[FieldTrip] Combine multiple blocks

Tue Nov 20 09:07:58 CET 2018

Pramod,

Your queries are lacking detail:  i.e. there is no detail as to what you actually want to do (e.g. type of source reconstruction etc.), nor any detail about what you have tried yourself so far. Therefore, it is hard to give constructive feedback/directions.

I suggest to first try and get a running pipeline to do source reconstruction using data from a single block. Then it’s relatively straightforward to extend this to a multiple block setting, where the exact sensible directions to take depend on the source reconstruction algorithm and the quality of the data per block. One possibility would be, as I already mentioned, to recover a more or less useable grad structure, using ft_average_sens. Alternatively, you could do the source reconstruction per block, and combine afterwards.

Good luck,
Jan-Mathijs

J.M.Schoffelen, MD PhD
Senior Researcher, VIDI-fellow - PI, language in interaction
Telephone: +31-24-3614793
Physical location: room 00.028
Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands

On 19 Nov 2018, at 23:04, Gaur, Pramod <p.gaur at ulster.ac.uk<mailto:p.gaur at ulster.ac.uk>> wrote:

Hi Jan-Mathijs,

I want to do source analysis. I have 3 blocks with 40 trials each for one subject and want to do the source analysis. Yes, you pointed it correctly “ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs.” Please advise me how to do the source analysis on them.

Best regards,
Pramod

From: fieldtrip [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Schoffelen, J.M. (Jan Mathijs)
Sent: 19 November 2018 19:51
To: FieldTrip discussion list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Subject: Re: [FieldTrip] Combine multiple blocks

Pramod,

I don’t understand how ‘combining all the three blocks before the preprocessing’ would help you to preserve the channel positions. Apparently, these are different for each of the blocks in your case. It’s also not clear to me, why this is causes problem in your specific case.

Anyway, ft_appenddata throws the grad-field away, since it recognizes that the channel (and coil) positions are different in each of the runs.
If you still want to obtain some kind of ‘average’ representation of the channel position, you can use ft_average_sens. If the positions are not altogether too different, it might be OK to average the positions across blocks, although technically it’s of course incorrect to do so. Yet, if for each of the blocks you have applied block-specific spatial transformations (e.g. maxfilter) then it’s a different story altogether, and things will become hairy very rapidly.

Best wishes,

Jan-Mathijs

On 19 Nov 2018, at 19:19, Gaur, Pramod <p.gaur at ulster.ac.uk<mailto:p.gaur at ulster.ac.uk>> wrote:

Dear Team,

I have a quick concern, I have a cognitive task and recorded three blocks. Is there any way in which I can combine all the three blocks before the preprocessing. I have alternate to preprocess the blocks separately and then combining them. In this case, I lose the data_MEG_filt                   .grad.chanpos location.

cfg = [];
    cfg.dataset = filename;
    % % cfg = ft_definetrial(cfg);
    cfg.trialdef.eventtype = 'STI101';
    %cfg.trialdef.eventtype = 'gui';
    cfg.trialdef.eventvalue = [16 17 18 32 33 34]% 17 18];% put your own
    cfg.trialdef.prestim        = prestim;
    cfg.trialdef.poststim       = poststim;
    %     cfg.length                  = 1;
    cfg                         = ft_definetrial(cfg);

    % read in the data from the magnetometer
    % cfg.channel = {'MEGMAG','STI101'};
    cfg.channel                 = {'MEG'};%,'-MEG2331','-MEG0122'};%%{'MEG*2','MEG*3'};%selected_sensors;%{'MEG'};%
    % cfg.lpfilter                = 'yes';
    % cfg.lpfreq                  = 40;
    % cfg.hpfilter                = 'yes';
    % cfg.hpfreq                  = 1;
    cfg.continuous              = 'yes';
    cfg.detrend                 = 'no';
    cfg.demean                  = 'yes';
    cfg.dftfilter               = 'yes';
    % cfg.dftfreq                 =[50 100 150];% power line noise
    cfg.bpfreq        = [1 48];
    cfg.metric                  = 'zvalue';
    cfg.layout                  = 'neuromag306all.lay';
    cfg.baselinewindow = [-0.5 0];
    data_MEG_filt                   = ft_preprocessing(cfg);

    if isempty(data_MEG_filt)
    data_MEG_filt=data;
    grad = data.grad;
    else
    cfg = [];
    data_MEG_filt = ft_appenddata(cfg, data_MEG_filt,data);
    end

Any advice will be highly appreciated.

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Dr Pramod Gaur
Research Assistant in Neuro-Imaging Technology
School of Computing, Engineering and Intelligent Systems
Magee Campus
E: p.gaur at ulster.ac.uk<mailto:p.gaur at ulster.ac.uk>  W: www.ulster.ac.uk<http://www.ulster.ac.uk/>
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