developer
[A black background with white text Description automatically generated with low confidence]
Rue de l’industrie 23
1950, sion
Switzerland
+41-782016728
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From janmathijs.schoffelen at donders.ru.nl Tue Oct 11 08:39:04 2022
From: janmathijs.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Tue, 11 Oct 2022 06:39:04 +0000
Subject: [FieldTrip] sloreta doest not get unity source variance
In-Reply-To: <006a01d8db26$305e3470$911a9d50$@eln.upv.es>
References: <001601d8d8da$1036b300$30a41900$@eln.upv.es>
<004801d8d8da$68d99630$3a8cc290$@eln.upv.es>
<006a01d8db26$305e3470$911a9d50$@eln.upv.es>
Message-ID: <86FEB024-EB2E-4AE1-8C85-F071A3DDAAB7@donders.ru.nl>
Hi Yiyao,
Thank you for your attempts at clarifying your doubts. I still don’t understand what the confusion is. I doubt whether many people have made the effort of opening the word document, but I did so, and cannot make much out of it. It’s a bit too confusing to me to try and map all the different variable name conventions (paper versus code) onto each other. In general, however, I don’t understand why you think that the ’source covariance matrix’ should be an identity matrix, which source covariance matrix you are referring to, and which combination of variables in the code you would expect to yield an identity matrix.
As in any distributed source model, the source reconstructed variance-covariance matrix (i.e. filter * C * filter’) surely will not be an identity matrix. This would namely imply that the variance of each of the reconstructed sources is the same (and has a value of 1). On the other hand, it could be that there’s some confusion with respect to the ‘prior’ that is used for the computations, i.e. the (implicit) identity matrix that is used for the computation of the Gram-matrix (lf * lf’) -> this can be thought of as (lf * S * lf’), with S being the prior variance-covariance matrix of the sources, which is often conveniently set to be an identity matrix.
Note that the FieldTrip code for the sloreta implementation has originally been contributed by Sarang Dalal, and this is based on the formulation as described in the Sekihara & Nagarajan book (adaptive spatial filters for electromagnetic brain imaging). It would therefore be better to check the code against the description in the book, rather than to the paper that you attached to the e-mail. And subsequently you might want to check the exposition in the book, with the paper.
Good luck and best wishes,
Jan-Mathijs
On 8 Oct 2022, at 16:56, Yiyao Ye Lin via fieldtrip > wrote:
Dear researchers,
I am sorry to not formulate correctly the question. In the attached file I attempted to explain my doubt. Hope that you can understand my question this time and can help me to better understand sLortea.
What I can not understand is that sLoreta is standardized minimum norm solution, the source covariance matrix should be identity matrix. However, I could not check this. I think that there be something that I did not understand correctly.
Thank you very much for your attention
Best regards
*******************
Yiyao Ye Lin
C/Camino de Vera SN
Universidad Politècnica de València
Departamento de Ingeniería Electrónica, Ed. 7F
46022, València
València
Ext. 76026
De: fieldtrip > En nombre de Schoffelen, J.M. (Jan Mathijs) via fieldtrip
Enviado el: jueves, 6 de octubre de 2022 8:09
Para: FieldTrip discussion list >
CC: Schoffelen, J.M. (Jan Mathijs) >
Asunto: Re: [FieldTrip] sloreta doest not get unity source variance
Hi Yiyao,
To be honest, I don’t understand your question. And it is not clear what the problem is.
Can you please have a look at the documentation below and reformulate your question, so that the readership of the list can constructively think along?
https://www.fieldtriptoolbox.org/faq/how_to_ask_good_questions_to_the_community/
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002202
http://www.catb.org/esr/faqs/smart-questions.html
Best wishes,
Jan-Mathijs
On 5 Oct 2022, at 18:49, Yiyao Ye Lin via fieldtrip > wrote:
Dear researchers,
After reading the sLoreta theory, I attempted to understand the sloreta implementation detail. I attempted to obtain the source covariance matrix, and I could not get the expected unit variance. To do this, I attempted to calculate by two ways:
a) lf'*invG*lf after line 268 of ft_inverse_sloreta
b) lf(:,ii)'*invG*lf(:,ii) at line 267 of ft_inverse_sloreta
For this, I used EEG signal with average reference.
What's wrong? How can the unity variance of source variance matrix?
Best regards
*******************
Yiyao Ye Lin
C/Camino de Vera SN
Universidad Politècnica de València
Departamento de Ingeniería Electrónica, Ed. 7F
46022, València
València
Ext. 76026
_______________________________________________
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 --------------
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From yiye at eln.upv.es Tue Oct 11 09:37:58 2022
From: yiye at eln.upv.es (Yiyao Ye Lin)
Date: Tue, 11 Oct 2022 09:37:58 +0200
Subject: [FieldTrip] sloreta doest not get unity source variance
In-Reply-To: <86FEB024-EB2E-4AE1-8C85-F071A3DDAAB7@donders.ru.nl>
References: <001601d8d8da$1036b300$30a41900$@eln.upv.es>
<004801d8d8da$68d99630$3a8cc290$@eln.upv.es>
<006a01d8db26$305e3470$911a9d50$@eln.upv.es>
<86FEB024-EB2E-4AE1-8C85-F071A3DDAAB7@donders.ru.nl>
Message-ID: <009001d8dd44$62a7ac20$27f70460$@eln.upv.es>
Dear Jan-Mathjis,
Thank you very much for your quick reply.
I really appreciate your technical comment. I think that I had some confusion of some terms. I think the equation(15) refers the prior variance-covariance matrix of the source, which was set to be an identity matrix. Then it was estimated scalp covariance matrix taking into account this consideration (eq 16). Then Then it was reformulated the estimated ‘prior’ variance-covariance matrix (eq 17). I thought that the estimated ‘prior’ variance-covariance matrix should be identity matrix, but I found that this term in eq17 does not meet the criteria regardless of the hypothesis eq15.
My question if the estimated ‘prior’ variance-covariance matrix in eq 17 should be identity matrix from theoretical point of view.
Best regards
*******************
Yiyao Ye Lin
C/Camino de Vera SN
Universidad Politècnica de València
Departamento de Ingeniería Electrónica, Ed. 7F
46022, València
València
Ext. 76026
De: fieldtrip En nombre de Schoffelen, J.M. (Jan Mathijs) via fieldtrip
Enviado el: martes, 11 de octubre de 2022 8:39
Para: FieldTrip discussion list
CC: Schoffelen, J.M. (Jan Mathijs)
Asunto: Re: [FieldTrip] sloreta doest not get unity source variance
Hi Yiyao,
Thank you for your attempts at clarifying your doubts. I still don’t understand what the confusion is. I doubt whether many people have made the effort of opening the word document, but I did so, and cannot make much out of it. It’s a bit too confusing to me to try and map all the different variable name conventions (paper versus code) onto each other. In general, however, I don’t understand why you think that the ’source covariance matrix’ should be an identity matrix, which source covariance matrix you are referring to, and which combination of variables in the code you would expect to yield an identity matrix.
As in any distributed source model, the source reconstructed variance-covariance matrix (i.e. filter * C * filter’) surely will not be an identity matrix. This would namely imply that the variance of each of the reconstructed sources is the same (and has a value of 1). On the other hand, it could be that there’s some confusion with respect to the ‘prior’ that is used for the computations, i.e. the (implicit) identity matrix that is used for the computation of the Gram-matrix (lf * lf’) -> this can be thought of as (lf * S * lf’), with S being the prior variance-covariance matrix of the sources, which is often conveniently set to be an identity matrix.
Note that the FieldTrip code for the sloreta implementation has originally been contributed by Sarang Dalal, and this is based on the formulation as described in the Sekihara & Nagarajan book (adaptive spatial filters for electromagnetic brain imaging). It would therefore be better to check the code against the description in the book, rather than to the paper that you attached to the e-mail. And subsequently you might want to check the exposition in the book, with the paper.
Good luck and best wishes,
Jan-Mathijs
On 8 Oct 2022, at 16:56, Yiyao Ye Lin via fieldtrip > wrote:
Dear researchers,
I am sorry to not formulate correctly the question. In the attached file I attempted to explain my doubt. Hope that you can understand my question this time and can help me to better understand sLortea.
What I can not understand is that sLoreta is standardized minimum norm solution, the source covariance matrix should be identity matrix. However, I could not check this. I think that there be something that I did not understand correctly.
Thank you very much for your attention
Best regards
*******************
Yiyao Ye Lin
C/Camino de Vera SN
Universidad Politècnica de València
Departamento de Ingeniería Electrónica, Ed. 7F
46022, València
València
Ext. 76026
De: fieldtrip > En nombre de Schoffelen, J.M. (Jan Mathijs) via fieldtrip
Enviado el: jueves, 6 de octubre de 2022 8:09
Para: FieldTrip discussion list >
CC: Schoffelen, J.M. (Jan Mathijs) >
Asunto: Re: [FieldTrip] sloreta doest not get unity source variance
Hi Yiyao,
To be honest, I don’t understand your question. And it is not clear what the problem is.
Can you please have a look at the documentation below and reformulate your question, so that the readership of the list can constructively think along?
https://www.fieldtriptoolbox.org/faq/how_to_ask_good_questions_to_the_community/
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002202
http://www.catb.org/esr/faqs/smart-questions.html
Best wishes,
Jan-Mathijs
On 5 Oct 2022, at 18:49, Yiyao Ye Lin via fieldtrip < fieldtrip at science.ru.nl> wrote:
Dear researchers,
After reading the sLoreta theory, I attempted to understand the sloreta implementation detail. I attempted to obtain the source covariance matrix, and I could not get the expected unit variance. To do this, I attempted to calculate by two ways:
a) lf'*invG*lf after line 268 of ft_inverse_sloreta
b) lf(:,ii)'*invG*lf(:,ii) at line 267 of ft_inverse_sloreta
For this, I used EEG signal with average reference.
What's wrong? How can the unity variance of source variance matrix?
Best regards
*******************
Yiyao Ye Lin
C/Camino de Vera SN
Universidad Politècnica de València
Departamento de Ingeniería Electrónica, Ed. 7F
46022, València
València
Ext. 76026
_______________________________________________
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
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From yiye at eln.upv.es Tue Oct 11 09:38:47 2022
From: yiye at eln.upv.es (Yiyao Ye Lin)
Date: Tue, 11 Oct 2022 09:38:47 +0200
Subject: [FieldTrip] sloreta doest not get unity source variance
References: <001601d8d8da$1036b300$30a41900$@eln.upv.es>
<004801d8d8da$68d99630$3a8cc290$@eln.upv.es>
<006a01d8db26$305e3470$911a9d50$@eln.upv.es>
<86FEB024-EB2E-4AE1-8C85-F071A3DDAAB7@donders.ru.nl>
Message-ID: <00b301d8dd44$7faabc40$7f0034c0$@eln.upv.es>
Dear Jan-Mathjis,
Thank you very much for your quick reply.
I really appreciate your technical comment. I think that I had some confusion of some terms. I think the equation(15) refers the prior variance-covariance matrix of the source, which was set to be an identity matrix. Then it was estimated scalp covariance matrix taking into account this consideration (eq 16). Then Then it was reformulated the estimated ‘prior’ variance-covariance matrix (eq 17). I thought that the estimated ‘prior’ variance-covariance matrix should be identity matrix, but I found that this term in eq17 does not meet the criteria regardless of the hypothesis eq15.
My question if the estimated ‘prior’ variance-covariance matrix in eq 17 should be identity matrix from theoretical point of view.
Best regards
*******************
Yiyao Ye Lin
C/Camino de Vera SN
Universidad Politècnica de València
Departamento de Ingeniería Electrónica, Ed. 7F
46022, València
València
Ext. 76026
De: fieldtrip > En nombre de Schoffelen, J.M. (Jan Mathijs) via fieldtrip
Enviado el: martes, 11 de octubre de 2022 8:39
Para: FieldTrip discussion list >
CC: Schoffelen, J.M. (Jan Mathijs) >
Asunto: Re: [FieldTrip] sloreta doest not get unity source variance
Hi Yiyao,
Thank you for your attempts at clarifying your doubts. I still don’t understand what the confusion is. I doubt whether many people have made the effort of opening the word document, but I did so, and cannot make much out of it. It’s a bit too confusing to me to try and map all the different variable name conventions (paper versus code) onto each other. In general, however, I don’t understand why you think that the ’source covariance matrix’ should be an identity matrix, which source covariance matrix you are referring to, and which combination of variables in the code you would expect to yield an identity matrix.
As in any distributed source model, the source reconstructed variance-covariance matrix (i.e. filter * C * filter’) surely will not be an identity matrix. This would namely imply that the variance of each of the reconstructed sources is the same (and has a value of 1). On the other hand, it could be that there’s some confusion with respect to the ‘prior’ that is used for the computations, i.e. the (implicit) identity matrix that is used for the computation of the Gram-matrix (lf * lf’) -> this can be thought of as (lf * S * lf’), with S being the prior variance-covariance matrix of the sources, which is often conveniently set to be an identity matrix.
Note that the FieldTrip code for the sloreta implementation has originally been contributed by Sarang Dalal, and this is based on the formulation as described in the Sekihara & Nagarajan book (adaptive spatial filters for electromagnetic brain imaging). It would therefore be better to check the code against the description in the book, rather than to the paper that you attached to the e-mail. And subsequently you might want to check the exposition in the book, with the paper.
Good luck and best wishes,
Jan-Mathijs
On 8 Oct 2022, at 16:56, Yiyao Ye Lin via fieldtrip > wrote:
Dear researchers,
I am sorry to not formulate correctly the question. In the attached file I attempted to explain my doubt. Hope that you can understand my question this time and can help me to better understand sLortea.
What I can not understand is that sLoreta is standardized minimum norm solution, the source covariance matrix should be identity matrix. However, I could not check this. I think that there be something that I did not understand correctly.
Thank you very much for your attention
Best regards
*******************
Yiyao Ye Lin
C/Camino de Vera SN
Universidad Politècnica de València
Departamento de Ingeniería Electrónica, Ed. 7F
46022, València
València
Ext. 76026
De: fieldtrip > En nombre de Schoffelen, J.M. (Jan Mathijs) via fieldtrip
Enviado el: jueves, 6 de octubre de 2022 8:09
Para: FieldTrip discussion list >
CC: Schoffelen, J.M. (Jan Mathijs) >
Asunto: Re: [FieldTrip] sloreta doest not get unity source variance
Hi Yiyao,
To be honest, I don’t understand your question. And it is not clear what the problem is.
Can you please have a look at the documentation below and reformulate your question, so that the readership of the list can constructively think along?
https://www.fieldtriptoolbox.org/faq/how_to_ask_good_questions_to_the_community/
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002202
http://www.catb.org/esr/faqs/smart-questions.html
Best wishes,
Jan-Mathijs
On 5 Oct 2022, at 18:49, Yiyao Ye Lin via fieldtrip < fieldtrip at science.ru.nl> wrote:
Dear researchers,
After reading the sLoreta theory, I attempted to understand the sloreta implementation detail. I attempted to obtain the source covariance matrix, and I could not get the expected unit variance. To do this, I attempted to calculate by two ways:
a) lf'*invG*lf after line 268 of ft_inverse_sloreta
b) lf(:,ii)'*invG*lf(:,ii) at line 267 of ft_inverse_sloreta
For this, I used EEG signal with average reference.
What's wrong? How can the unity variance of source variance matrix?
Best regards
*******************
Yiyao Ye Lin
C/Camino de Vera SN
Universidad Politècnica de València
Departamento de Ingeniería Electrónica, Ed. 7F
46022, València
València
Ext. 76026
_______________________________________________
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
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From contact.t4te at gmail.com Fri Oct 14 10:15:09 2022
From: contact.t4te at gmail.com (Marta Bortoletto)
Date: Fri, 14 Oct 2022 10:15:09 +0200
Subject: [FieldTrip] Launch of the Team 4 TMS-EEG initiative - T4TE
Message-ID:
Dear Fieldtrip community,
We are launching the first study of the Team for TMS-EEG (T4TE) initiative
and would be happy to involve interested labs and researcher of the
Fieldtrip community.
T4TE is inspired by other collaborative initiatives that aim at improving
research quality through the promotion of large datasets acquisition, high
methodological rigor, scientific transparency, and data sharing in the
field of neuroscience.
As a community, we need to define good practices and to better understand
what is measured with TMS-EEG through a wide joint effort.
T4TE aims at fostering collaboration community for projects aimed at
testing RELIABILITY, VALIDITY and BIOMARKERS DEVELOPMENT of specific
TMS-EEG indexes (e.g., TMS-Evoked Potentials, Global Mean Field Power,
Natural Frequencies, etc.).
The first study is focused on the TEPs obtained with the stimulation of M1
and it aims at testing their reliability for variations in procedures
across labs. The study is designed to disentangle the effects of
flexibility in data acquisition and in data processing on TEPs. Moreover,
it will investigate the direct effect of flexibility on: a) the comparison
of TEPs in trials of high and low cortical excitability, as indexed by
motor-evoked potentials (MEPs), and b) the effect of prestimulus
oscillations on TEPs.
This study is grounded on one of the TMS-EEG indexes that are most studied
in the literature but that still presents several sources of variability
and controversy. We hope that this endeavour will be of benefit for the
entire community.
We have now opened the registration to join the project until December
2022. For more information about it and to check requirements, please visit
our website at www.T4TE.org
Labs where there is no previous experience in TMS-EEG coregistration are
welcome to partecipate and can benefit of the collaboration with more
experienced participating lab if they wish support.
If you want to contribute to the first study, but are unsure whether you
can meet all the set requirements, please don’t hesitate to contact us.
For registering, sending questions or comments, please write to
contact.T4TE at gmail.com
Best Regards,
The T4TE core team (Agnese, Chiara, Julio, Marta, Mimma, Petro, Tuomas)
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From ronconi.luca at unisr.it Wed Oct 19 09:25:15 2022
From: ronconi.luca at unisr.it (Ronconi Luca)
Date: Wed, 19 Oct 2022 07:25:15 +0000
Subject: [FieldTrip] Post-doc position in Milan, Italy
Message-ID:
Dear Fieldtrip-ers,
There is one post-doctoral position for 18 months available in my research group (‘Brain Dynamics and Cognition Lab’; www.bdaclab.com) at the School of Psychology of Vita-Salute San Raffaele University and the Division of Neuroscience of I.R.C.C.S. San Raffaele Hospital in Milan (Italy).
This position is founded to work on project aimed at testing new rehabilitative trainings for reading impairments that make use of visuo-attentional training combined with transcranial alternating current stimulation (tACS) to improve the functionality/plasticity of the magnocellular-dorsal visual stream in adult participants with developmental dyslexia.
The ideal candidate should have (or should be about to obtain) a PhD in psychology, cognitive neuroscience, or a linked field, and research experience in using electroencephalography (EEG) and related data analysis. Experience with Eye-tracking is not mandatory but will be considered a plus.
The starting date is ideally December 2022 / January 2023, although there is some flexibility. More info about the position can be found here: https://www.bdaclab.com/open-positions
Interested candidates are welcome to send their CV directly to me (ronconi.luca at unisr.it). I’m also available for informal enquires about the position. The position will be available until fulfillment.
Thank you!
Kind regards,
Luca Ronconi
--
Prof. Luca Ronconi, Ph.D.
Associate Professor
School of Psychology
Vita-Salute San Raffaele University
via Olgettina 58, 20132, Milan, Italy
Phone (Office): +39 02 2643 4887
Phone (Lab.): +39 02 2643 6132
Website: www.bdaclab.com
Email: ronconi.luca at unisr.it
Rispetta l’ambiente: non stampare questa mail se non è necessario.
Respect the environment: print this email only if necessary.
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From janmathijs.schoffelen at donders.ru.nl Wed Oct 19 09:51:09 2022
From: janmathijs.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Wed, 19 Oct 2022 07:51:09 +0000
Subject: [FieldTrip] Mex file issue
In-Reply-To:
References:
Message-ID: <05408310-F1CB-468F-B9CC-8D1B56621CD2@donders.ru.nl>
Hi Samuel
I hardly ever use FieldTrip on a windows machine, and more in general, handling/maintaining one-shoe-fits-all mex-files on windows platforms is quite challenging. And compilation of mex files an windows platforms can be challenging as well. So I hope that windows users can chime in a bit here.
If you don’t manage to compile the code on your platform, and given that your original problem was with the mex file combineCllusters.mexw64, you could try to delete given mex-file altogether, and fall back to the (slower) m-file.
Best wishes and good luck,
Jan-Mathijs
On 5 Oct 2022, at 10:30, Samuel Harris Cosper via fieldtrip > wrote:
Dear all,
I am having an issue, as seems to be a common occurrence, with mex files and fieldtrip. I have double checked that “mex -setup” is working correctly and have also installed the MATLAB Support for MinGW-w64 C/C++ Compiler (version 22.1.0) already (in Matlab 2022a). I have also installed the most recent version of Fieldtrip on the Website/github. Another important factor: I have ran this exact script before on many computers with no issue. Only on my new laptop (windows 10) am I running into this issue. I have made sure that all updates are installed.
Now to my issue. When running ft_timelockstatistics with all of the proper cfg configurations, I receive an error message “Invalid MEX-file 'C:\Program Files\MATLAB\fieldtrip-20220104\private\combineClusters.mexw64': The specified module could not be found.”
I then ran “ft_compile_mex(true)”. Everything seemed to be working until “Compiling MEX file src/mxSerialize_cpp ... Building with 'MinGW64 Compiler (C++)'.” And I received the following error:
Error using ft_compile_mex>compile_mex_list
Error using mex
C:\Program Files\MATLAB\fieldtrip-20220104\src\mxSerialize_cpp.cpp:10:1: error: 'EXTERN_C' does not name a type
EXTERN_C mxArray* mxSerialize(mxArray const *);
^~~~~~~~
C:\Program Files\MATLAB\fieldtrip-20220104\src\mxSerialize_cpp.cpp:11:1: error: 'EXTERN_C' does not name a type
EXTERN_C mxArray* mxDeserialize(const void *, size_t);
^~~~~~~~
C:\Program Files\MATLAB\fieldtrip-20220104\src\mxSerialize_cpp.cpp: In function 'void mexFunction(int, mxArray**, int, const mxArray**)':
C:\Program Files\MATLAB\fieldtrip-20220104\src\mxSerialize_cpp.cpp:22:50: error: 'mxSerialize' was not declared in this scope
plhs[0] = (mxArray *) mxSerialize(prhs[0]);
^
Error in ft_compile_mex (line 114)
compile_mex_list(L, ftpath, force);
Is there a solution to this issue? I have looked online, but was unsuccessful, particularly for Matlab 2022a and with the newest version of Fieldtrip.
Thanks in advance!
Best wishes,
Sam
---
Dr.rer.nat. Samuel H. Cosper
Postdoc
Entwicklingspsychologie und Neurowissenschaft der Lebensspanne
(Lifespan Developmental Neuroscience)
Fakultät Psychologie (Faculty of Psychology)
Zellescher Weg 17, Raum A229
01062 Dresden
Email: samuel.cosper at tu-dresden.de
Technische Universität Dresden
_______________________________________________
fieldtrip mailing list
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
https://urldefense.com/v3/__https://doi.org/10.1371/journal.pcbi.1002202__;!!HJOPV4FYYWzcc1jazlU!6fFkX_c6xBjzVG_eLPIZL71U6GTCJldsWGNF1w3wnvpUnAXPZRhCB81IctVkI0s6HUbjRuBJhwEL845Dz6L8zWdECcTWVDMK7cLSqg$
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From n.molinaro at bcbl.eu Wed Oct 19 11:19:27 2022
From: n.molinaro at bcbl.eu (Nicola Molinaro)
Date: Wed, 19 Oct 2022 11:19:27 +0200 (CEST)
Subject: [FieldTrip] 3 years post-doc position - Brain Rhythms and Cognition
Message-ID: <1414444185.590862.1666171167248.JavaMail.zimbra@bcbl.eu>
Dear colleagues
the Brain Rhythms and Cognition group has an open post-doc position in a project focused on real-time closed-loop technology and language processing. We will test the applicability, conditions, and validity of an ad-hoc, adaptive, closed-loop system that will push the brain to self-regulation and plasticity.
More infos at https://www.bcbl.eu/en/join-us/job-offers/funded-postdoctoral-candidate-position-brain-rhythms-cognition-group
Nicola
------------------------------
Nicola Molinaro
www.ikerbasque.net/nicola.molinaro
Brain Rhythms and Cognition PI
http://www.bcbl.eu/groups/
Ikerbasque Associate Professor
www.ikerbasque.net
Legal disclaimer/Aviso legal/Lege-oharra: www.bcbl.eu/legal-disclaimer
From marta.vergara at uv.es Wed Oct 19 12:30:18 2022
From: marta.vergara at uv.es (=?UTF-8?Q?Marta_Vergara=2DMart=C3=ADnez?=)
Date: Wed, 19 Oct 2022 12:30:18 +0200
Subject: [FieldTrip] 18-month postdoc position in Valencia,
Spain -Reading Comprehension (Lalo Salmeron)
Message-ID:
Dear colleagues, could you please post this call?
thanks
*Postdoc position in Reading comprehension, with expertise on EEG
(University of Valencia) *Applications are invited for an 18-month
full-time postdoctoral position at the ERI Lectura of the University of
Valencia (* www.uv.es/lectura * )
The postdoctoral fellow will join the Digital Reading group. The selected
applicant is expected to work on an ongoing project aimed to analyze the
long-term effects of a digital reading comprehension training on
comprehension and attention, using behavioral as well as neurophysiological
measures. This is a basic research project with a strong emphasis on its
applications. The consortium incorporates an educational software company
and several participating high-schools.
The primary duties of the post holder include:
- collecting and analysing data from EEG and eye-movement experiments
- training graduate and undergraduate students
- disseminating results through publications.
Salary will be in accordance with the salary scale of the University of
Valencia, €1860 per month after tax (26278,92€ annual gross salary). The
selected applicant is expected to start on March, 2023 or soon afterward.
The work is based at the ERI Lectura ( *www.uv.es/lectura
* ), a multidisciplinary research institute for
the scientific study of reading, which currently hosts several basic as
well as applied projects. Our facilities are equipped with up-to-date
devices, including eye-trackers, EEG recording and analysis devices. The
Laboratory and its members have strong academic collaborations with
academic partners in Norway, Germany, France, Italy, UK and USA.
Qualifications: Applicants must have a PhD in Psychology, Linguistics, or a
related discipline, a background in reading comprehension,
psycholinguistics or a related field.
Expertise on EEG analyses (e.g., EEGlab, ERPlab, Fieldtrip, Matlab,... or
Python), good knowledge of eyetracking paradigms, ability to conduct
statistical analyses in R (linear mixed models), and an excellent command
of oral and written English, are important requirements. Knowledge of
Spanish or another Romance language is desirable, but not required.
To apply: Please e-mail your CV, a letter of motivation, a reference
letter, and a sample of accepted or published papers (max. of 3) to
lasalgon at uv.es. The application portal will be open soon.
For further details contact Lalo Salmerón (lasalgon at uv.es).
Expected closing date: February, 2023
--
+++++++++++++++++++++++++++++++++++++
Marta Vergara-Martínez, Prof. Titular
marta's web UV
Dpto de Psicología Evolutiva y de la Educación
Universitat de València
Av. Blasco Ibáñez, 21
46010-Valencia (Spain)
Marta.Vergara at uv.es
PHONE: +34 963983443
FAX: +34 963864671
+++++++++++++++++++++++++++++++++++++
CLÁUSULA DE CONFIDENCIALIDAD / CONFIDENTIALITY CLAUSE:
http://www.uv.es/websiuv/clausula_confidencialitat.html
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From nathanweisz at me.com Thu Oct 20 08:14:37 2022
From: nathanweisz at me.com (nathanweisz at me.com)
Date: Thu, 20 Oct 2022 06:14:37 +0000
Subject: [FieldTrip] open call at Univ Salzburg
Message-ID:
Dear all,
in an initiative to strengthen “digital science“ at the University of Salzburg, there are 3 professorship positions (~Assoc. Prof. level) to fill. The call is at the moment and the deadline is around the corner (24.10.).
https://www.plus.ac.at/wp-content/uploads/2022/09/§-99-1-EXDIGIT-englisch-1.pdf
This may be interesting for some of you who come more e.g. from a more data science or AI perspective. Cognitive neuroscience is a so-called focus area at the University of Salzburg (see https://ccns.gitlab.io).
This is short notice. But if you have your documents almost ready anyway, then give it a shot.
Best,
Nathan
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From evelyn.muschter at tu-dresden.de Thu Oct 20 08:51:05 2022
From: evelyn.muschter at tu-dresden.de (Evelyn Muschter)
Date: Thu, 20 Oct 2022 06:51:05 +0000
Subject: [FieldTrip] open call at Univ Salzburg
In-Reply-To:
References:
Message-ID: <3ACD72D3-0CE9-4A85-B0DC-CC4DFA313BDA@tu-dresden.de>
I am currently sick & out of the office until 21.10. 2022.
Evelyn Muschter
On 20. Oct 2022, at 08:14, nathanweisz--- via fieldtrip wrote:
Dear all,
in an initiative to strengthen “digital science“ at the University of Salzburg, there are 3 professorship positions (~Assoc. Prof. level) to fill. The call is at the moment and the deadline is around the corner (24.10.).
https://www.plus.ac.at/wp-content/uploads/2022/09/§-99-1-EXDIGIT-englisch-1.pdf
This may be interesting for some of you who come more e.g. from a more data science or AI perspective. Cognitive neuroscience is a so-called focus area at the University of Salzburg (see
https://ccns.gitlab.io).
This is short notice. But if you have your documents almost ready anyway, then give it a shot.
Best,
Nathan
_______________________________________________
fieldtrip mailing list
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
https://doi.org/10.1371/journal.pcbi.1002202
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From evelyn.muschter at tu-dresden.de Thu Oct 20 09:14:42 2022
From: evelyn.muschter at tu-dresden.de (Evelyn Muschter)
Date: Thu, 20 Oct 2022 07:14:42 +0000
Subject: [FieldTrip] open call at Univ Salzburg
In-Reply-To: <3ACD72D3-0CE9-4A85-B0DC-CC4DFA313BDA@tu-dresden.de>
References:
<3ACD72D3-0CE9-4A85-B0DC-CC4DFA313BDA@tu-dresden.de>
Message-ID: <440527A6-8BCE-435F-ACD0-78E5548486D3@tu-dresden.de>
I am currently sick & out of the office until 21.10. 2022.
Evelyn Muschter
On 20. Oct 2022, at 08:51, Evelyn Muschter via fieldtrip wrote:
I am currently sick & out of the office until 21.10. 2022.
Evelyn Muschter
On 20. Oct 2022, at 08:14, nathanweisz--- via fieldtrip wrote:
Dear all,
in an initiative to strengthen “digital science“ at the University of Salzburg, there are 3 professorship positions (~Assoc. Prof. level) to fill. The call is at the moment and the deadline is around the corner (24.10.).
https://www.plus.ac.at/wp-content/uploads/2022/09/§-99-1-EXDIGIT-englisch-1.pdf
This may be interesting for some of you who come more e.g. from a more data science or AI perspective. Cognitive neuroscience is a so-called focus area at the University of Salzburg (see
https://ccns.gitlab.io).
This is short notice. But if you have your documents almost ready anyway, then give it a shot.
Best,
Nathan
_______________________________________________
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
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From evelyn.muschter at tu-dresden.de Thu Oct 20 10:14:24 2022
From: evelyn.muschter at tu-dresden.de (Evelyn Muschter)
Date: Thu, 20 Oct 2022 08:14:24 +0000
Subject: [FieldTrip] open call at Univ Salzburg
In-Reply-To: <440527A6-8BCE-435F-ACD0-78E5548486D3@tu-dresden.de>
References:
<3ACD72D3-0CE9-4A85-B0DC-CC4DFA313BDA@tu-dresden.de>
<440527A6-8BCE-435F-ACD0-78E5548486D3@tu-dresden.de>
Message-ID:
I am currently sick & out of the office until 21.10. 2022.
Evelyn Muschter
On 20. Oct 2022, at 09:14, Evelyn Muschter via fieldtrip wrote:
I am currently sick & out of the office until 21.10. 2022.
Evelyn Muschter
On 20. Oct 2022, at 08:51, Evelyn Muschter via fieldtrip wrote:
I am currently sick & out of the office until 21.10. 2022.
Evelyn Muschter
On 20. Oct 2022, at 08:14, nathanweisz--- via fieldtrip wrote:
Dear all,
in an initiative to strengthen “digital science“ at the University of Salzburg, there are 3 professorship positions (~Assoc. Prof. level) to fill. The call is at the moment and the deadline is around the corner (24.10.).
https://www.plus.ac.at/wp-content/uploads/2022/09/§-99-1-EXDIGIT-englisch-1.pdf
This may be interesting for some of you who come more e.g. from a more data science or AI perspective. Cognitive neuroscience is a so-called focus area at the University of Salzburg (see
https://ccns.gitlab.io).
This is short notice. But if you have your documents almost ready anyway, then give it a shot.
Best,
Nathan
_______________________________________________
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
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From lizhipeng3924 at gmail.com Thu Oct 20 10:23:56 2022
From: lizhipeng3924 at gmail.com (zhipeng li)
Date: Thu, 20 Oct 2022 16:23:56 +0800
Subject: [FieldTrip] Cluster-based permutation tests
Message-ID:
Dear community,
1. When I make multiple comparisons based on cluster, does cfg.statistic =
'ft_statfun_depsamplesT' refer to the test of each sample in the first step
using paired t-test? And cfg.method = 'montecarlo' refers to the second
cluster test using a permutation test?
2. If that's the case, and we didn't adjust for multiple comparisons in
example 2 (cfg.correctm = 'no'), then cfg.statistic =
'ft_statfun_depsamplesT' is a paired t-test, and what is cfg.method =
'montecarlo'?
3. If this is the case, I would like to use non-parametric tests such as
the Wilcoxon test and Kruskal-Wallis test in the first step of each sample
test, which parameter in the cfg should be modified?
Example 1：Cluster-based permutation in FieldTrip
cfg = [];
cfg.channel = 'MEG';
cfg.neighbours = neighbours; % defined as above
cfg.latency = [0.3 0.7];
cfg.avgovertime = 'yes';
cfg.parameter = 'avg';
cfg.method = 'montecarlo';
cfg.statistic = 'ft_statfun_depsamplesT';
cfg.alpha = 0.05;
cfg.correctm = 'cluster';
cfg.correcttail = 'prob';
cfg.numrandomization = 'all';
Example 2：Permutation test based on t statistics in FieldTrip
cfg = [];
cfg.channel = 'MEG';
cfg.latency = [0.3 0.7];
cfg.avgovertime = 'yes';
cfg.parameter = 'avg';
cfg.method = 'montecarlo';
cfg.statistic = 'ft_statfun_depsamplesT';
cfg.alpha = 0.05;
cfg.correctm = 'no';
cfg.correcttail = 'prob';
cfg.numrandomization = 'all';
Best,
Zhipeng
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From evelyn.muschter at tu-dresden.de Thu Oct 20 11:14:10 2022
From: evelyn.muschter at tu-dresden.de (Evelyn Muschter)
Date: Thu, 20 Oct 2022 09:14:10 +0000
Subject: [FieldTrip] Cluster-based permutation tests
In-Reply-To:
References:
Message-ID: <074B1F90-6299-42D5-9BB7-5B13E0D7AD73@tu-dresden.de>
I am currently sick & out of the office until 21.10. 2022.
Evelyn Muschter
On 20. Oct 2022, at 10:23, zhipeng li via fieldtrip wrote:
Dear community,
1. When I make multiple comparisons based on cluster, does cfg.statistic = 'ft_statfun_depsamplesT' refer to the test of each sample in the first step using paired t-test? And cfg.method = 'montecarlo' refers to the second cluster test using a permutation test?
2. If that's the case, and we didn't adjust for multiple comparisons in example 2 (cfg.correctm = 'no'), then cfg.statistic = 'ft_statfun_depsamplesT' is a paired t-test, and what is cfg.method = 'montecarlo'?
3. If this is the case, I would like to use non-parametric tests such as the Wilcoxon test and Kruskal-Wallis test in the first step of each sample test, which parameter in the cfg should be modified?
Example 1：Cluster-based permutation in FieldTrip
cfg = [];
cfg.channel = 'MEG';
cfg.neighbours = neighbours; % defined as above
cfg.latency = [0.3 0.7];
cfg.avgovertime = 'yes';
cfg.parameter = 'avg';
cfg.method = 'montecarlo';
cfg.statistic = 'ft_statfun_depsamplesT';
cfg.alpha = 0.05;
cfg.correctm = 'cluster';
cfg.correcttail = 'prob';
cfg.numrandomization = 'all';
Example 2：Permutation test based on t statistics in FieldTrip
cfg = [];
cfg.channel = 'MEG';
cfg.latency = [0.3 0.7];
cfg.avgovertime = 'yes';
cfg.parameter = 'avg';
cfg.method = 'montecarlo';
cfg.statistic = 'ft_statfun_depsamplesT';
cfg.alpha = 0.05;
cfg.correctm = 'no';
cfg.correcttail = 'prob';
cfg.numrandomization = 'all';
Best,
Zhipeng
_______________________________________________
fieldtrip mailing list
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
https://doi.org/10.1371/journal.pcbi.1002202
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From evelyn.muschter at tu-dresden.de Thu Oct 20 11:14:10 2022
From: evelyn.muschter at tu-dresden.de (Evelyn Muschter)
Date: Thu, 20 Oct 2022 09:14:10 +0000
Subject: [FieldTrip] open call at Univ Salzburg
In-Reply-To:
References:
<3ACD72D3-0CE9-4A85-B0DC-CC4DFA313BDA@tu-dresden.de>
<440527A6-8BCE-435F-ACD0-78E5548486D3@tu-dresden.de>
Message-ID:
I am currently sick & out of the office until 21.10. 2022.
Evelyn Muschter
On 20. Oct 2022, at 10:14, Evelyn Muschter via fieldtrip wrote:
I am currently sick & out of the office until 21.10. 2022.
Evelyn Muschter
On 20. Oct 2022, at 09:14, Evelyn Muschter via fieldtrip wrote:
I am currently sick & out of the office until 21.10. 2022.
Evelyn Muschter
On 20. Oct 2022, at 08:51, Evelyn Muschter via fieldtrip wrote:
I am currently sick & out of the office until 21.10. 2022.
Evelyn Muschter
On 20. Oct 2022, at 08:14, nathanweisz--- via fieldtrip wrote:
Dear all,
in an initiative to strengthen “digital science“ at the University of Salzburg, there are 3 professorship positions (~Assoc. Prof. level) to fill. The call is at the moment and the deadline is around the corner (24.10.).
https://www.plus.ac.at/wp-content/uploads/2022/09/§-99-1-EXDIGIT-englisch-1.pdf
This may be interesting for some of you who come more e.g. from a more data science or AI perspective. Cognitive neuroscience is a so-called focus area at the University of Salzburg (see
https://ccns.gitlab.io).
This is short notice. But if you have your documents almost ready anyway, then give it a shot.
Best,
Nathan
_______________________________________________
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
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From janmathijs.schoffelen at donders.ru.nl Thu Oct 20 15:32:44 2022
From: janmathijs.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Thu, 20 Oct 2022 13:32:44 +0000
Subject: [FieldTrip] Cluster-based permutation tests
In-Reply-To:
References:
Message-ID:
Hi Zhipeng,
I think that the documentation on the website is clear enough about this, but to reiterate:
When you want to do non-parametric statistical inference, using permutation tests you specify cfg.method = ‘montecarlo’. This does not specify yet how to address the multiple comparisons problem.
The correction scheme for the multiple comparisons problem is specified by cfg.correctm. This can be set to ‘cluster’, which means that you use a clustering heuristic to reduce the number of comparisons to 1 (or 2, if two-sided testing is involved).
The test statistic that you boil out of your data - and that is going to be used to construct the permutation distribution under the null hypothesis of exchangeability - is specified by cfg.statistic. The value for this option should refer to an existing matlab m-file that converts the input data into the required test statistic. Fieldtrip comes shipped with a few pre-cooked ones, e.g. the (in)dependent samples T-statistic. If you want some more adventurous test statistics, such as the one you mentioned, you need to write your own ’statfun’. Feel free to contribute your own statfuns as a pull request to the fieldtrip code base.
Best wishes and good luck,
Jan-Mathijs
> On 20 Oct 2022, at 10:23, zhipeng li via fieldtrip wrote:
>
> Dear community,
>
> 1. When I make multiple comparisons based on cluster, does cfg.statistic = 'ft_statfun_depsamplesT' refer to the test of each sample in the first step using paired t-test? And cfg.method = 'montecarlo' refers to the second cluster test using a permutation test?
>
> 2. If that's the case, and we didn't adjust for multiple comparisons in example 2 (cfg.correctm = 'no'), then cfg.statistic = 'ft_statfun_depsamplesT' is a paired t-test, and what is cfg.method = 'montecarlo'?
>
> 3. If this is the case, I would like to use non-parametric tests such as the Wilcoxon test and Kruskal-Wallis test in the first step of each sample test, which parameter in the cfg should be modified?
>
> Example 1：Cluster-based permutation in FieldTrip
>
> cfg = [];
> cfg.channel = 'MEG';
> cfg.neighbours = neighbours; % defined as above
> cfg.latency = [0.3 0.7];
> cfg.avgovertime = 'yes';
> cfg.parameter = 'avg';
> cfg.method = 'montecarlo';
> cfg.statistic = 'ft_statfun_depsamplesT';
> cfg.alpha = 0.05;
> cfg.correctm = 'cluster';
> cfg.correcttail = 'prob';
> cfg.numrandomization = 'all';
>
> Example 2：Permutation test based on t statistics in FieldTrip
>
> cfg = [];
> cfg.channel = 'MEG';
> cfg.latency = [0.3 0.7];
> cfg.avgovertime = 'yes';
> cfg.parameter = 'avg';
> cfg.method = 'montecarlo';
> cfg.statistic = 'ft_statfun_depsamplesT';
> cfg.alpha = 0.05;
> cfg.correctm = 'no';
> cfg.correcttail = 'prob';
> cfg.numrandomization = 'all';
>
> Best,
> Zhipeng
>
> _______________________________________________
> fieldtrip mailing list
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> https://urldefense.com/v3/__https://doi.org/10.1371/journal.pcbi.1002202__;!!HJOPV4FYYWzcc1jazlU!6plwN9AGqY-PwZFmuMuA5NLPiL9zZ6oQM3C3vFAhrWI6RWLh-D5Xvwq1S5O7Io0ezYDxf2HbJQi9p8eVwRtO4OlMvYpJ-668T202uA$
From akul.satish at york.ac.uk Thu Oct 20 16:32:56 2022
From: akul.satish at york.ac.uk (Akul Satish)
Date: Thu, 20 Oct 2022 15:32:56 +0100
Subject: [FieldTrip] ft_sourcestatistics not working as expected
Message-ID:
Dear Fieldtrippers,
I am trying to run a paired-samples t-test at source level using the output
structure of the ft_sourceanalysis function. Although the script does run
without any errors, I am getting a p-value of 0.002 and "stat" = Infinity
at all 3294 voxels within the brain. Note that I have attempted to run
these tests with 1) no correction for multiple comparisons, 2) cluster
correction, and 3) varying participant numbers. I am getting the same
result regardless of these 3 changes.
However, when I plot the grand-averaged raw 'pow' differences, the plots
make sense - there is localised activity from some brain regions that could
be involved in the task, but no activity in other brain regions.
I am not sure what I am doing wrong and any help would be appreciated!
I have run the analysis in the following way:
1) LCMV beamrforming to obtain time-series data at each voxel/virtual
sensor/dipole (using the 3D volumetric grid)
2) time-frequency decomposition to determine baseline-normalised power at
each point in the voxel x frequency x time data matrix
3) averaged data over a certain freq x time cluster region, at each voxel
4) For each participant and condition of interest, created a dummy output
variable from ft_sourceanalysis and replaced the averaged power from step
(3) into the 'avg.pow' variable at each voxel
5) Run ft_sourcestatistics using the following parameters:
cfg = [];
cfg.dim = SourceTF_forstat.overlap{1}.dim;
cfg.method = 'montecarlo';
cfg.statistic = 'depsamplesT';
cfg.parameter = 'pow';
cfg.correctm = 'no';
%cfg.alpha = 0.05;
%cfg.numrandomization = 1000;
%cfg.tail = 0;
%cfg.correcttail = 'prob';
% Specify Design Matrix
subj = numel(SourceTF_forstat.overlap);
design = [1:subj, 1:subj];
design(2,1:subj) = 1;
design(2,subj+1:2*subj) = 2;
cfg.design = design;
cfg.uvar = 1;
cfg.ivar = 2;
cfg.spmversion = 'spm12';
SourceClusterResult = ft_sourcestatistics(SourceTF_forstat.overlap{:},
SourceTF_forstat.nonoverlap{:})
I used the following documentation for help with ft_sourcestatistics:
https://www.fieldtriptoolbox.org/example/source_statistics/
Thanks a lot,
Akul
*___________________________________________Dr Akul Satish | Post-D**octoral
Research Associate*
B218, Department of Psychology
University of York
York YO10 5DD
akul.satish at york.ac.uk
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From greig.dezubicaray at qut.edu.au Mon Oct 24 01:10:14 2022
From: greig.dezubicaray at qut.edu.au (Greig de Zubicaray)
Date: Sun, 23 Oct 2022 23:10:14 +0000
Subject: [FieldTrip] 2 PhD Scholarships in Psycholinguistics and
Neurobiology of Language (Queensland University of Technology, Australia)
Message-ID:
Dear colleagues,
We have 2 PhD scholarship/stipends associated with Australian Research Council (ARC) funded projects in our lab (https://research.qut.edu.au/lcbs/) at Queensland University of Technology (QUT), Brisbane, Australia.
The aim of Project 1 is to investigate the cognitive and neural mechanisms of language production using psycholinguistic, scalp and intracranial EEG, fMRI and TMS methods. Investigators are Greig de Zubicaray (QUT), Katie McMahon (QUT), Angelique Volfart (QUT), Vitória Piai (Radboud University) and Catherine Liégeois-Chauvel (University of Pittsburgh)
The aim of Project 2 is to investigate the role of language statistics/statistical learning mechanisms in production and comprehension using psycholinguistic, fMRI, EEG and TMS methods. Investigators are Greig de Zubicaray (QUT), Katie McMahon (QUT), Elaine Kearney (QUT), Joanne Arciuli (Flinders University) and Frank Guenther (Boston University)
Applicants interested in the PhD scholarships should contact Prof Greig de Zubicaray (greig.dezubicaray at qut.edu.au)
The scholarships also come with tuition fee sponsorships (i.e., waivers) for international students, as explained here:
https://www.qut.edu.au/study/fees-and-scholarships/scholarships/qut-postgraduate-research-award-qutpra
Eligible applicants will:
* have completed a postgraduate qualification (MPhil or MSc) administered and assessed in English from an institution in the EU, Norway, Switzerland, Canada, USA, Australia, New Zealand or UK.
For more information on how to apply, please visit:
https://www.qut.edu.au/research/study-with-us/phd
--
Greig de Zubicaray, MClinPsych(Neuro), PhD, FAPS
Professor | Psychology and Counselling | Faculty of Health | QUT
Phone: +61 (0)7 3138 9639 | Email: greig.dezubicaray at qut.edu.au
B629, Level 6, B Wing, O Block, Kelvin Grove, Brisbane, QLD 4059.
Web: https://research.qut.edu.au/lcbs/
ORCID: orcid.org/0000-0003-4506-0579
ResearcherID: researcherid.com/rid/B-1763-2008
CRICOS No. 00213J
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From marios.philiastides at gmail.com Mon Oct 24 15:00:23 2022
From: marios.philiastides at gmail.com (Marios Philiastides)
Date: Mon, 24 Oct 2022 14:00:23 +0100
Subject: [FieldTrip] Postdoc position in theoretical/computational
neuroscience @CCNi UoGlasgow
In-Reply-To:
References:
Message-ID:
Dear all (apologies for cross posting),
We have an opportunity for a postdoctoral fellow to make a leading
contribution to an ERC-funded project on human decision making and learning
at the Centre for Cognitive Neuroimaging (CCNi) at the University of
Glasgow.
This job has a strong theoretical neuroscience component and requires the
development of computational models to uncover and predict patterns in
large multimodal datasets (e.g. behavior, simultaneous EEG-fMRI and
eye-tracking data). Moreover, the work will focus on the development of a
computational framework, in which neural data can be used directly to
constrain parameter estimation (i.e. neurally-informed cognitive modelling)
to enable mechanistic insights that might otherwise be missed with a
conventional modelling approach (i.e. based on behaviour alone).
The post-holder will be contributing to the design and programming of
experiments, recruiting and running the participants. The post holder will
also have the opportunity to be involved in cutting-edge bespoke
neuroimaging technology [7TfMRI-EEG] and will be expected to work closely
with other postdoctoral fellows working on the same project.
This post is full time, and has funding for up to 31st May 2026.
For further information, please contact me at
Marios.Philiastides at glasgow.ac.uk.
Apply online at:
https://my.corehr.com/pls/uogrecruit/erq_jobspec_version_4.jobspec?p_id=095728
Closing Date: 10th November 2022.
--
Marios G. Philiastides, Ph.D.
Professor of Decision Neuroscience
Institute of Neuroscience and Psychology
Centre for Cognitive Neuroimaging
University of Glasgow
Glasgow, G12 8QB
Web: http://decision.ccni.gla.ac.uk/
Email: marios.philiastides at glasgow.ac.uk
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From ivaylo.iotchev at gmail.com Mon Oct 24 16:33:41 2022
From: ivaylo.iotchev at gmail.com (Ivaylo Iotchev)
Date: Mon, 24 Oct 2022 16:33:41 +0200
Subject: [FieldTrip] simple question on frequency space representation
Message-ID:
Dear all,
this might be an easy one to answer, but being relatively new to Fieldtrip
I think the fastest way for me to get there is to ask:
How do I control the padding size in Fieldtrip? So far all examples I have
encountered in order to learn the programm only specify that it happens or
not, e.g.
cfg.taper = 'Hanning'
However, my end-result doesn't give me the 0.1 Hz steps I am used to from
specifying zero-padding in spectrogram, where I can have a clean succession
of 0.1 Hz steps, e.g. 1.1 Hz, 1.2 Hz, 1.3 Hz... and so on...
Instead I currently get something like:
0.9 Hz, 1.9 Hz, 2.9 Hz...
The latter resolution increases a little bit by specifying 0.1 Hz steps in
the cfg.foi, but never reaches the fine-grained 0.1 steps of spectrogram. I
know it's my bad, if you have time shine me the light :)
Thanks in advance,
Ivaylo (Ivo) Iotchev, PhD
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From tzvetan.popov at uni-konstanz.de Mon Oct 24 16:56:07 2022
From: tzvetan.popov at uni-konstanz.de (Tzvetan Popov)
Date: Mon, 24 Oct 2022 16:56:07 +0200
Subject: [FieldTrip] simple question on frequency space representation
In-Reply-To:
References:
Message-ID: <0620FC2C-B3C7-40A2-AF05-AA264F3A3849@uni-konstanz.de>
Dear Ivo,
cfg.pad=9; will add 9 sec of zero padding to your 1 sec signal that will result in your intended resolution.
Good luck,
Tzvetan
> Am 10/24/22 um 16:36 schrieb Ivaylo Iotchev via fieldtrip :
>
>
> Dear all,
>
> this might be an easy one to answer, but being relatively new to Fieldtrip I think the fastest way for me to get there is to ask:
>
> How do I control the padding size in Fieldtrip? So far all examples I have encountered in order to learn the programm only specify that it happens or not, e.g.
>
> cfg.taper = 'Hanning'
>
> However, my end-result doesn't give me the 0.1 Hz steps I am used to from specifying zero-padding in spectrogram, where I can have a clean succession of 0.1 Hz steps, e.g. 1.1 Hz, 1.2 Hz, 1.3 Hz... and so on...
>
> Instead I currently get something like:
>
> 0.9 Hz, 1.9 Hz, 2.9 Hz...
>
> The latter resolution increases a little bit by specifying 0.1 Hz steps in the cfg.foi, but never reaches the fine-grained 0.1 steps of spectrogram. I know it's my bad, if you have time shine me the light :)
>
> Thanks in advance,
>
> Ivaylo (Ivo) Iotchev, PhD
> _______________________________________________
> fieldtrip mailing list
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
> https://doi.org/10.1371/journal.pcbi.1002202
From ivaylo.iotchev at gmail.com Mon Oct 24 16:58:54 2022
From: ivaylo.iotchev at gmail.com (Ivaylo Iotchev)
Date: Mon, 24 Oct 2022 16:58:54 +0200
Subject: [FieldTrip] simple question on frequency space representation
In-Reply-To: <0620FC2C-B3C7-40A2-AF05-AA264F3A3849@uni-konstanz.de>
References:
<0620FC2C-B3C7-40A2-AF05-AA264F3A3849@uni-konstanz.de>
Message-ID:
Thank you/blagodarya!
Am Mo., 24. Okt. 2022 um 16:56 Uhr schrieb Tzvetan Popov via fieldtrip <
fieldtrip at science.ru.nl>:
> Dear Ivo,
> cfg.pad=9; will add 9 sec of zero padding to your 1 sec signal that will
> result in your intended resolution.
>
> Good luck,
> Tzvetan
>
> > Am 10/24/22 um 16:36 schrieb Ivaylo Iotchev via fieldtrip <
> fieldtrip at science.ru.nl>:
> >
> >
> > Dear all,
> >
> > this might be an easy one to answer, but being relatively new to
> Fieldtrip I think the fastest way for me to get there is to ask:
> >
> > How do I control the padding size in Fieldtrip? So far all examples I
> have encountered in order to learn the programm only specify that it
> happens or not, e.g.
> >
> > cfg.taper = 'Hanning'
> >
> > However, my end-result doesn't give me the 0.1 Hz steps I am used to
> from specifying zero-padding in spectrogram, where I can have a clean
> succession of 0.1 Hz steps, e.g. 1.1 Hz, 1.2 Hz, 1.3 Hz... and so on...
> >
> > Instead I currently get something like:
> >
> > 0.9 Hz, 1.9 Hz, 2.9 Hz...
> >
> > The latter resolution increases a little bit by specifying 0.1 Hz steps
> in the cfg.foi, but never reaches the fine-grained 0.1 steps of
> spectrogram. I know it's my bad, if you have time shine me the light :)
> >
> > Thanks in advance,
> >
> > Ivaylo (Ivo) Iotchev, PhD
> > _______________________________________________
> > 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
>
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From luy8357 at naver.com Tue Oct 25 05:45:41 2022
From: luy8357 at naver.com (=?utf-8?B?7J207Jqw7Jqp?=)
Date: Tue, 25 Oct 2022 12:45:41 +0900
Subject: [FieldTrip] =?utf-8?q?ft=5Fsourceanalysis_-_connectivity?=
In-Reply-To:
References: <52fc8b97e346fa91e637f1ec97c7376@cweb002.nm.nfra.io>
Message-ID: <1fdf13f9b3a875d2fb2ba1da0f9d06@cweb005.nm.nfra.io>
hello, everyone
When using ft_sourceanalysis, (sourcemodel: cortex_8196.surf.gii') If i use the lcmv method or the eloreta method, source.avg.pow is obtained.
As far as I know, I understood that pow represents connectivity between 8196 areas, Is this correct?
And if it is a wrong concept, what method is used to obtain connectivity between regions?
and I was also wondering if there is a way to represent both negative and positive values in representing connectivity.
Thank you and Best Regards
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From mikkelcv at drcmr.dk Tue Oct 25 09:26:08 2022
From: mikkelcv at drcmr.dk (mikkelcv at drcmr.dk)
Date: Tue, 25 Oct 2022 07:26:08 +0000
Subject: [FieldTrip] ft_sourceanalysis - connectivity
In-Reply-To: <1fdf13f9b3a875d2fb2ba1da0f9d06@cweb005.nm.nfra.io>
References: <52fc8b97e346fa91e637f1ec97c7376@cweb002.nm.nfra.io>
<1fdf13f9b3a875d2fb2ba1da0f9d06@cweb005.nm.nfra.io>
Message-ID:
Dear 이우용
Source.avg.pow is the power of the source in whatever unit the source reconstruction algorithm provide. The length of source.avg.pow should be 8196 representing one value per source point.
How to obtain connectivity between brain regions does not have a straight forward answer. It depends on a lot of different processing steps and decisions, e.g., how you define “brain regions”, what connectivity measure you are interest in, how you do the source reconstruction, etc.. You can take a look at ft_connectivityanalysis to see how and what you can calculate connectivity in FieldTrip. See also the tutorials on FieldTrip website for more detailed how to: https://www.fieldtriptoolbox.org/tutorial/#connectivity-analysis
Best regards
Mikkel
Fra: fieldtrip på vegne af via fieldtrip
Dato: tirsdag, 25. oktober 2022 kl. 05.46
Til: fieldtrip at science.ru.nl
Cc: 이우용
Emne: [FieldTrip] ft_sourceanalysis - connectivity
hello, everyone
When using ft_sourceanalysis, (sourcemodel: cortex_8196.surf.gii') If i use the lcmv method or the eloreta method, source.avg.pow is obtained.
As far as I know, I understood that pow represents connectivity between 8196 areas, Is this correct?
And if it is a wrong concept, what method is used to obtain connectivity between regions?
and I was also wondering if there is a way to represent both negative and positive values in representing connectivity.
Thank you and Best Regards
[https://mail.naver.com/readReceipt/notify/?img=PrY9KqCl%2B6w4pzMwhAnXKxF4a6UqMx2XF6tZp4UZM4uwpzumpo0vKApgMX%2B0MogmFLl5WLl5pNiC740ThoR416knpH3Z1BIgbrFGp6lopLlZ%2BLl5Wg%3D%3D.gif]
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From AFARNELL at tcd.ie Tue Oct 25 14:37:40 2022
From: AFARNELL at tcd.ie (Adelais Farnell Sharp)
Date: Tue, 25 Oct 2022 12:37:40 +0000
Subject: [FieldTrip] Research Assistant - Job Vacancy
Message-ID:
Hello everyone!
We have another Research Assistant vacancy at the Academic Unit of Neurology in Trinity College Dublin. The successful applicant will be working with the Irish ALS Research Group, supporting the neurophysiology and neural signal analysis research team.
The ideal candidate will have an undergraduate or master’s degree in Engineering (including but not limited to Neural, Biomedical, or Electrical Engineering), Computer Science, Bio-Statistics, Bio/Neuro-informatics, Mathematics, Physics, Data Sciences, or other computational/quantitative sciences such as Computational Neuroscience. A strong interest in neural engineering/neuroscience and a strong understanding of MATLAB would also be desirable. Any experience in recording or analysing electrophysiological signals (i.e. electroencephalography (EEG) and electromyography (EMG) would be a bonus.
The application deadline for this post is the 14th November 2022, at 12 noon.
Best of luck!
Adelais
AUoN
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From brooks.jl at gmail.com Fri Oct 28 17:33:31 2022
From: brooks.jl at gmail.com (Joseph Brooks)
Date: Fri, 28 Oct 2022 16:33:31 +0100
Subject: [FieldTrip] Lecturer (Assistant Professor Equivalent) in Cog Psych
and/or Cog Neuro @ Keele University, UK
Message-ID:
Dear Colleagues,
The School of Psychology at Keele University in the UK is advertising a
permanent Lecturer position (Associate Prof equivalent) in cognitive psych
and/or cognitive neuroscience. We welcome applicants who align with or
complement the research strengths of our Cognition, Brain, & Behaviour
Research group
and
who can contribute to our undergraduate Psychology programmes, postgraduate
programmes (including MSc in Cognition & Cognitive Neuroscience), and PhD
supervision.
For more information and to apply, please see the advert at:
https://www.jobs.ac.uk/job/CUK317/lecturer-in-psychology-education-and-research
All the best,
Joe
--------------------------------------------
Dr Joseph Brooks
Director of Research, School of Psychology
Senior Lecturer in Psychology
School of Psychology, Keele University
Office: Dorothy Hodgkin Building 1.74
Lab Website: https://sites.google.com/view/brookslab
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From janmathijs.schoffelen at donders.ru.nl Sat Oct 29 17:53:58 2022
From: janmathijs.schoffelen at donders.ru.nl (Schoffelen, J.M. (Jan Mathijs))
Date: Sat, 29 Oct 2022 15:53:58 +0000
Subject: [FieldTrip] Fwd: alpha one is looking for a senior EEG scientist
References: <6FEBA459-6D3B-4822-B626-CCC19A8B6DB2@donders.ru.nl>
Message-ID: <53D5645B-367E-4CBB-A156-6227CEC42CF4@donders.ru.nl>
Dear all,
On behalf of Ingrid Nieuwenhuis, a FieldTrip alumnus, I would like to bring the below job ad to your attention. It might be of interest to some of you. It is the same position that was advertised a while ago (see below), but the strict requirement of being proficient in Dutch has been relaxed a bit. Here’s a link to the refreshed job advert: https://www.alpha.one/vacatures/senior-eeg-scientist-en
Best wishes,
Jan-Mathijs
+++++ old job advert from early october, slightly different from the current one +++++
Looking for Senior EEG scientist - (close to) PhD level - Location Amsterdam
Are you a methodologically strong neuroscientist with a lot of experience in M/EEG? Do you enjoy translating scientific insights into practical recommendations that customers can use? Then you might be our new Senior EEG Scientist!
About the company, Alpha.One
Alpha.One is a young Dutch company that operates in the domain where neuroscience, marketing and machine learning (AI) meet.
The Senior EEG scientist will be part of the Alpha.One science team. This team conducts neuroscience lab research using EEG and eye tracking. With these tools we generate actionable insights for customers to develop better advertising and communication. In addition, the science team is responsible for the scientific development of the AI-powered ad testing platforms expoze.io and Junbi.ai. These platforms allow users to test the effectiveness of advertisements without the need for participants.
The core values of Alpha.One are rooted-in-science, actionable-results, and what-you-see-is-what-you-get. In addition to developing marketing products, we also develop applications and products to increase social impact and diversity.
About the role
As a Senior EEG scientist you stand with one leg firmly in science. You manage the team of junior researchers and carry out the EEG and eye tracking research with them. You analyze the data according to our standardized methods, and work on optimizing the analysis pipeline. You contribute to the development of new applications based on machine learning and you are jointly responsible for supervising master students who do an internship in the science team. With the other leg you stand on the customer side in marketing. We don't expect you to have experience with this part, but you are interested in it. You will be trained in this and grow and develop this skill on the job. You learn to translate the client’s research questions into a robust research proposal. You will also work on reports and presentations in which the research findings are translated into practical recommendations for our clients.
Profile
*
You are a methodologically strong neuroscientist and enjoy translating scientific insights into actionable recommendations that customers can use.
*
You have at least three years of full-time experience in independently developing, performing and analyzing M/EEG experiments.
*
Preferably you have a PhD. Fields of study: neuroscience, cognitive psychology or a related field. But if you discovered during your PhD research that the academic system isn't for you, and didn't finish your PhD, we get it.
*
You have a strong proficiency in programming and data analysis, in Python and/or Matlab.
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You have an excellent command of the Dutch and English language.
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You are proactive, solution-oriented, able to plan well and set priorities.
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You are good at communicating, both verbally, in writing and visually. You can translate complex scientific findings into useful insights.
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You are able to work approximately 50% of the time in our Neurolab in Amsterdam (Marnixkade 109F).
We offer:
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Working in a team with passionate, intelligent and supportive colleagues from different scientific fields.
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Working on bringing together the two most interesting scientific fields, neuroscience and AI, into practical applications.
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A hybrid work environment with flexible working hours.
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A flat organizational structure in which you have intellectual freedom and the opportunity to implement your ideas.
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An intellectually challenging environment, where we’re always working on something new, and where it is never boring.
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Good supervision, so that you can continue to grow and develop.
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A salary comparable to the salary level at universities (at PhD or postdoc level depending on experience).
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An office/lab on the Marnixkade in Amsterdam
At Alpha.One we understand that every person is different, with their own talents, dreams and ambitions. That is why we strive to help you create a career that matches your ambitions.
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_______________________________________________
fieldtrip mailing list
https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
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