[FieldTrip] EEG source reconstruction using DICS method

[Elena Krugliakova]

Dear Fieldtrip community,

I have a question regarding source reconstruction using the 'dics' method applied to EEG data.
I have two problems: first, even with 32GB of RAM it takes 9 hours to call one ft_sourceanalysis. Maybe, there is a way to optimise the procedure somehow?

Second, at the end of analysis I obtain a very strange figure, on which I see activity localised outside the mri scan. Mistake on which step of analysis might cause this problem?

As a template I used http://www.fieldtriptoolbox.org/tutorial/beamformer
Please, find my script below.

Thank you in advance!

Kind Regards,
Elena


Script:

% freqanalysis
cfg = [];
cfg.toilim  = [-0.5 -0.1]; % prestimulus
Pre         = ft_redefinetrial(cfg, MyData);
cfg = [];
cfg.toilim  = [0.9 1.3]; % poststimulus
Post        = ft_redefinetrial(cfg,  MyData);
cfg = [];
dataAll     = ft_appenddata([], Pre, Post);

cfg = [];
cfg.method       = 'mtmfft';
cfg.output       = 'powandcsd'
cfg.keeptrials   = 'no';
cfg.taper        = 'dpss';
cfg.foi          = 35;
cfg.tapsmofrq    = 4;

freq_Pre         = ft_freqanalysis(cfg,  Pre);
freq_Post        = ft_freqanalysis(cfg,  Post);
freq_PrePost     = ft_freqanalysis(cfg, dataAll);

%% headmodel preparation --- with standard brain
mri     = ft_read_mri('Subject01.mri');
cfg = [];
cfg.dim = mri.dim;
mri     = ft_volumereslice(cfg,mri);

cfg = [];
cfg.output          = {'gray','white','csf','skull','scalp'}
segmentedmri        = ft_volumesegment(cfg, mri);

cfg = [];
cfg.shift           = 0.3;
cfg.method        = 'hexahedral';
cfg.tissue          = {'gray','white','csf','skull','scalp'}
cfg.numvertices     = [800, 800, 800, 400, 200];
cfg.unit                = segmentedmri.unit
bndFEM              = ft_prepare_mesh(cfg,segmentedmri);

cfg = [];
cfg.method          ='simbio';
cfg.conductivity    = [0.33 0.14 1.79 0.01 0.43];
vol_simbio_lowresol = ft_prepare_headmodel(cfg, bndFEM);

%% loading aligned electrodes
load elec_aligned    % 109 EEG electrodes

%% leadfield preparation
cfg = [];
cfg.elec                = elec_aligned;
cfg.vol                 = vol_simbio_lowresol;
cfg.channel             = 'all';
cfg.reducerank          = 3;     % 3 for eeg
cfg.grid.unit           = 'mm';
cfg.grid.resolution     = 10;
leadfield_FEM_lowresol  = ft_prepare_leadfield(cfg);

%% sourceanalysis
cfg = [];
cfg.frequency           = 35;
cfg.vol                 = vol_simbio_lowresol;
cfg.grid                = leadfield_FEM_lowresol
cfg.projectnoise        = 'yes';
cfg.method              = 'dics';
cfg.dics.projectnoise   = 'yes';
cfg.dics.lambda         = '5%';
cfg.dics.keepfilter     = 'yes';
cfg.dics.realfilter     = 'yes';
sourceAll               = ft_sourceanalysis(cfg, freq_PrePost);
cfg.grid.filter         = sourceAll.avg.filter;

sourcePre_con           = ft_sourceanalysis(cfg, freq_Pre);

sourcePost_con          = ft_sourceanalysis(cfg, freq_Post);

sourceDiff              = sourcePost_con;
sourceDiff.avg.pow      = (sourcePost_con.avg.pow - sourcePre_con.avg.pow) ./ sourcePre_con.avg.pow;

%% sourceplot
cfg = [];
cfg.downsample = 2;
cfg.parameter  = 'pow';
sourceDiffInt  = ft_sourceinterpolate(cfg, sourceDiff, mri);

cfg = [];
sourceDiffIntNorm = ft_volumenormalise(cfg, sourceDiffInt);

cfg = [];
cfg.method        = 'glassbrain';
cfg.funparameter  = 'pow';
cfg.maskparameter = cfg.funparameter;
ft_sourceplot(cfg, sourceDiffIntNorm);


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