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dics_beamformer.m
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dics_beamformer.m
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function dics_beamformer(sj, sessions, vs, ls)
% Beamforms predefined frequency bands
if ~isdeployed,
addpath(genpath('~/code/MEG'));
addpath(genpath('~/code/Tools'));
addpath('~/Documents/fieldtrip');
ft_defaults; % ft_defaults should work in deployed app?
end
% ==================================================================
% LOAD IN SUBJECT SPECIFICS AND READ DATA
% ==================================================================
close all;
if ischar(sj), sj = str2double(sj); end
subjectdata = subjectspecifics(sj);
if ~exist('sessions', 'var'), sessions = [1:length(subjectdata.session)]; end
if ischar(sessions), sessions = str2double(sessions); end
freqs = dics_freqbands; % retrieve specifications
if ~exist('vs', 'var'), vs = [1:length(freqs)]; end
if ischar(vs), vs = str2double(vs); end
% lockings
if ~exist('ls', 'var'), ls = [1:4]; end
if ischar(ls), ls = str2double(ls); end
for session = sessions,
% get pre-computed headmodel
mri = load(sprintf('%s/P%02d_headmodel.mat', subjectdata.mridir, sj));
mri.headmodel = rmfield(mri.headmodel, 'cfg');
% load data
load(sprintf('%s/P%02d-S%d_cleandata.mat', ...
subjectdata.preprocdir, sj, session));
data.grad = data.grad_first;
grad = data.grad_first;
data = rmfield(data, {'grad_first', 'grad_avg', 'grad_all'});
% load leadfields
lf = load(sprintf('%s/P%02d-S%d_leadfields_firstgrad.mat', ...
subjectdata.mridir, sj, session));
fprintf('\n BEAMFORMING subject %d, session %d \n', sj, session);
for v = vs,
% ==================================================================
% CREATE A SLIDING WINDOW - loop over lockings
% ==================================================================
locking(1).name = 'ref';
locking(1).prestim = 0.5;
locking(1).poststim = 1.5;
locking(2).name = 'stim';
locking(2).prestim = 0.5;
locking(2).poststim = 1;
locking(3).name = 'resp';
locking(3).prestim = 0.5;
locking(3).poststim = 1.5;
locking(4).name = 'fb';
locking(4).prestim = 0.5;
locking(4).poststim = 1.5;
for l = ls,
% ==============================
% don't redo if this already exists
% ===============================
filename = sprintf('%s/P%02d-S%d_dics_%s_%s.mat', ...
subjectdata.sourcedir, sj, session, freqs(v).name, locking(l).name);
if exist(filename, 'file'),
try % test if this file is not corrupted!
load(filename);
fprintf('\nSkipping %s/P%02d-S%d_dics_%s_%s.mat, already exists \n', ...
subjectdata.sourcedir, sj, session, freqs(v).name, locking(l).name);
continue;
catch
fprintf('\nDeleting %s/P%02d-S%d_dics_%s_%s.mat, corrupted \n', ...
subjectdata.sourcedir, sj, session, freqs(v).name, locking(l).name);
delete(filename);
end
end
% ==================================================================
% MAKE COMMON FILTER
% ==================================================================
% only use the time before the end of the second trial -> clean
data_common = ft_selectdata({'latency', [0 1.99], 'avgovertime', 'no'}, data);
data_common = ft_timelockanalysis(struct('keeptrials', 'yes'), data_common);
data_common = rmfield(data_common, 'cfg');
cfg = [];
cfg.method = 'mtmfft';
cfg.output = 'powandcsd';
cfg.taper = 'dpss';
cfg.channel = ft_channelselection('MEG', data_common.label);
cfg.keeptrials = 'yes'; % work with all trials throughout
cfg.keeptapers = 'no';
cfg.precision = 'single'; % saves disk space
cfg.foi = freqs(v).freq;
cfg.tapsmofrq = freqs(v).tapsmofrq;
cfg.pad = 2; % to speed up computation
cfg.feedback = 'none'; % keep logfiles clean
freq_common = ft_freqanalysis(cfg, data_common);
assert(freq_common.freq == freqs(v).freq, 'intended frequency could not be recovered');
cfg = [];
cfg.method = 'dics';
cfg.sourcemodel = lf.leadfield; % should be grid defined in MNI space
cfg.headmodel = mri.headmodel; % previously computed volume conduction model
cfg.frequency = freq_common.freq;
cfg.dics.keepfilter = 'yes'; % remember the filter
cfg.dics.lambda = '10%'; % higher lambda is sparses source estimate?
cfg.dics.realfilter = 'yes';
cfg.dics.fixedori = 'yes'; % if fixedori = no, get 3 directions for each grid point
cfg.dics.projectnoise = 'yes'; % for baseline vs activity
cfg.dics.feedback = 'none';
common_filter = ft_sourceanalysis(cfg, freq_common); % use concatenated data
% PRE-ALLOCATE THE OUTPUT STRUCTURE
source = struct('freq', freq_common.freq, ...
'dim', common_filter.dim, ...
'inside', common_filter.inside, ...
'pos', common_filter.pos, ...
'method', 'rawtrial', ...
'powdimord', 'pos_rpt_time', ...
'trialinfo', data.trialinfo);
% ==============================
% get locked data
% ===============================
data_lock = dics_redefinetrials(data, l);
% make nice timebins at each 50 ms, will include 0 point
timestep = 0.05; % in seconds
toi = -locking(l).prestim : timestep : locking(l).poststim;
% make sure there are no bins outside the time in the data
toi(toi < min(data_lock.time)) = [];
toi(toi > max(data_lock.time)) = [];
source.time = toi;
% separate this out for the parfor loop
pow = nan(size(common_filter.pos, 1), length(data.trial), length(toi));
% save some memory
common_filter = common_filter.avg.filter;
clear freq_common data_common
for t = 1:length(toi),
fprintf('\n\n Beamforming P%02d-S%d, %d Hz, %s, timewindow %f (%d/%d)... \n', ...
sj, session, freqs(v).freq, locking(l).name, toi(t), t, length(toi));
% ==================================================================
% FREQANALYSIS ON A SMALL TIME WINDOW
% ==================================================================
tic;
cfg = [];
thistoi = toi(t); % each of these gets its own loop
windowlength = freqs(v).timewin; % half a second, an integer nr of cycles has to fit in
cfg.toilim = [thistoi-windowlength/2 thistoi+windowlength/2]; % redefine
data_toi = ft_redefinetrial(cfg, data_lock);
data_toi.grad = grad; % make sure this is consistent
assert(all(rem(freqs(v).freq ./ windowlength, 1) == 0), ...
'an integer number of cycles must fit into this time window');
if ~(roundn(data_toi.time(1), -2) == roundn(cfg.toilim(1), -2) ...
&& roundn(data_toi.time(end), -2) == roundn(cfg.toilim(2), -2)),
warning('could not grab the specific timewin intended: %s, %d', locking(l).name, t);
end
cfg = [];
cfg.method = 'mtmfft';
cfg.output = 'powandcsd';
cfg.taper = 'dpss';
cfg.channel = ft_channelselection('MEG', data_toi.label);
cfg.keeptrials = 'yes'; % work with all trials throughout
cfg.keeptapers = 'no';
cfg.precision = 'single'; % saves disk space
cfg.foi = freqs(v).freq;
cfg.tapsmofrq = freqs(v).tapsmofrq;
cfg.pad = 2; % to speed up computation
cfg.feedback = 'none'; % keep logfiles clean
freq_toi = ft_freqanalysis(cfg, data_toi);
assert(freq_toi.freq == freqs(v).freq, 'intended frequency could not be recovered');
% ==================================================================
% PROJECT ALL TRIALS THROUGH COMMON FILTER
% ==================================================================
cfg = [];
cfg.method = 'dics';
cfg.headmodel = mri.headmodel;
cfg.sourcemodel = lf.leadfield;
cfg.sourcemodel.filter = common_filter; % use the common filter computed in the previous step!
cfg.dics.keepfilter = 'no'; % remember the filter
cfg.dics.lambda = '10%'; % higher lambda is sparses source estimate?
cfg.dics.realfilter = 'yes';
cfg.dics.fixedori = 'yes'; % if fixedori = no, get 3 directions for each grid point
cfg.dics.projectnoise = 'yes'; % for baseline vs activity
cfg.dics.feedback = 'text'; % keep logfiles clean
cfg.frequency = freq_toi.freq;
cfg.rawtrial = 'yes'; % project each single trial through the filter
cfg.keeptrial = 'no'; % dont keep trialinfo struct, can copy from data
source_toi = ft_sourceanalysis(cfg, freq_toi);
assert(source_toi.freq == freqs(v).freq, 'intended frequency could not be recovered');
% save this output in the large 3-d matrix
pow(:, :, t) = [source_toi.trial(:).pow];
toc;
end % toi
source.pow = pow;
% SAVE
savefast(sprintf('%s/P%02d-S%d_dics_%s_%s.mat', ...
subjectdata.sourcedir, sj, session, freqs(v).name, locking(l).name), 'source');
fprintf('\nSAVED %s/P%02d-S%d_dics_%s.mat \n\n', ...
subjectdata.sourcedir, sj, session, freqs(v).name , locking(l).name);
end % lockings
end % freqs
end % sessions
end