-
Notifications
You must be signed in to change notification settings - Fork 0
/
batchDemoForICA.m
175 lines (140 loc) · 7.21 KB
/
batchDemoForICA.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
% 08/28/2018 Makoto. Created.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% 1. Perform ARfitStudio in the batch mode. Capture the result plot. %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
cd /data/projects/makoto/Hemifield/p01_import_chan_event
allSet = dir('*.set');
for n = 1:length(allSet)
dataName = (allSet(n).name);
EEG = pop_loadset('filename', dataName,'filepath','/data/projects/makoto/Hemifield/p01_import_chan_event');
% Epoch data.
epochEEG = pop_epoch(EEG, {'Picture'}, [-0.2 0.1], 'newname', 'tmp epoch', 'epochinfo', 'yes');
% Interpolate using ARfit.
cleanEEG = arfit2interpolate(epochEEG.data, [96 106], 5);
epochEEG.data = cleanEEG;
[denoisedEEG, figureHandle] = putBackEpoch2Continuous(EEG, epochEEG, 'Picture', 1);
EEG = denoisedEEG;
% Save the figure
drawnow
print(figureHandle, '-djpeg99', '-r300', ['/data/projects/makoto/Hemifield/p03_cleanSpike/' dataName(1:end-4)])
close(figureHandle)
% Save the data.
pop_saveset(EEG, 'filename', dataName, 'filepath', '/data/projects/makoto/Hemifield/p03_cleanSpike');
end
%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% 2. Preprocess up to Dipole fitting. Note that interpolated data points are excluded from AMICA. %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
cd /data/projects/makoto/Hemifield/p03_cleanSpike
allSet = dir('*.set');
for n = 1:length(allSet)
loadName = (allSet(n).name);
dataName = loadName(1:end-4);
EEG = pop_loadset('filename', loadName,'filepath','/data/projects/makoto/Hemifield/p03_cleanSpike/');
% High-pass filter the data.
EEG = pop_firws(EEG, 'fcutoff', 0.5, 'ftype', 'highpass', 'wtype', 'hamming', 'forder', 3300, 'minphase', 0);
% Apply cleanline.
EEG = pop_cleanline(EEG, 'bandwidth', 2, 'chanlist', 1:EEG.nbchan,...
'computepower', 1, 'linefreqs', 60, 'normSpectrum', 0, 'p', 0.05,...
'pad',2,'plotfigures',0,'scanforlines',1,'sigtype','Channels','tau', 100,...
'verb',1,'winsize',4,'winstep',4);
% Run ASR.
currentCleanRawdata = which('clean_rawdata');
mobilabTest = strfind(currentCleanRawdata, 'mobilab');
if any(mobilabTest)
error('clean_rawdata under mobilab is being used.')
end
originalEEG = EEG;
EEG = clean_rawdata(EEG, -1, -1, 0.85, -1, 20, 0.25); % the last one originally 0.25; ASR originally 20
% Interpolate all the removed channels.
EEG = pop_interp(EEG, originalEEG.chanlocs, 'spherical');
% Re-reference the data to average.
EEG.nbchan = EEG.nbchan+1;
EEG.data(end+1,:) = zeros(1, EEG.pnts);
EEG.chanlocs(1,EEG.nbchan).labels = 'initialReference';
EEG = pop_reref(EEG, []);
EEG = pop_select( EEG,'nochannel',{'initialReference'});
% For AMICA, remove the interpolated datapoints.
allEventTypes = {EEG.event.type}';
eventOnsetIdx = find(strcmp(allEventTypes, 'Picture'));
eventOnsetFrame = [EEG.event(eventOnsetIdx).latency]';
numFramesRemovedBeforeOnset = 5;
numFramesRemovedAfterOnset = 10;
epochEdges = [eventOnsetFrame-numFramesRemovedBeforeOnset eventOnsetFrame+numFramesRemovedAfterOnset];
EEG_forAMICA = pop_select(EEG, 'nopoint', epochEdges);
% Run AMICA.
if isfield(EEG.etc, 'clean_channel_mask')
dataRank = min([rank(double(EEG_forAMICA.data')) sum(EEG_forAMICA.etc.clean_channel_mask)]);
else
dataRank = rank(double(EEG_forAMICA.data'));
end
runamica15(EEG_forAMICA.data, 'num_chans', EEG_forAMICA.nbchan,...
'outdir', ['/data/projects/makoto/Hemifield/p04_preprocessUpToDipfit/' dataName],...
'pcakeep', dataRank, 'num_models', 1, 'nodeproc', [4 16],...
'do_reject', 1, 'numrej', 15, 'rejsig', 3, 'rejint', 1);
% Load amica results.
EEG = pop_loadmodout(EEG, ['/data/projects/makoto/Hemifield/p04_preprocessUpToDipfit/' dataName]);
% Perfrom Dipfit.
transformParameters = [0.6681 -17.8438 -1.0369 3.9898e-07 1.4929e-07 -1.5708 1 1 1];
EEG = pop_dipfit_settings(EEG,...
'hdmfile', '/data/common/matlab/eeglab/plugins/dipfit2.4/standard_BEM/standard_vol.mat',...
'mrifile', '/data/common/matlab/eeglab/plugins/dipfit2.4/standard_BEM/standard_mri.mat',...
'chanfile', '/data/common/matlab/eeglab/plugins/dipfit2.4/standard_BEM/elec/standard_1005.elc',...
'coord_transform', transformParameters, 'chansel', 1:EEG.nbchan, 'coordformat','MNI');
EEG = pop_multifit(EEG, 1:size(EEG.icaact,1) ,'threshold',100);
EEG = fitTwoDipoles(EEG, 'LRR', 35);
% Change EEG.setname.
EEG.setname = dataName;
EEG.subject = dataName;
% Save the data.
pop_saveset(EEG, 'filename', dataName, 'filepath', '/data/projects/makoto/Hemifield/p04_preprocessUpToDipfit');
end
%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% 3. Perform ARfitStudio on EEG.icaact. %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
cd /data/projects/makoto/Hemifield/p04_preprocessUpToDipfit
allSet = dir('*.set');
for n = 1:length(allSet)
loadName = (allSet(n).name);
dataName = loadName(1:end-4);
EEG = pop_loadset('filename', loadName, 'filepath','/data/projects/makoto/Hemifield/p04_preprocessUpToDipfit/');
% Obtain all the EEG.events.
allEventTypes1 = {EEG.event.type}';
% Obtain boundaries.
boundaryIdx = find(strcmp(allEventTypes1, 'boundary'));
boundaryFrameIdx = [EEG.event(boundaryIdx).latency];
% Remove boundaries temtatively.
EEG.event(boundaryIdx) = [];
% Backup and delete the current EEG.urevent.
originalUrevent = EEG.urevent;
EEG = eeg_checkset(EEG, 'makeur');
% Obtain all the EEG.events after removing boundaries.
allEventTypes2 = {EEG.event.type}';
% Epoch to the event.
eventOnsetIdx = find(strcmp(allEventTypes2, 'Picture'));
eventOnsetFrame = [EEG.event(eventOnsetIdx).latency]';
epochedEEG = pop_epoch(EEG, {'Picture'}, [-0.2 0.1], 'newname', 'ICAed epoched', 'epochinfo', 'yes');
latencyZeroIdx = find(epochedEEG.times == 0);
epochEdges = [eventOnsetFrame-(latencyZeroIdx-1) eventOnsetFrame+(epochedEEG.pnts-latencyZeroIdx)];
% Interpolate data using ARfit.
cleanIcaact = arfit2interpolate(epochedEEG.icaact, [96 106], 5);
% Obtain its scalp projection.
cleanIcaact2D = cleanIcaact(:,:);
backproj2D = epochedEEG.icawinv*cleanIcaact2D;
% Update the current EEG.data and EEG.icaact with the backprojected data.
replacingFrameIdx = mcolon(epochEdges(:,1), epochEdges(:,2));
EEG2 = EEG;
EEG2.data( :,replacingFrameIdx) = backproj2D;
EEG2.icaact(:,replacingFrameIdx) = cleanIcaact2D;
% Put back boundaries.
for boundaryIdx = 1:length(boundaryFrameIdx)
EEG2.event(end+1).type = 'boundary';
EEG2.event(end).latency = boundaryFrameIdx(boundaryIdx);
end
latencyList = [EEG2.event.latency];
[~,sortingOrder] = sort(latencyList, 'ascend');
EEG2.event = EEG2.event(sortingOrder);
% Save the data.
pop_saveset(EEG2, 'filename', dataName, 'filepath', '/data/projects/makoto/Hemifield/p05_cleanSpikeOnICA');
end