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Copy pathPracticalMEEG_Session_2_Time_Frequency_Analysis.m
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154 lines (128 loc) · 7.81 KB
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% Practical MEEG 2022
% Wakeman & Henson Data analysis: Spectral and Time-Frequency Analsyis
% Authors: Ramon Martinez-Cancino, Brain Products, 2022
% Romain Grandchamp, LPNC, 2025
% Arnaud Delorme, SCCN, 2022-2025
% Johanna Wagner, Zander Labs, 2022
%
% Copyright (C) 2022 Ramon Martinez-Cancino
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program; if not, write to the Free Software
% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
%%
% Clearing all is recommended to avoid variable not being erased between calls
clear;
clear globals;
% Path to data below. Using relative paths so no need to update.
path2data = fullfile(pwd,'ds000117_pruned', 'derivatives', 'meg_derivatives', 'sub-01', 'ses-meg/', 'meg/'); % Path to data
filename = 'wh_S01_run_01_preprocessing_data_session_1_out.set';
filename_epoched_famous = 'wh_S01_run_01_ERP_Analysis_Session_2_famous_out.set';
filename_epoched_unfamiliar = 'wh_S01_run_01_ERP_Analysis_Session_2_unfamiliar_out.set';
filename_epoched_scrambled = 'wh_S01_run_01_ERP_Analysis_Session_2_scrambled_out.set';
% Start EEGLAB
[ALLEEG, EEG, CURRENTSET] = eeglab;
%% Loading data
EEG = pop_loadset('filename', filename,'filepath',path2data);
%% Identifying Artifacts Using ICLabel and removing them (EEG only)
if ~contains(EEG.chanlocs(1).type, 'meg')
[M,I] = max(EEG.etc.ic_classification.ICLabel.classifications,[],2); % Use max prob for classification
Brain_comps = find(I == find(strcmp(EEG.etc.ic_classification.ICLabel.classes, 'Brain')));
EEG = pop_subcomp( EEG, Brain_comps, 0, 1);
end
%% The plot below are for each conditions
%% To compute statistics, you must create a STUDY with a single subject
%%-------------------------------------------------------------------------
%% Plot spectrum using Welch’s method
% Default vaues. winsize = Sampling Rate; overlap = 0
figure('name', 'spectopo_defaults');
pop_spectopo(EEG, 1, [0 EEG.xmax*1000], 'EEG' , 'freq', [6 10 22], 'freqrange',[2 40],'electrodes','off');
% saveas(gcf,'spectopo_defaults.jpg')
% winsize = 200; overlap = 0
figure('name', 'winsize = 200; overlap = 0');
pop_spectopo(EEG, 1, [0 EEG.xmax*1000], 'EEG' , 'freq', [6 10 22], 'freqrange',[2 40],'electrodes','off', 'winsize', 200);
% winsize = 300; overlap = 0
figure('name', 'winsize = 300; overlap = 0');
pop_spectopo(EEG, 1, [0 EEG.xmax*1000], 'EEG' , 'freq', [6 10 22], 'freqrange',[2 40],'electrodes','off', 'winsize', 300);
saveas(gcf,'spectopo_winsize_300.jpg')
% winsize = 300; overlap = 5
figure('name', 'winsize = 300; overlap = 50');
pop_spectopo(EEG, 1, [0 EEG.xmax*1000], 'EEG' , 'freq', [6 10 22], 'freqrange',[2 40],'electrodes','off', 'winsize', 300, 'overlap', 50);
%% Plot spectrum for channel eeg065
figure('name', 'Spectrum Channel EEG065');
spectopo( EEG.data(55,:), EEG.pnts, EEG.srate,'winsize', 300, 'overlap', 50);
title('Spectrum Channel EEG065')
%% Time-frequency
%% ERS Vs ERP
% Load Epoched data here
EEG_famous = pop_loadset('filename', filename_epoched_famous,'filepath',path2data);
EEG_unfamiliar = pop_loadset('filename', filename_epoched_unfamiliar,'filepath',path2data);
EEG_scrambled = pop_loadset('filename', filename_epoched_scrambled,'filepath',path2data);
figure;
pop_newtimef( EEG_famous, 1, 52, [-1000 1990], [3 0.8] , 'topovec', 52,...
'elocs', EEG_famous.chanlocs,...
'chaninfo', EEG_famous.chaninfo,...
'caption', 'ERS: Famous eeg065',...
'baseline', [NaN],...
'plotitc' , 'off',...
'plotphase', 'off',...
'padratio', 1,...
'winsize', 100);
figure;
pop_newtimef( EEG_famous, 1, 52, [-1000 1990], [3 0.8] , 'topovec', 52,...
'elocs', EEG_famous.chanlocs,...
'chaninfo', EEG_famous.chaninfo,...
'caption', 'ERSP: Famous eeg065',...
'baseline', 1,...
'plotitc' , 'on',...
'plotphase', 'off',...
'padratio', 1,...
'winsize', 100);
%% ERSP all conditions with same scale
% ERSP for famous faces
figure;
pop_newtimef( EEG_famous, 1, 52, [-1000 1990], [3 0.8] , 'topovec', 52,...
'elocs', EEG_famous.chanlocs,...
'chaninfo', EEG_famous.chaninfo,...
'caption', 'ERS: Famous eeg065',...
'baseline', 1,...
'plotitc' , 'off',...
'plotphase', 'off',...
'padratio', 1,...
'winsize', 100,...
'erspmax', 3);
% ERSP for undfamiliar faces
figure;
pop_newtimef( EEG_unfamiliar, 1, 52, [-1000 1990], [3 0.8] , 'topovec', 52,...
'elocs', EEG_unfamiliar.chanlocs,...
'chaninfo', EEG_unfamiliar.chaninfo,...
'caption', 'ERSP: Unfamiliar eeg065',...
'baseline', 1,...
'plotitc' , 'off',...
'plotphase', 'off',...
'padratio', 1,...
'winsize', 100,...
'erspmax', 3);
% ERSP for scrambled faces
figure;
pop_newtimef( EEG_scrambled, 1, 52, [-1000 1990], [3 0.8] , 'topovec', 52,...
'elocs', EEG_scrambled.chanlocs,...
'chaninfo', EEG_scrambled.chaninfo,...
'caption', 'ERSP: Scrambled eeg065',...
'baseline', 1,...
'plotitc' , 'off',...
'plotphase', 'off',...
'padratio', 1,...
'winsize', 100,...
'erspmax', 3);
% For significance testing, add option: 'alpha',0.01