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ME125_Master_SensorLevel_weiforyanan_paul_noAudio.m
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ME125_Master_SensorLevel_weiforyanan_paul_noAudio.m
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%% This script is created for analyzing %%%%%%
%%%%%% Roving MMN data acquaried at KIT-MQ %%%%%%
%%%%%% funded by ARC-DP [DP170103148] %%%%%%
%%%%%% prepared by Wei He using fieldtrip %%%%%%
%%%%%% and functions written by Paul Sowman %%%%%%
%%%%%% and Robert Seymour , August 2018 %%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%!!! eash subject folder should have 5 files: .con .hsp .elp .mrk .txt(matlab trigger output) !!!
%%!!! manually reformat the .mrk name to '*_ini.mrk' !!!
%%!!! make sure Fieldtrip toolbox is in the path !!!
%%!!! make sure "scripts" folder is in the path !!!
%%!!! make sure "database_for_MEMES" folder is in the path !!!
%%
clc
close all
% % add fieldtrip in path
% restoredefaultpath
% addpath E:\fieldtrip-20180825; % change path if necessary
% ft_defaults
% fprintf('\nAdd Fieldtrip into path.\n')
%
% % add scripts full path
% addpath(genpath('E:\ARC-DP\RawData\MMN\scripts'));% change path if necessary
% fprintf('\nAdd scripts into path.\n')
%
% %% BATCH STARTS
%
% subjects = input('please type the number of subjects\n','s');
% ee = str2num(subjects);%offset number of directories
% y=subdir(cd);
y = dir('*rethm.con');
for i = 1:length(y)
x = [];
close all;
if strcmp(y.name(end-8:end-4), 'rethm')
%cd(y{i});
%fprintf('\n%s is a ReTHM directory! Perform analysis\n', y{i})
%% PART 1: MEMES: Co-registration between MEG, Head Surface, and Brain Sruface in the MRI-library
fname = dir('*.elp');
fname = fname.name(1:strfind(fname.name,'.')-1);
pathname = [pwd,'/'];
elpfile = [fname,'.elp'];
hspfile = [fname,'.hsp'];
confile = [fname,'_B1_denoise_rethm.con']; % !!! if NOT ReTHM data change the con file name here !!!
mrkfile = [fname,'_INI.mrk'];
bad_coil = [];
%path_to_MRI_library = 'C:\Users\mq42604613\Desktop\ARC_DP_pipeline_test\database_for_MEMES\'; % !!! manually check !!!
% child_MEMES(pathname,elpfile,hspfile,confile,mrkfile,path_to_MRI_library,bad_coil,'yes'); % found here that
%% PART 2: Functional processing - Sensor Level ERFs - pre-&post-Deviants
hdr = ft_read_header(confile,'dataformat','yokogawa_con'); %hdr to get Fs etc.
filename = confile;
%%2.1 Raw data & Bandpass Filter
cfg = [];
cfg.trialfun = 'ft_trialfun_general'; %ft_definetrial: defines the segments of data that will be read in by FT_PREPROCESSING
cfg.channel = hdr.label(1:125);
cfg.lpfilter = 'yes';
cfg.lpfreq = 40;
cfg.hpfilter = 'yes';
cfg.hpfreq = 1; %FIXME should try something lower to appease reviewers
cfg.hpfiltord = 5;
cfg.dftfreq = 50; % removal line noise
cfg.headerfile = filename;
cfg.datafile = filename;
data = ft_preprocessing(cfg);
%%2.2 Create layout file for later + save
cfg = [];
cfg.grad = data.grad; % struct containing gradiometer definition
lay = ft_prepare_layout(cfg, data); % creates a 2-D layout of the channel locations
%ft_layoutplot(cfg);
save (['lay_', num2str(fname(1:4)),'.mat'],'lay')
%%2.3 Trigger-based trial selection
cfg = [];
cfg.dataset = filename;
cfg.path = pathname;
cfg.trialdef.prestim = 0.1; % pre-stimulus interval
cfg.trialdef.poststim = 0.5; % post-stimulus interval
cfg.trialdef.eventtype = 'trigger';
cfg.trialdef.eventvalue = 1:7;%Trigger numbers
cfg.Fs = hdr.Fs;
cfg.First_Channel = 146; % First trigger channel
cfg.Last_Channel = 158; % Last trigger channel
cfg.Audio_Channel = 0; % 0 for none
cfg.fixed_offset = 42;
cfg.trialfun = 'FindTriggers_AudioCh';
cfg = ft_definetrial(cfg);
alldata = ft_redefinetrial(cfg,data);
event = cfg.event;
trl = cfg.trl;
save (['event_', num2str(fname(1:4)),'.mat'], 'event')
save (['trl_', num2str(fname(1:4)),'.mat'], 'trl')
% Detrend and demean each trial
cfg = [];
cfg.demean = 'yes';
cfg.detrend = 'yes';
% cfg.baselinewindow = [-0.5 0];
alldata = ft_preprocessing(cfg,alldata);
%%2.4 Visual artefact rejection
[z,bad_trials,data_clean] = artifacts_max_z(alldata,10);
good_trials_idx = setdiff(1:length(event),bad_trials);
cfg = [];
cfg.trials = good_trials_idx;
data_clean = ft_selectdata(cfg,data_clean); %remove bad trials
event_clean=event(good_trials_idx); %remove bad trials events
trl_clean=trl(good_trials_idx,:);
%good_trials_idx = find(~isnan(cell2mat(cellfun(@(isgood)isgood(1),data_clean.trial,'uni',0)))); %just need to evaluate the first element as all samples in bad trial are NaN
% for j = 1:length(bad_trials)
% event(bad_trials(j)).bad = 1;
% end
event=event_clean;
for j=1:length(event)
if event(j).value == 1
event(j).type = 'deviant';
else
event(j).type = 'standard';
end
end
for j=1:length(event)-1
if strcmp(event(j).type,'standard') & strcmp(event(j+1).type,'deviant')
event(j).type = 'predeviant';
event(j+1).type = event(j+1).type;
else
end
end
%%2.5 Downsample
downsample_factor = 5;
cfg = [];
cfg.resamplefs = data_clean.fsample/downsample_factor; % Here we are downsampling from 1000Hz --> 200Hz: Garrido et al., 2008, Neuroimage
cfg.detrend = 'yes'; % Helps with low-frequency drift
data_clean = ft_resampledata(cfg, data_clean);
save (['data_clean_', num2str(fname(1:4)),'.mat'], 'data_clean')
%%2.6 Epoching
trigger_types = {event.type};
trigger_values = cell2mat({event.value});
% pre-deviants/deviants
predeviant_trials = find(ismember(trigger_types,'predeviant')); %trigger number 1 is first deviant
deviant_trials = find(ismember(trigger_types,'deviant')); %trigger number 1 is first deviant
% repeat_1 = setdiff(find(ismember(trigger_values,2)),bad_trials); %trigger number
% repeat_2 = setdiff(find(ismember(trigger_values,3)),bad_trials); %trigger number
% repeat_3 = setdiff(find(ismember(trigger_values,4)),bad_trials); %trigger number
% repeat_4 = setdiff(find(ismember(trigger_values,5)),bad_trials); %trigger number
% repeat_5 = setdiff(find(ismember(trigger_values,6)),bad_trials); %trigger number
% repeat_6 = setdiff(find(ismember(trigger_values,7)),bad_trials); %trigger number
%
% %equalise repeat numbers
% repeat_1 = repeat_1(randperm(length(repeat_1)));
% repeat_1 = repeat_1(1:length(repeat_6));
%
% repeat_2 = repeat_2(randperm(length(repeat_2)));
% repeat_2 = repeat_2(1:length(repeat_6));
%
% repeat_3 = repeat_3(randperm(length(repeat_3)));
% repeat_3 = repeat_3(1:length(repeat_6));
%
% repeat_4 = repeat_4(randperm(length(repeat_4)));
% repeat_4 = repeat_4(1:length(repeat_6));
%
% repeat_5 = repeat_5(randperm(length(repeat_5)));
% repeat_5 = repeat_5(1:length(repeat_6));
cfg = [];
cfg.trials = setdiff(deviant_trials,bad_trials);
deviant = ft_redefinetrial(cfg,data_clean);
save (['deviant_', num2str(fname(1:4)),'.mat'], 'deviant')
cfg = [];
cfg.trials = setdiff(predeviant_trials,bad_trials);
predeviant = ft_redefinetrial(cfg,data_clean);
save (['predeviant_', num2str(fname(1:4)),'.mat'], 'predeviant')
%%2.7 Averaging
cfg = [];
deviant_ave = ft_timelockanalysis(cfg,deviant);
predeviant_ave = ft_timelockanalysis(cfg,predeviant);
save (['deviant_ave_', num2str(fname(1:4)),'.mat'], 'deviant_ave')
save (['predeviant_ave_', num2str(fname(1:4)),'.mat'], 'predeviant_ave')
%%2.8 Planar gradient transform
% Calculate the planar gradient of the averaged data:
cfg = [];
cfg.method = 'triangulation';
cfg.neighbours = ft_prepare_neighbours(cfg, data_clean.grad); %To get neigbours of the pak for plotting
cfg.planarmethod = 'sincos';
deviant_ave_planar = ft_megplanar(cfg, deviant_ave);
predeviant_ave_planar = ft_megplanar(cfg, predeviant_ave);
% Combine the horizontal and vertical components of the planar gradient
cfg = [];
deviant_ave_planar_comb = ft_combineplanar(cfg,deviant_ave_planar);
cfg = [];
predeviant_ave_planar_comb = ft_combineplanar(cfg,predeviant_ave_planar);
save (['deviant_ave_planar_comb_', num2str(fname(1:4)),'.mat'], 'deviant_ave_planar_comb')
save (['predeviant_ave_planar_comb_', num2str(fname(1:4)),'.mat'], 'predeviant_ave_planar_comb')
% GFP for planar gradient
cfg = [];
cfg.method = 'power';
deviant_planar_GFP = ft_globalmeanfield(cfg, deviant_ave_planar_comb);
predeviant_planar_GFP = ft_globalmeanfield(cfg, predeviant_ave_planar_comb);
cfg = [];
cfg.operation = 'subtract';
cfg.parameter = 'avg';
difference_planar_GFP = ft_math(cfg, deviant_planar_GFP , predeviant_planar_GFP );
difference_planar_GFP.avg = abs(difference_planar_GFP.avg);
%
% cfg = [];
% cfg.xlim = [-0.1 0.4];
% cfg.title = 'Global Field Power';
% cfg.graphcolor = 'brk';
% figure;
% % ft_singleplotER(cfg,standard_1_GFP,standard_2_GFP,standard_3_GFP,standard_4_GFP,standard_5_GFP,deviant_GFP)
% ft_singleplotER(cfg,predeviant_planar_GFP ,deviant_planar_GFP , difference_planar_GFP)
% legend('pre-deviant','deviant', 'difference')
% title(['GPF of the difference waveform in subject ', num2str(fname(1:4)),' (planar gradients)']); drawnow;
% print(['GFP_StdvsDev_', num2str(fname(1:4))],'-dpng');
%
% % Toppo plots on the deviant waveform (planar gradients)
% % To plot a squence of topo plots equally spaced between 0.1 and 0.4 second
% timestep = 0.05; % in 50 ms steps
% sampling_rate = data_clean.fsample;
% sample_count = length(deviant_ave_planar_comb.time);
%
% j = [0:timestep:0.4];
% % m = [1:timestep*sampling_rate:sample_count]; % temporal endpoints in MEEG samples
% ft_hastoolbox('brewermap', 1); % ensure this toolbox is on the path
%
% figure;
% for k = 1: length(j)-1
% cfg = [];
% cfg.comment = 'no';
% % cfg.marker = 'off';
% cfg.layout = lay;
% % cfg.colorbar = 'southoutside';
% cfg.style = 'straight';
%
% subplot(2,round(length(j)-1)/2,k)
% cfg.xlim = [j(k) j(k+1)];
% cfg.zlim = 'maxabs';
% ft_topoplotER(cfg, deviant_ave_planar_comb)
% colormap(flipud(brewermap(64,'RdBu'))) % change the colormap
% % colorbar
% title(['Time [', num2str(j(k)),' ', num2str(j(k+1)),']']); drawnow;
% hold on
% end
% h = suptitle (['Topographic plots of the deviant response in subject ', num2str(fname(1:4)),' (planar gradients)']);
% set (h,'FontSize',12,'FontWeight','bold')
% set(gcf, 'Position', [200, 200, 1000, 1000])
% print(['Topo_deviantavg_planar_', num2str(fname(1:4))],'-dpng');
%
% figure;
% for k = 1: length(j)-1
% cfg = [];
% cfg.comment = 'no';
% % cfg.marker = 'off';
% cfg.layout = lay;
% % cfg.colorbar = 'southoutside';
% cfg.style = 'straight';
%
% subplot(2,round(length(j)-1)/2,k)
% cfg.xlim = [j(k) j(k+1)];
% cfg.zlim = 'maxabs';
% ft_topoplotER(cfg, predeviant_ave_planar_comb)
% colormap(flipud(brewermap(64,'RdBu'))) % change the colormap
% % colorbar
% title(['Time [', num2str(j(k)),' ', num2str(j(k+1)),']']); drawnow;
% hold on
% end
% h = suptitle (['Topographic plots of the standard response in subject ', num2str(fname(1:4)),' (planar gradients)']);
% set (h,'FontSize',12,'FontWeight','bold')
% set(gcf, 'Position', [200, 200, 1000, 1000])
% print(['Topo_standardavg_planar_', num2str(fname(1:4))],'-dpng');
else
% fprintf('\n%s is NOT a ReTHM directory!\n', y{i})
end
end