[FieldTrip] Rejecting BrainVision-marked 'Bad Intervals'

[Kate Stone]

Brilliant! Thanks Daniel, this worked perfectly.


On Wed., 17 Oct. 2018, 15:35 Daniel Matthes, <dmatthes at cbs.mpg.de> wrote:

> Hi Kate,
>
> I did it in the following way:
>
> First, I did a regular import by ignoring the 'Bad Interval' markers.
>
> % -------------------------------------------------------------------------
> % Data import
> % -------------------------------------------------------------------------
>
> cfg              = [];
> cfg.dataset      = headerfile;
> cfg.trialfun     = 'ft_trialfun_brainvision_segmented';
> cfg.stimformat   = 'S %d';
> cfg.showcallinfo = 'no';
>
> cfg = ft_definetrial(cfg);
> data = ft_preprocessing(cfg);
>
> After that step I have all trials in my data structure, also the trials
> which have bad intervals.
>
> In the second step I did this with the data:
>
> % -------------------------------------------------------------------------
> % Estimate artifacts
> % -------------------------------------------------------------------------
> events = data.cfg.event;
>   % extract all events from the data structure
> artifact = zeros(length(events), 2);
>   % allocate memory for the artifact array
> j = 1;
>
> for i=1:1:length(events)
>   if(strcmp(events(i).type, 'Bad Interval'))
>   % search for bad interval events
>     artifact(j,1)=events(i).sample;
>  % create artifact matrix
>     artifact(j,2)=events(i).sample + events(i).duration - 1;
>     j = j +1;
>   end
> end
>
> artifact = artifact(1:j-1, :);
>   % prune the artifact array to its actual size
>
> % -------------------------------------------------------------------------
> % Revise data
> % -------------------------------------------------------------------------
> cfg                           = [];
> cfg.event                     = events;
> cfg.artfctdef.reject          = 'complete';
> cfg.artfctdef.feedback        = 'no';
> cfg.artfctdef.xxx.artifact    = artifact;
> cfg.showcallinfo              = 'no';
>
> data = ft_rejectartifact(cfg, data);
>
> These line are removing all trials with bad intervals completely from the
> data structure. But if you set the option cfg.artfctdef.reject to another
> value i.e. 'partial', you can also remove only the bad parts of certain
> trials
>
> I wrote this code some time ago, today I would replace the for-cycle with
> some more effective code. But in general it should work.
>
> All the best,
> Daniel
>
> ----- Original Message -----
> From: "K S" <katemsto at gmail.com>
> To: "bioeng yoosofzadeh" <bioeng.yoosofzadeh at gmail.com>
> Cc: fieldtrip at science.ru.nl
> Sent: Wednesday, October 17, 2018 12:01:46 PM
> Subject: Re: [FieldTrip] Rejecting BrainVision-marked 'Bad Intervals'
>
> Hi Vahab,
>
> Thanks for the response.
>
> The data is already epoched so I tried ft_redefine trial as you suggested.
> I think the problem is that the segments marked as artefact are also marked
> with triggers. I therefore need some way of saying:
>
> "cfg.trials = {'s 1', 's 2'} except those also marked 'Bad Interval'"
>
> I even tried cfg.trials = not('Bad Interval') - it doesn't work. I tried
> it also with ft_rejectartifact but I'm not sure how to get it to recognise
> the 'Bad Interval' marking.
>
> Any ideas?
>
> Many thanks,
> Kate
>
>
> On Tue, Oct 16, 2018 at 10:13 PM Vahab Yousofzadeh <
> bioeng.yoosofzadeh at gmail.com > wrote:
>
>
> Hi Kate,
>
> if it is a continuous data (before epoching), you can treat bad
> segments as an artifact (e.g. eog or muscle, etc) and do something
> like this,
>
> % artifact_EOG = [100 500]; % in sample
>
> cfg = [];
> cfg.artfctdef.eog.artifact = artifact_EOG;
> cfg.artfctdef.reject = 'value';
> % cfg.artfctdef.value = 0; % replacing values with nan or 0
> data_continuous_eog_clean = ft_rejectartifact(cfg, data); % data is
> the output from ft_preprocessing.
>
> % inspecting cleaned data
> cfg = [];
> cfg.continuous = 'yes';
> cfg.viewmode = 'vertical'; % all channels seperate
> cfg.blocksize = 5; % view the continous data in 30-s blocks
> ft_databrowser(cfg, data_continuous_eog_clean);
>
> if the data is epoched, simply use ft_redefinetrial.
>
> Best,
> Vahab
>
>
> --
> Kate Stone
> PhD candidate
> Vasishth Lab | Department of Linguistics
> Potsdam University, 14467 Potsdam, Germany
> https://auskate.github.io
>
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