-
Notifications
You must be signed in to change notification settings - Fork 0
/
Timelimit11_power.m
234 lines (173 loc) · 7.63 KB
/
Timelimit11_power.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
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
%% Compute power spectrum within subject
%=========================================================================%
% AUTHOR: Bianca Trovo ([email protected])
% DATE: created on April 2019
% EXPERIMENT: Timelimit_2018
%{
SCOPE: compute the
OUTPUT: freq{condi} in
FIXME:
%}
%=========================================================================%
%% START of the script
%% Housekeeping
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% clear workspace (if needed)
if input('clear all? (1/0) ... ')
clearvars; close all;
end
% set paths (if needed)
BT_setpath
% choose subj & go to the right folder
BT_getsubj
clear LevelAnalysis name numlines prompt subj_folders
%% Load preprocessed files or
for subi=1:nSubjs; %nSubjs
cd([data_Path, sprintf('/subj%02d', subi)])
if subi== 1 || subi== 18 || subi== 19 || subi== 20 || subi== 21 || subi== 22
load(sprintf('TimeLimit_v2_Resp_subj%02d_EEG_clean_concat_rej_interp',subi))
else
load(sprintf('TimeLimit_2_subj%02d_EEG_clean_concat_rej_interp',subi))
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Setting up indexes for getting only the good trials
[idx_goodxcond,idx_goodtrls, idx_allbadtrls]= BTmy_cleandatamore(TRIALS);
good_trls = setdiff([1:length(DATA_REJ_INTERP.trial)],idx_allbadtrls);
if isequal(idx_goodtrls',good_trls)==1; disp('YES'); else disp('NO'); end;
% redundant but we redo it just in case
cond= [TRIALS.cond]; %we put all the conditions in a row
cond(cond==32) = Inf;
un_conds = unique(cond);
newcond= cond(good_trls);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Re-preprocessing data for further analyses
cfg=[];
cfg.trials = good_trls;
cfg.latency = [-1 -.2];
DATA_CLEAN= ft_selectdata(cfg,DATA_REJ_INTERP);
% DATA_bl = ft_preprocessing(cfg,DATA_REJ_INTERP); % In case you apply
% baseline
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
freq=[];
for condi = 1:length(un_conds)
cfg=[];
cfg.trials= find(newcond == un_conds(condi));
cfg.method= 'mtmfft';
cfg.taper= 'hanning';
cfg.foilim= [1 40];
cfg.pad = 2;
% cfg.width= 7;
% cfg.gwidth= 3;
freq{condi} = ft_freqanalysis(cfg,DATA_CLEAN);
% Save averages power spectra
% Create the folder if it doesn't exist already.
disp('Save SPECTRAL AVERAGES results? RISK OF OVERWRITING... ')
powerspectra_folder= [results_Path, '/Powerspect']; % it can be also current_subj_folder
if ~exist(fullfile(powerspectra_folder)); mkdir(fullfile(powerspectra_folder)); end;
cd(powerspectra_folder);
filename= [sprintf('subj%02d_RSp_freq', subi)];
save(filename,'freq','-v7.3');
end
end
cd([results_Path, '/Powerspect']);
disp('END of (TIME)FREQ ANALYSIS by condition per subject');
%% Grandaverage
nSubjs= 22;
%~~~ Other
OKsubjs= [3 6 7 8 10 13 15 17 18 19 20 21];
cd(pwd);
for subi=1:nSubjs;
fname_freq= sprintf('subj%02d_RSp_freq',subi);
pickupFreq(subi) = load(fname_freq);
end
%
freqmatrix=[];
for subi= 1:nSubjs; %nGoodSubjects
for k= 1:5
freqmatrix{subi,k}= pickupFreq(subi).freq{k}; %pickupSub(i).avg_EEG{k}
end
end
save freqmatrix freqmatrix
Grand_Freq=[];
for k= 1:5
% cfg=[];
%
% cfg.channel = {'EEG020','EEG021','EEG029','EEG030','EEG031','EEG039','EEG040'};
Grand_Freq{k}= ft_freqgrandaverage([],freqmatrix{:,k});
% Grand_Freq_ROI{k}= mean(Grand_Freq{k}.powspctrm,1);
end
save Grand_Freq Grand_Freq
cfg=[];
cfg.layout = 'eeg_64_NM20884N.lay';
cfg.linewidth = 2;
cfg.showlabels= 'yes';
figure
ft_multiplotER(cfg,Grand_Freq{1},Grand_Freq{2},Grand_Freq{3},Grand_Freq{4},Grand_Freq{5});
%%
powermatrix_premov= struct('alpha', [], 'beta', []);
mean_premov_freq= struct('alpha', [], 'beta', []);
for i= 1: nSubjs; %nGoodSubjects or nSubjects
for k= 1:5
cfg = [];
cfg.channel = {'EEG038','EEG039','EEG040','EEG041','EEG045','EEG046','EEG047','EEG048','EEG049','EEG050','EEG051','EEG053','EEG054','EEG055','EEG056','EEG057','EEG058','EEG059','EEG060'};
cfg.frequency= [7.5 12.5];
% powermatrix_premov.alpha{i,k} = ft_selectdata(cfg, freqmatrix{i,k});
% powermatrix_premov.alpha{i,k}= mean(powermatrix_premov.alpha{i,k}.powspctrm,1);
mean_premov_freq.alpha(i, k) = mean(powermatrix_premov.alpha{i,k});
% (i, k) = mean(datamatrix_premov.alpha{i,k}.powspctrm);
% mean_premov_freq.alpha
% datamatrix_premov.ch30{i,k} = ft_selectdata(cfg, avgmatrix{i,k});
% mean_premov_amp.ch30(i, k) = mean(datamatrix_premov.ch30{i,k}.avg);
end
end
%%
% Alpha= 0.05, tail 'left'
TAIL= 'right'; %'both','left','right'.
% Condition n.1 (2sec) vs all the others
[p1,h1,stats] =signrank(mean_premov_freq.alpha(:, 1),mean_premov_freq.alpha(:, 5),'tail',TAIL) % vs Inf
[p2,h2,stats] =signrank(mean_premov_freq.alpha(:, 1),mean_premov_freq.alpha(:, 4),'tail',TAIL) % vs 16sec
[p3,h3,stats] =signrank(mean_premov_freq.alpha(:, 1),mean_premov_freq.alpha(:, 3),'tail',TAIL) % vs 8sec
[p4,h4,stats] =signrank(mean_premov_freq.alpha(:, 1),mean_premov_freq.alpha(:, 2),'tail',TAIL) % vs 4sec
% Condition n.2 (4sec) vs all the others
[p5,h5,stats] =signrank(mean_premov_freq.alpha(:, 2),mean_premov_freq.alpha(:, 5),'tail',TAIL) % vs Inf
[p6,h6,stats] =signrank(mean_premov_freq.alpha(:, 2),mean_premov_freq.alpha(:, 4),'tail',TAIL) % vs 16sec
[p7,h7,stats] =signrank(mean_premov_freq.alpha(:, 2),mean_premov_freq.alpha(:, 3),'tail',TAIL) % vs 8sec
% Condition n.3 (8sec) vs all the others
[p8,h8,stats8] =signrank(mean_premov_freq.alpha(:, 3),mean_premov_freq.alpha(:, 5),'tail',TAIL) % vs Inf
[p9,h9,stats] =signrank(mean_premov_freq.alpha(:, 3),mean_premov_freq.alpha(:, 4),'tail',TAIL) % vs 16sec
% Condition n.4 (16sec) vs 5
[p10,h10,stats] =signrank(mean_premov_freq.alpha(:, 4),mean_premov_freq.alpha(:, 5),'tail',TAIL) % vs Inf
% FDR
pvals_SF_05= [p1,p2,p3,p4,p5,p6,p7,p8,p9,p10];
[h, crit_p, adj_ci_cvrg, adj_p]=fdr_bh(pvals_SF_05,0.05,'pdep','yes');
%%
GROUP= {'2 sec','4 sec', '8 sec','16 sec','Inf'};
% Alpha range
[P,ANOVATAB,STATS] = anova1(mean_premov_freq.alpha,GROUP);
[c,m,h,nms] = multcompare(STATS);
[P,ANOVATAB,STATS] = kruskalwallis(mean_premov_freq.alpha,GROUP);
[c,m,h,nms] = multcompare(STATS);
%% cluster stats
% use freqmatrix
chans = {'EEG038','EEG039','EEG040','EEG041','EEG045','EEG046','EEG047','EEG048','EEG049','EEG050','EEG051','EEG053','EEG054','EEG055','EEG056','EEG057','EEG058','EEG059','EEG060'};
freqs = [7 14];
nsubs = 22;
design = [ones(1,nsubs) ones(1,nsubs)*2; ...
1:nsubs 1:nsubs];
mat = freqmatrix';
cfg = [];
cfg.method = 'montecarlo';
cfg.channel = chans;
cfg.avgoverchan = 'yes';
cfg.frequency = freqs;
cfg.avgoverfreq = 'yes';
cfg.correctm = 'cluster';
cfg.clusteralpha = 0.05;
cfg.correcttail = 'alpha';
cfg.statistic = 'ft_statfun_depsamplesT';
cfg.design = design;
cfg.ivar = 1;
cfg.uvar = 2;
cfg.numrandomization = 10000;
stat = ft_freqstatistics(cfg, mat{1,:}, mat{4,:})