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eegEpoch.m
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eegEpoch.m
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% Creates an object from the class eegEpoch
% The class eegEpoch can handle epoched data either in channel or component space.
% The class defines methods for ERP computation, single trial wavelet denoising based on Quian and Garcia (2002),
% and time-frequency analysis. See examples below.
%
% Author: Alejandro Ojeda, Swartz Center for Computational Neuroscience, UCSD, 28-Aug-2012
% email: alejandro@sccn.ucsd.edu
%
% Reference: Quian Quiroga, R., and Garcia, H., Single-trial event-related potentials with wavelet denoising.
% Clinical Neurophysiology 114 (2003) 376???390
%
% Before running the examples below, download example_erp.mat from http://code.google.com/p/mobilab/downloads/
% Then copy the code in a new .m file and press Ctrl+R to uncomment
% %% Loading the data
% load example_erp.mat;
% % data: event locked time series data (EEG channel data or independent componet), dimensions: number of time points X number of trials
% % time: time vector in seconds, dimensions: number of time points X 1
% % channelLabel: label of the channel/or component, example: 'Pz' for channel Pz, 'IC1' for independent component #1
% % condition: name of the condition, example: 'Target', 'Non target', and so on
% % preStimulusMaxLatency: [time(i) time(j)] interval considered as pre-stimulus period, dimension: two elements vector
%
% %% Constructing the object
% epochDataObj = eegEpoch('data',data,'time',time,'channelLabel',label,'condition',condition,'preStimulusMaxLatency',preStimulusMaxLatency);
%
% %% Plotting stacked trials and ERP
% plot(epochDataObj);
%
% %% Time/Frequency Analysis
% % Log-spaced frequencies are recommended because that way you are doing narrow-band decomposition at low frequencies and broad-band at high frequencies.
% % See the help of scal2frq on the Wavelet Toolbox
% fmin = 2; % 1Hz
% fmax = 40; % 40 Hz
% numFreq = 64 % 64 frequencies
% wname = 'cmor1-1.5'; % complex morlet wavelet, see the help of 'cwt' on the Wavelet Toolbox
% plotFlag = true; % plot the results
% [coefficients,ersp,itc,frequency,time] = waveletTimeFrequencyAnalysis(epochDataObj,wname,fmin,fmax,numFreq,plotFlag);
classdef eegEpoch < epochObject
properties
preStimulusMaxLatency
erp = [];
end
methods
function obj = eegEpoch(varargin)
obj@epochObject(varargin{:});
if length(varargin) < 7
obj.preStimulusMaxLatency = [1 floor(length(obj.timeStamp)/2)];
else obj.preStimulusMaxLatency = varargin{7};
end
end
%%
function erp = get.erp(obj)
if isempty(obj.erp),
disp('Computing wavelet-denoised ERP.');
obj.computeERP;
end
erp = obj.erp;
end
%%
function hFigure = plot(obj,channel)
dim = obj.mmfObj.Format{1,2};
if length(dim) == 3, Nch = dim(3);else Nch = 1;end
if nargin < 2, channel = 1:Nch;end
Nch = length(channel);
if Nch > 1
for it=1:Nch, hFigure = plot(obj,channel(it));end
return
end
[~,loc] = min(abs(obj.timeStamp));
hFigure = figure('Color',[0.93 0.96 1]);
subplot(211);imagesc(obj.timeStamp,1:dim(2),obj.data(:,obj.sorting,channel)');
title(['Trials ' obj.label{channel} ' Condition: ' obj.condition]);
hold on;plot([1 1]*obj.timeStamp(loc),get(gca,'YLim'),'k-.','LineWidth',2);
subplot(212);plot(obj.timeStamp,obj.erp(:,channel));
set(gca,'Xlim',obj.timeStamp([1 end]))
title(['ERP ' obj.label{channel} ' Condition: ' obj.condition]);
hold on;plot([1 1]*obj.timeStamp(loc),get(gca,'YLim'),'k-.','LineWidth',2);
xlabel('Time (sec)')
end
%%
function rmThis = detectOutliers(obj,threshold,plotFlag)
if nargin < 2, threshold = 0.99;end
if nargin < 3, plotFlag = false;end
rmThis = false(size(obj.data,2),1);
for it=1:length(obj.label)
D= pdist(obj.data(:,:,it)');
Y = mdscale(D,3);
r = sqrt(sum(Y.^2,2));
th = raylinv(threshold, raylfit(r));
rmThis = any([rmThis r>th],2);
end
if plotFlag
figure('Color',[0.93 0.96 1]);hold on;
scatter(Y(:,1),Y(:,2),'.','linewidth',2);
scatter(Y(rmThis,1),Y(rmThis,2),'r.','linewidth',2);
title('MDS trials');grid on;
if any(rmThis), legend({'normal' 'outliers'});
else legend({'normal'});
end
axis xy
end
end
%%
function removeOutliers(obj,rmThis)
persistent flag
if ~isempty(flag), disp('You have removed outliers already.');return;end
if nargin < 2, rmThis = detectOuliers(obj,0.95);end
data = obj.data(:,~rmThis,:);
fid = fopen(obj.binFile,'w');fwrite(fid,data(:),class(data));fclose(fid);
obj.mmfObj = memmapfile(obj.binFile,'Format',{class(data) size(data) 'x'},'Writable',true);
flag = 1;
end
%%
function sortingByTrialSimilarity(obj)
dim = size(obj.data);
% dataSVD = svdDenoising4ERP(obj.data(:,:,1),8);
% X = zscore(dataSVD);
X = zscore(obj.data);
if length(obj.label) > 1
X = permute(X,[1 3 2]);
X = reshape(X,[dim(1)*dim(3) dim(2)]);
end
D= pdist(X');
Y = mdscale(D,2);
r = sqrt(sum(Y.^2,2));
[~,obj.sorting] = sort(r);
end
%%
function erp = computeERP(obj,alpha)
if nargin < 2, alpha = 0.95;end
data = waveletDenoising(obj,alpha);
dim = size(data);
erp = zeros(dim(1),size(data,3));
for it=1:size(data,3)
erp(:,it) = geometric_median( squeeze(data(:,:,it))')';
end
obj.erp = erp;
end
%%
function [fdata, bootstat] = waveletDenoising(obj,alpha,channel)
if nargin < 2, alpha = 0.95;end
dim = obj.mmfObj.Format{1,2};
if length(dim) == 3, Nch = dim(3);else Nch = 1;end
if nargin < 3, channel = 1:Nch;end
Nch = length(channel);
if Nch > 1
fdata = obj.data;
for it=1:Nch
disp(['Channel ' num2str(it)]);
fdata(:,:,channel(it)) = waveletDenoising(obj,alpha,channel(it));
end
return
end
data = obj.data(:,:,channel);
mu = mean(data(obj.preStimulusMaxLatency(1):obj.preStimulusMaxLatency(2),:));
X = bsxfun(@minus,data,mu);
erp = mean(X,2); %#ok
% scales = logspace(0.59,2.8,64);
dt = diff(obj.timeStamp([1 2]));
dim = size(X);
%--
s0 = 4*dt; ds = 0.25; NbSc = 32;
wname = 'morl';
%scales = freq2scales(1, 1./dt/2, 64, wname, dt);
SIG = {erp,dt}; %#ok
SCA = {s0,ds,NbSc,'pow',2};
WAV = {wname,4};
%--
%-- computing wavelet coefficients
cwtStruct = cwtft(SIG,'scales',SCA,'wavelet',WAV);
%-- computing ITC
cwtStructTmp = cwtft({X,dt},'scales',SCA,'wavelet',WAV);
nf = size(cwtStructTmp.cfs,1);
coef = reshape(cwtStructTmp.cfs,[nf dim]);
P = coef./abs(coef);
itc = squeeze(abs(mean(P,3)));
coef = permute(coef,[3 1 2]);
coef = reshape(coef,[dim(2) nf*dim(1)]);
dim = [dim(2) nf dim(1)];
nboots = 5;
bootstat = bootstrp(nboots,@boots_itc,coef,ones(dim(1),1)*dim);
th = raylinv(alpha, raylfit(bootstat(:)));
if th < 1
Iitc = itc(:) > th;
I = Iitc;
[x,~] = ind2sub(size(cwtStruct.cfs),find(I));
ux = unique(x);
[~,loc]= min(abs(((1:NbSc)'*ones(1,length(ux)) - ones(NbSc,1)*ux')));
Nl = length(loc);
nelem = zeros(Nl,1);
for it=1:Nl, nelem(it) = sum(x==loc(it));end
rmThis = nelem < 0.25*median(nelem);
if sum(rmThis) < Nl, ux(rmThis) = [];end
ux(ux<4) = [];
I = ismember(1:NbSc,ux);
I = I(:)*ones(1,length(cwtStruct.omega));
I = logical(I(:));
else
I = true(numel(cwtStruct.cfs),1);
end
%--
fdata = X;
for it=1:dim(1)
cwtStruct = cwtft({X(:,it),dt},'scales',SCA,'wavelet',WAV);
cwtStruct.cfs(~I) = 0;
fdata(:,it) = icwtft(cwtStruct,'signal',{X(:,it),dt})';
end
end
%%
function [coefficients,ersp,itc,frequency,time] = waveletTimeFrequencyAnalysis(obj,wname,fmin,fmax,numFreq,plotFlag,numberOfBoundarySamples,multCompCorrectionMethod, varargin)
T = diff(obj(1).timeStamp([1 2]));
if nargin < 2, wname = 'cmor1-1.5';end
if nargin < 3, fmin = 2;end
if nargin < 4, fmax = 1/T/2;end
if nargin < 5, numFreq = 64;end
if nargin < 6, plotFlag = true;end
if nargin < 7, numberOfBoundarySamples = 0;end
if nargin < 8, multCompCorrectionMethod = 'none';end
Nsubjects = length(obj);
if Nsubjects > 1
[ersp,itc,frequency,time] = subjectLevelWaveletTimeFrequencyAnalysis(obj,wname,fmin,fmax,numFreq,plotFlag, multCompCorrectionMethod, varargin);
coefficients = [];
return
end
data = obj.mmfObj.Data.x;
dim = size(obj.data);
data = reshape(data,[size(data,1) prod(dim(2:end))]);
scales = freq2scales(fmin, fmax, numFreq, wname, T);
frequency = scal2frq(scales,wname,T);
frequency = fliplr(frequency);
if ~numberOfBoundarySamples
toCut = round(0.05*length(obj.timeStamp));
else
toCut = numberOfBoundarySamples;
end
time = obj.timeStamp(toCut:end-toCut-1);
%-- computing wavelet coefficients
coefficients = zeros([length(scales) dim(1) prod(dim(2:end))]);
hwait = waitbar(0,'Computing cwt...','Color',[0.93 0.96 1]);
prodDim = prod(dim(2:end));
for it=1:prodDim
coefficients(:,:,it) = cwt(data(:,it),scales,wname);
waitbar(it/prodDim,hwait);
end
close(hwait);
% fliping frequency dimension
coefficients = permute(coefficients,[2 1 3]);
coefficients = reshape(coefficients,[dim(1) length(scales) dim(2:end)]);
coefficients = flipdim(coefficients,2);
if toCut > obj.preStimulusMaxLatency(1), t1 = toCut; else t1 = obj.preStimulusMaxLatency(1);end
if length(obj.timeStamp)-toCut <= obj.preStimulusMaxLatency(2)
obj.preStimulusMaxLatency(2) = length(obj.timeStamp)-toCut-t1;
t2 = obj.preStimulusMaxLatency(2);
else
t2 = length(obj.timeStamp)-toCut;
end
coefficientsDB = 10*log10(abs(coefficients).^2+eps);
base = mean(coefficientsDB(t1:t2,:,:,:));
coefficients = coefficients(toCut:end-toCut-1,:,:,:);
coefficientsDB = 10*log10(abs(coefficients).^2+eps);
ersp = bsxfun(@minus,coefficientsDB,(base)+eps);
% ersp = squeeze(mean(ersp,3));
ersp = permute(ersp,[3 1 2 4]);
finalDim = size(ersp);
ersp = geometric_median(ersp(:,:));
ersp = reshape(ersp,finalDim(2:end));
itc = coefficients./abs(coefficients);
itc = squeeze(abs(mean(itc,3)));
Nv = length(varargin);
switch multCompCorrectionMethod
case 'none'
% disp('Not significance test was computed.');
case 'bootstrap'
if Nv < 1, nboot = 1000; else nboot = varargin{1};end
if Nv < 2, alpha = 0.05; else alpha = varargin{2};end
% ersp
coefficientsDB = permute(coefficientsDB,[3 setdiff(1:ndims(coefficientsDB),3)]);
dim = size(coefficientsDB);
coefficientsDB = reshape(coefficientsDB,[dim(1) prod(dim(2:end))]);
bootstat = bootstrp(nboot,@boots_ersp,coefficientsDB,ones(dim(1),1)*[t1 t2],ones(dim(1),1)*dim);
bootstat = reshape(bootstat,[nboot dim(2:end)]);
ersp = reshape(ersp,[prod(dim(2:3)) length(obj.label)]);
I1 = false(prod(dim(2:3)),length(obj.label));
I2 = false(prod(dim(2:3)),length(obj.label));
for it=1:length(obj.label)
tmp = bootstat(:,:,:,it);
tmp = reshape(tmp,[nboot prod(dim(2:3))]);
maxmin = prctile(tmp,100*[alpha 1-alpha],2);
% th = [min(th(:,1)) max(th(:,2))];
th(1) = prctile(maxmin(:,1),100*alpha);
th(2) = prctile(maxmin(:,2),100*(1-alpha));
I = ersp(:,it) > th(1) & ersp(:,it) < th(2);
I1(:,it) = I;
ersp(I,it) = 0;
end
ersp = reshape(ersp,[dim(2:3) length(obj.label)]);
% itc
coefficientsTmp = permute(coefficients,[3 setdiff(1:ndims(coefficients),3)]);
coefficientsTmp = reshape(coefficientsTmp,[dim(1) prod(dim(2:end))]);
bootstat = bootstrp(nboot,@boots_itc,coefficientsTmp,ones(dim(1),1)*dim);
bootstat = reshape(bootstat,[nboot dim(2:end)]);
itc = reshape(itc,[prod(dim(2:3)) length(obj.label)]);
for it=1:length(obj.label)
% th = raylinv((1-alpha), raylfit(itc(:,it)));
tmp = bootstat(:,:,:,it);
tmp = reshape(tmp,[nboot prod(dim(2:3))]);
th = prctile(tmp,100*(1-alpha),2);
th = prctile(th,100*(1-alpha));
I = itc(:,it) < th;
I2(:,it) = I;
itc(I,it) = 0;
end
itc = reshape(itc,[dim(2:3) length(obj.label)]);
otherwise
error('Unknown method. Stick to bootstrap by now.');
end
if plotFlag
G = fspecial('gaussian',[4 4],2);
ersp_s = ersp;
itc_s = itc;
for it=1:length(obj.label)
ersp_s(:,:,it) = imfilter(ersp_s(:,:,it),G,'same');
itc_s(:,:,it) = imfilter(itc_s(:,:,it), G,'same');
%ersp(I1) = 0;
%itc(I2) = 0;
eegEpoch.imageLogData(time,frequency,ersp_s(:,:,it));
title(['ERSP (dB) ' obj.label{it} ' Condition: ' obj.condition]);
strTitle = ['ITC ' obj.label{it} ' Condition: ' obj.condition];
eegEpoch.imageLogData(time,frequency,itc_s(:,:,it),strTitle);
end
end
end
end
methods(Hidden)
function [t_ersp,t_itc,frequency,time] = subjectLevelWaveletTimeFrequencyAnalysis(obj,wname,fmin,fmax,numFreq,plotFlag, multCompCorrectionMethod, varargin)
Nsubjects = length(obj);
if Nsubjects < 2, error('You must input an array of eegEpoch objects, each element in the array containing single subject data.');end
T = diff(obj(1).timeStamp([1 2]));
if nargin < 2, wname = 'cmor1-1.5';end
if nargin < 3, fmin = 2;end
if nargin < 4, fmax = 1/T/2;end
if nargin < 5, numFreq = 64;end
if nargin < 6, plotFlag = true;end
if nargin < 7, multCompCorrectionMethod = 'none';end
[~,ersp,itc,frequency,time] = waveletTimeFrequencyAnalysis(obj(1),wname,fmin,fmax,numFreq,false);
ersp = repmat(ersp,[1 1 Nsubjects]);
itc = repmat(itc,[1 1 Nsubjects]);
for it=2:Nsubjects
[~,ersp(:,:,it),itc(:,:,it)] = waveletTimeFrequencyAnalysis(obj(it),wname,fmin,fmax,numFreq,false);
if ~mod(it,10), fprintf(' %i%',round(100*it/Nsubjects));end
end
fprintf('\n');
ersp = permute(ersp,[3 1 2]);
itc = permute(itc,[3 1 2]);
t_ersp = tStudent2Dmap(ersp);
t_itc = tStudent2Dmap(itc);
Nv = length(varargin);
switch multCompCorrectionMethod
case 'bootstrap'
if Nv < 1, nboot = 1000; else nboot = varargin{1};end
if Nv < 2, alpha = 0.05; else alpha = varargin{2};end
if Nv < 3, tail = 'both';else tail = varargin{3}; end
% ersp
bootstat{1} = bootstrp(nboot,@tStudent2Dmap,ersp);
bootstat{2} = bootstrp(nboot,@tStudent2Dmap,itc);
switch tail
case 'both'
th = prctile(bootstat{1},100*[alpha 1-alpha],2);
th = [min(th(:,1)) max(th(:,2))];
I = t_ersp > th(1) & t_ersp < th(2);
t_ersp(I) = 0;
th = prctile(bootstat{2},100*[alpha 1-alpha],2);
th = [min(th(:,1)) max(th(:,2))];
I = t_itc > th(1) & t_itc < th(2);
t_itc(I) = 0;
pval = 100*[alpha 1-alpha];
case 'right'
th = prctile(bootstat{1},100*alpha,2);
th = max(th);
I = t_ersp < th;
t_ersp(I) = 0;
th = prctile(bootstat{2},100*alpha,2);
th = max(th);
I = t_itc < th;
t_itc(I) = 0;
pval = 100*alpha;
case 'left'
th = prctile(bootstat{1},100*(1-alpha),2);
th = min(th);
I = t_ersp > th;
t_ersp(I) = 0;
th = prctile(bootstat{2},100*(1-alpha),2);
th = min(th);
I = t_itc > th;
t_itc(I) = 0;
pval = 100*alpha;
otherwise
error('Wrong tail, select from: ''both'', or ''right''.');
end
otherwise
error('Unknown method. Stick to bootstrap by now.');
end
if plotFlag
strTitle = ['T-ERSP, pval = [ ' num2str(pval) '], ' obj(1).label{1} ' Condition: ' obj(1).condition];
imageLogData(time,frequency,t_ersp,strTitle);
strTitle = ['T-ITC, pval = [ ' num2str(pval) '], ' obj(1).label{1} ' Condition: ' obj(1).condition];
imageLogData(time,frequency,t_itc,strTitle);
end
end
end
methods(Static)
function imageLogData(time,frequency,data,strTitle)
if nargin < 4, strTitle = '';end
figure('Color',[0.93 0.96 1]);
imagesc(time,log10(frequency),data');
hAxes = gca;
tick = get(hAxes,'Ytick');
fval = 10.^tick;
Nf = length(tick);
yLabel = cell(Nf,1);
fval(fval >= 10) = round(fval(fval >= 10));
for it=1:Nf, yLabel{it} = num2str(fval(it),3);end
mx = max(data(:));
if min(data(:)) < 0,
mn = -mx;
else
mn = min(data(:));
end
set(hAxes,'YDir','normal','Ytick',tick,'YTickLabel',yLabel,'CLim',[mn mx]);
[~,loc] = min(abs(time));
hold(hAxes,'on');plot([1 1]*time(loc),get(hAxes,'YLim'),'k-.','LineWidth',2);
xlabel('Time (sec)');
ylabel('Frequency (Hz)');
title(strTitle)
colorbar;
end
end
end
%-
function ersp = boots_ersp(coefficientsDB,preStimulusLatency,dim)
coefficientsDB = reshape(coefficientsDB,dim(1,:));
base = mean(coefficientsDB(:,preStimulusLatency(1,1):preStimulusLatency(1,2),:,:),2);
ersp = bsxfun(@minus,coefficientsDB,base+eps);
ersp = squeeze(mean(ersp));
end
%--
function itc = boots_itc(coefficients,dim)
coefficients = reshape(coefficients,dim(1,:));
itc = coefficients./abs(coefficients);
itc = squeeze(abs(mean(itc)));
end
%--
function [geometricMedian,convergenceHistory,weights]= geometric_median(x, varargin)
% geometricMedian = geometric_median(x, {key, value pairs})
% Input
%
% x is an N x M matrix, representing N observations of a M-dimensional matrix.
%
% Key, value pairs
%
% initialGuess is optional an 1 x M matrix, representing the initial guess for the gemetrix median
%
% tolerance an scalar value. It is the maximum relative change in geometricMedian vector (size of the change in
% the last iteration divided by the size of the geometricMedian vector) that makes the
% algorithm to continue to the next iteration. If relative change is less than tolerance, it is assumed
% that convergence is achieved.
% had a relative change more than tolerance then more iterations are performed.
% default = 1e-4.
%
% Output
% geometricMedian is an 1 x m matrix.
% convergenceHistory shows the value of maximum relative chage, which is compared to tolerance in
% each iteration.
% use mean as the median as an initial guess if none is provided.
inputOptions = finputcheck(varargin, ...
{'initialGuess' 'real' [] mean(x);...
'tolerance' 'real' [0 1] 1e-4;...
'maxNumberOfIterations' 'integer' [1 Inf] 1000;...
});
geometricMedian = inputOptions.initialGuess;
for i=1:inputOptions.maxNumberOfIterations
lastGeometricMedian = geometricMedian;
differenceToEstimatedMedian = bsxfun(@minus, x, geometricMedian);
sizeOfDifferenceToEstimatedMedian = (sum(differenceToEstimatedMedian .^2, 2) .^ 0.5);
oneOverSizeOfDifferenceToEstimatedMedian = 1 ./ sizeOfDifferenceToEstimatedMedian;
% to prevent nans
oneOverSizeOfDifferenceToEstimatedMedian(isinf(oneOverSizeOfDifferenceToEstimatedMedian)) = 1e20;
geometricMedian = sum(bsxfun(@times, x , oneOverSizeOfDifferenceToEstimatedMedian)) / sum(oneOverSizeOfDifferenceToEstimatedMedian);
%maxRelativeChange = max(max(abs(lastGeometricMedian - geometricMedian)) ./ abs(geometricMedian));
maxRelativeChange = (sum((lastGeometricMedian - geometricMedian).^2) / sum(geometricMedian.^2)) .^ 0.5;
if nargout > 1, convergenceHistory(i) = maxRelativeChange;end;
if (maxRelativeChange < inputOptions.tolerance || isnan(maxRelativeChange)), break;end
end
if nargout > 2
differenceToEstimatedMedian = bsxfun(@minus, x, geometricMedian);
sizeOfDifferenceToEstimatedMedian = (sum(differenceToEstimatedMedian .^2, 2) .^ 0.5);
oneOverSizeOfDifferenceToEstimatedMedian = 1 ./ sizeOfDifferenceToEstimatedMedian;
% to prevent nans
oneOverSizeOfDifferenceToEstimatedMedian(isinf(oneOverSizeOfDifferenceToEstimatedMedian)) = 1e20;
weights = oneOverSizeOfDifferenceToEstimatedMedian / sum(oneOverSizeOfDifferenceToEstimatedMedian);
end
end
%%
function t = tStudent2Dmap(x)
n = size(x,1);
t = mean(x)./(std(x)+eps)/sqrt(n);
t = squeeze(t);
end