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StefanHaufe
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‎demos/demo_analysis_ga_ERPs.m

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files = {'VPibv_10_11_02\calibration_CenterSpellerMVEP_VPibv',
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'VPibq_10_09_24\calibration_CenterSpellerMVEP_VPibq',
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'VPiac_10_10_13\calibration_CenterSpellerMVEP_VPiac',
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'VPibs_10_10_20\calibration_CenterSpellerMVEP_VPibs',
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'VPibt_10_10_21\calibration_CenterSpellerMVEP_VPibt'};
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nsub = length(files);
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for isub = 1:nsub
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file = files{isub};
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%% Load data
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hdr= file_readBVheader(file);
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Wps= [42 49]/hdr.fs*2;
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[n, Ws]= cheb2ord(Wps(1), Wps(2), 3, 40);
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[filt.b, filt.a]= cheby2(n, 50, Ws);
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[cnt, mrk_orig]= file_readBV(file, 'Fs',100, 'Filt',filt);
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%% Marker struct
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stimDef= {[31:46], [11:26];
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'target','nontarget'};
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mrk= mrk_defineClasses(mrk_orig, stimDef);
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%% Re-referencing to linked-mastoids
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A= eye(length(cnt.clab));
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iA1= util_chanind(cnt.clab,'A1');
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if isempty(iA1)
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iA1= util_chanind(cnt.clab,'A2');
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end
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A(iA1,:)= -0.5;
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A(:,iA1)= [];
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cnt= proc_linearDerivation(cnt, A);
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%% Electrode Montage
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grd= sprintf(['scale,_,F5,F3,Fz,F4,F6,_,legend\n' ...
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'FT7,FC5,FC3,FC1,FCz,FC2,FC4,FC6,FT8\n' ...
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'T7,C5,C3,C1,Cz,C2,C4,C6,T8\n' ...
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'P7,P5,P3,P1,Pz,P2,P4,P6,P8\n' ...
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'PO9,PO7,PO3,O1,Oz,O2,PO4,PO8,PO10']);
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mnt= mnt_setElectrodePositions(cnt.clab);
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mnt= mnt_setGrid(mnt, grd);
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% Define some settings
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disp_ival= [-200 1000];
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ref_ival= [-200 0];
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crit_maxmin= 70;
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crit_ival= [100 800];
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crit_clab= {'F9,z,10','AF3,4'};
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clab= {'Cz','PO7'};
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colOrder= [1 0 1; 0.4 0.4 0.4];
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% Apply highpass filter to reduce drifts
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b= procutil_firlsFilter(0.5, cnt.fs);
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cnt= proc_filtfilt(cnt, b);
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% Artifact rejection based on variance criterion
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%mrk= reject_varEventsAndChannels(cnt, mrk, disp_ival, 'verbose', 1);
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% Segmentation
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epo= proc_segmentation(cnt, mrk, disp_ival);
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% Artifact rejection based on maxmin difference criterion on frontal chans
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[epo iArte] = proc_rejectArtifactsMaxMin(epo, crit_maxmin, 'Clab',crit_clab, ...
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'Ival',crit_ival, 'Verbose',1);
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% Baseline subtraction, and calculation of a measure of discriminability
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epo= proc_baseline(epo, ref_ival);
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epos_av{isub} = proc_average(epo, 'Stats', 1);
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% three different but almost equivalent ways to make statistics about class differences
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epos_diff{isub} = proc_classmeanDiff(epo, 'Stats', 1);
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epos_r{isub} = proc_rSquareSigned(epo, 'Stats', 1);
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% epos_auc{isub} = proc_aucValues(epo, 'Stats', 1);
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end
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% grand average
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epo_av = proc_grandAverage(epos_av, 'Average', 'INVVARweighted', 'Stats', 1, 'Bonferroni', 1, 'Alphalevel', 0.01);
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epo_r = proc_grandAverage(epos_r, 'Average', 'INVVARweighted', 'Stats', 1, 'Bonferroni', 1, 'Alphalevel', 0.01);
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epo_diff = proc_grandAverage(epos_diff, 'Average', 'INVVARweighted', 'Stats', 1, 'Bonferroni', 1, 'Alphalevel', 0.01);
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% epo_auc = proc_grandAverage(epos_auc, 'Average', 'INVVARweighted', 'Stats', 1, 'Bonferroni', 1, 'Alphalevel', 0.01);
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mnt = mnt_setElectrodePositions(epo_av.clab);
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mnt= mnt_setGrid(mnt, grd);
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% Select some discriminative intervals, with constraints to find N2, P2, P3 like components.
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constraint= ...
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{{-1, [100 300], {'I#','O#','PO7,8','P9,10'}, [50 300]}, ...
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{1, [200 350], {'P3-4','CP3-4','C3-4'}, [200 400]}, ...
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{1, [400 500], {'P3-4','CP3-4','C3-4'}, [350 600]}};
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[ival_scalps, nfo]= ...
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select_time_intervals(epo_r, 'Visualize', 0, 'VisuScalps', 1, ...
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'Title', util_untex(file), ...
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'Clab',{'not','E*'}, ...
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'Constraint', constraint);
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%printFigure('r_matrix', [18 13]);
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ival_scalps= visutil_correctIvalsForDisplay(ival_scalps, 'fs',epo.fs);
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% plot classwise grand-average ERPs
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fig_set(1);
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H= plot_scalpEvolutionPlusChannel(epo_av, mnt, clab, ival_scalps, defopt_scalp_erp, ...
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'ColorOrder',colOrder);
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grid_addBars(epo_r);
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%printFigure(['erp_topo'], [20 4+5*size(epo.y,1)]);
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% plot difference of the class means
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fig_set(2, 'shrink',[1 2/3]);
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plot_scalpEvolutionPlusChannel(epo_diff, mnt, clab, ival_scalps, defopt_scalp_r);
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%printFigure(['erp_topo_r'], [20 9]);
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% plot signed log10 p-values of the null hypothesis
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% that the difference of the class means is zero
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% interpretation: abs(sgnlogp) > 1 <--> p < 0.1
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% abs(sgnlogp) > 2 <--> p < 0.01
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% abs(sgnlogp) > 3 <--> p < 0.001 , and so on
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fig_set(3, 'shrink',[1 2/3]);
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epo_diff_sgnlogp = epo_diff;
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epo_diff_sgnlogp.x = epo_diff_sgnlogp.sgnlogp;
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epo_diff_sgnlogp.yUnit = 'sgnlogp';
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plot_scalpEvolutionPlusChannel(epo_diff_sgnlogp, mnt, clab, ival_scalps, defopt_scalp_r);
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%printFigure(['erp_topo_r'], [20 9]);
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% now plot differences again, with all insignificant results set to zero
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epo_diff.x = epo_diff.x.*epo_diff.sigmask;
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fig_set(4, 'shrink',[1 2/3]);
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plot_scalpEvolutionPlusChannel(epo_diff, mnt, clab, ival_scalps, defopt_scalp_r);
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