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pac_man.m
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% pac_man() - Computes phase-amplitude coupling measure. Requires inputs
% from pac_pop_main.
%
% Usage:
% >> EEG = pac_man(EEG, varargin);
% >> EEG = pac_man(EEG, 'lfoPhase', [1,4], 'hfoAmp', [80,300],...
% 'hfoTopRatio', 10, 'hfoPool', 1, 'whichMarker', 1,...
% 'windowLength', [], 'alpha', 0.05, 'numSurro', 500,...
% 'numPhaseBin', 30);
%
% Input arguments (ALL necessary):
% lfoPhase : Band-pass frequencies for low-frequency phase.
% hfoAmp : Band-pass frequencies for high-frequency oscillation
% (HFO) amplitude.
% hfoTopRatio : Highest amplitude rate in HFO (unit: percent)
% hfoPool : 1, each channel (default); 2, all channels; 3, use
% user-selected event markers generated by Mobilab-based
% single-channel event marking tool developed for
% Children's Hospital of Michigan (not generally available;
% if interested, please email Makoto Miyakoshi); 4, use
% user-selected event markers generated by EEGLAB. When
% this option is selected, phase-amplitude coupling is
% computed from the data points at windows centered at
% the preselected events. The surrogate statistics is
% calculated by using the same data points for the
% amplitude but different data points sampled from all
% the valid (i.e. not within +/- 1-s window from the
% 'boundary') data points.
% for the optimal automatic processes.
% whichMarker : When user-selected event marker is specified for 'hfoPool',
% select the type of event to use for event markers.
% Otherwise, can be left empty. The index of the event
% marker used can be identified by running
% eventTypes = unique({EEG.event.type}).
% windowLength : When user-selected event marker is specified for 'hfoPool',
% select the window length. Ex. 500 means a window of
% +/- 500 ms from the center of the event marker will be
% used for a window. Overlapped winodws are not counted
% only once.
% alpha : statistical significance level. Eg. 0.05 -> 5% level.
% numSurro : Number of iteration for surrogation test. This test
% method is explained in Canolty et al. (2006). For a
% preliminary test, use 200-500. For publication, use 2000-.
% numPhaseBin : Number of equidistant phase bins. Increasing this
% number results in increase of statistical power. Use
% 20-30 for start.
%
% Outputs: All outputs are located in EEG.pac.
% analyticEEG : Band-pass filterd low-frequency phase coupled with
% band-pass filtered high-frequency oscillation
% amplitude. Same size as EEG.data.
% hfoIndex : Data indices of selected highest amplitudes.
% angleMean : Mean angles of data selected with hfoIndex.
% vectLength : Resultant vector length of data selected with hfoIndex.
% vectLengthVar : Variance of the resultant vector length.
% histBinMax : Maximum values in histograms for drawing graphs.
% phaseProbability : Number of data count in each phase bins.
% phaseSrotedAmp : Mean amplitdes of each phase bins.
% phaseSortedAmpSe : Standard errors of amplitdes in each phase bins.
%
% When hfoPool==4 (i.e. event-related window-wise PAC), the following items are generated and stored.
% windowMeanAmp : Mean high-freq amp across selected widnows.
% windowStdAmp : Standard dev. of high-freq amp across selected widnows.
% windowMeanPhase : Circular-mean low-freq amp across selected widnows.
% windowStdPhase : Circular-std low-freq amp across selected widnows.
%
% These four structures contains results from different multiple comparison corrections.
% uncorrected : Channel-wise multiple comparison uncorrected.
% Bonferroni : Channel-wise multiple comparison with Bonferroni.
% BonfHolm : Channel-wise multiple comparison with Bonferroni-Holm.
% FDR : Channel-wise multiple comparison with false discovery rate.
%
% Each of the structure contains p-values from various tests:
% MIpval : Results from phase permutation on Modulation Index.
% phaseRayleighPval : Results from Rayleigh test on phase distribution.
% phaseOmniTestPval : Results from Omnibus test on phase distribution.
% phaseRaoSpacePval : Results from Rao's spacing test on phase distribution.
% This is currently not used for weired results.
% phaseChi2GofPval : Results from Chi-square goodness of fit test on phase distribution.
% phaseKstestPval : Results from Kolmogorov-Smirnov test on phase distribution.
% ampChi2GofPval : Results from Chi-square goodness of fit test on phase-bin-mean amplitude across phase bins.
% ampKstestPval : Results from Kolmogorov-Smirnov test on phase-bin-mean amplitude across phase bins.
%
% In the MIpval, the observed Modulation Index is tested against the null
% hypothesis that there is no phase-amplitude coupling in the observed data.
% In the phaseXXXPval, the phase distribution of the HFO-indexed data are tested
% against the null hypothesis that the observed phase distributions are uniform.
% In the ampXXXPval, the phase-bin-mean amplitudes are tested against the
% null hypothesis that the observed phase-bin-mean amplitudes are uniformly
% distributed.
%
% Note that all of these statistics except for MIpval are critically affected
% by numPhaseBin. If you set numPhaseBin to 10000 (suppose your data is longer
% than that), p-value will be ridiculously small (such as 1.616199e-35).
%
% External toolbox dependency:
% Berens (2009) circstat: A MATLAB toolbox for circular statistics. J stat softw. 31.
% History:
% 01/30/2025 Makoto. Subsampling for 'binValue' is fixed again. Thanks Amir Mohammad Khezri!
% 08/16/2024 Makoto and Henrico. Supported PAC-ERP visualization suggested by Henrioco Stam, Erasmus University.
% 08/14/2024 Makoto. Fixed whichMarker-1 to whichMarker. Rewrote the stats.
% 08/09/2024 Makoto. Fix request by Henrico. 'hfoPool', '4' is added.
% 01/13/2021 Makoto. Checked for moving to Github.
% 11/04/2020 Makoto. Removing 1-s data before and after data edges and boundaries to avoid filter's edge effect for window-rejected data.
% 07/24/2019 Makoto. 'binValue' size mismatch issue fixed again. Thanks Brian Kavanaugh!
% 05/10/2019 Makoto. 'binValue' size mismatch issue fixed. Thanks Brian Kavanaugh!
% 02/01/2017 ver 5.2 by Makoto. Abnormally low amplitude channel error added.
% 06/03/2014 ver 5.1 by Makoto. phasebin+1 changed.
% 06/21/2013 ver 5.0 by Makoto. Watson-Williams test added.
% 04/16/2013 ver 4.1 by Makoto. Confidence Interval option added.
% 12/26/2012 ver 4.0 by Makoto. Optimized for Matrix calculation. Statistics upgraded. Help written.
% 11/02/2012 ver 3.0 by Makoto. Statistics and AA added.
% 10/31/2012 ver 2.1 by Makoto. fixed EEG.pac.maskESC_N = pacAllChan4 < critical4N;
% 10/22/2012 ver 2.0 by Makoto. Made into a part of plugin.
% 09/18/2012 ver 1.1 by Makoto. More accurate window length. EEG.pac.
% 09/14/2012 ver 1.0 by Makoto. Prototype created.
% Copyright (C) 2012 Makoto Miyakoshi, JSPS/SCCN,INC,UCSD; Cincinnati Children's Hospital Medical Center.
%
% 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
function EEG = pac_man(EEG, varargin)
%%%%%%%%%%%%%%%%%%%
%%% input check %%%
%%%%%%%%%%%%%%%%%%%
if nargin < 18
help pac_man
return
end
result = finputcheck(varargin, {...
'lfoPhase' 'real' [0 Inf] []
'hfoAmp' 'real' [0 Inf] []
'hfoTopRatio' 'real' [0 100] []
'hfoPool' 'integer' [1 3] []
'whichMarker' 'integer' [1 Inf] []
'windowLength' 'integer' [1 Inf] []
'alpha' 'real' [0 100] []
'numSurro' 'integer' [1 Inf] []
'numPhaseBin' 'integer' [1 Inf] []});
if ischar(result), error(result); end
clear result
% Check dead channels
allChanStd = std(EEG.data, [], 2);
allChanStdRatio = allChanStd/median(allChanStd);
if any(allChanStdRatio<0.001)
badChanIdx = find(allChanStdRatio<0.001); % 06/19/2020 Makoto. Loosened.
for chIdx = 1:length(badChanIdx)
fprintf('Channel %.0f have abnormally low SD. Remove this channel from analysis.\n', badChanIdx(chIdx))
end
error('Use ''Edit''->''Select data'' to remove the channels above.')
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% remove existing pac %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%
if isfield(EEG, 'pac')
EEG = rmfield(EEG, 'pac');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% initial parameter setting %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
lfoPhaseInput = vararginReader('lfoPhase', varargin);
hfoAmpInput = vararginReader('hfoAmp', varargin);
hfoTopRatioInput = vararginReader('hfoTopRatio', varargin);
hfoPoolInput = vararginReader('hfoPool', varargin);
whichMarkerInput = vararginReader('whichMarker', varargin);
windowLengthInput = vararginReader('windowLength', varargin);
alphaInput = vararginReader('alpha', varargin);
numSurroInput = vararginReader('numSurro', varargin);
numPhaseBinInput = vararginReader('numPhaseBin', varargin);
%%%%%%%%%%%%%%%%%%%%%%%%
%%% store parameters %%%
%%%%%%%%%%%%%%%%%%%%%%%%
EEG.pac.lfoPhase = lfoPhaseInput;
EEG.pac.hfoAmp = hfoAmpInput;
EEG.pac.hfoTopRatio = hfoTopRatioInput;
EEG.pac.hfoPool = hfoPoolInput;
EEG.pac.whichMarker = whichMarkerInput;
EEG.pac.windowLength = windowLengthInput;
EEG.pac.alpha = alphaInput;
EEG.pac.numSurro = numSurroInput;
EEG.pac.numPhaseBin = numPhaseBinInput;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% reserve memory for storing results %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
EEG.pac.analyticEEG = zeros(size(EEG.data));
%%%%%%%%%%%%%%%%%%
%%% DC removal %%%
%%%%%%%%%%%%%%%%%%
EEG.data = bsxfun(@minus, EEG.data, mean(EEG.data,2));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% compute analytic EEG %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Low-frequency instantaneous phase.
EEG_lowFreqPhase = pop_eegfiltnew(EEG, lfoPhaseInput(1), lfoPhaseInput(end));
lowFreqPhase_data = EEG_lowFreqPhase.data;
analyPhase = angle(hilbert(lowFreqPhase_data'))';
% High-frequency instantaneous amplitude.
EEG_highFreqAmp = pop_eegfiltnew(EEG, hfoAmpInput(1), hfoAmpInput(end));
highFreqAmp_data = EEG_highFreqAmp.data;
analyAmp = abs(hilbert(highFreqAmp_data'))';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Remove 1-s peri-boundary data points. %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This is mainly for window-rejected data to avoid filter's edge effect (11/04/2020 Makoto. Added).
if ~isempty(EEG.event)
boundaryIdx = find(strcmp({EEG.event.type}, 'boundary'));
if ~isempty(boundaryIdx)
boundaryLatency = round([EEG.event(boundaryIdx).latency]);
periBoundaryIdx = [1:EEG.srate (1+EEG.pnts-EEG.srate):EEG.pnts]; % The first and the last 1-s by the edge effect.
for boundaryIdx = 1:length(boundaryLatency)
periBoundaryIdx = [periBoundaryIdx 1+boundaryLatency(boundaryIdx)-EEG.srate:boundaryLatency(boundaryIdx)+EEG.srate]; %#ok
end
periBoundaryIdx = unique(periBoundaryIdx);
% Trim indices outside the data range.
periBoundaryIdx(periBoundaryIdx<1) = [];
periBoundaryIdx(periBoundaryIdx>EEG.pnts) = [];
else
boundaryLatency = [];
periBoundaryIdx = [1:EEG.srate (1+EEG.pnts-EEG.srate):EEG.pnts]; % The first and the last 1-s by the edge effect.
end
else
boundaryLatency = [];
periBoundaryIdx = [1:EEG.srate (1+EEG.pnts-EEG.srate):EEG.pnts]; % The first and the last 1-s by the edge effect.
end
offTheBoundaryIdx = setdiff(1:EEG.pnts, periBoundaryIdx);
analyAmp_offTheBoundary = analyAmp( :,offTheBoundaryIdx);
analyPhase_offTheBoundary = analyPhase(:,offTheBoundaryIdx);
fprintf('\n\n%.0f boundaries found. %.1f s data will be removed.\n\n\n', length(boundaryLatency), length(periBoundaryIdx)/EEG.srate)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Combine the low-freq phase with the high-freq amp. %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
analyticEEG_PAC = analyAmp_offTheBoundary.*exp(1i*analyPhase_offTheBoundary);
EEG.pac.analyticEEG = analyticEEG_PAC;
clear EEG_*
switch hfoPoolInput
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Channel-wise pooling %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
case 1 % Re-done. (01/30/2025)
for chIdx = 1:EEG.nbchan
critical = prctile(analyAmp_offTheBoundary(chIdx,:), 100-EEG.pac.hfoTopRatio);
EEG.pac.hfoIndex{chIdx,1} = find(analyAmp_offTheBoundary(chIdx,:)>critical);
end
% analyAmpSort = sort(analyAmp_offTheBoundary, 2, 'descend');
% critical = floor(length(analyAmpSort)*hfoTopRatioInput/100);
% criticalValues = analyAmpSort(:, critical);
% logicalHasMask = analyAmp_offTheBoundary >= repmat(criticalValues, [1 size(analyAmp_offTheBoundary,2)]);
% for chIdx = 1:EEG.nbchan
% EEG.pac.hfoIndex{chIdx,1} = find(logicalHasMask(chIdx,:));
% end
%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Whole-data pooling %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%
case 2 % Re-done. (01/30/2025)
critical = prctile(analyAmp_offTheBoundary(:), 100-EEG.pac.hfoTopRatio);
for chIdx = 1:EEG.nbchan
EEG.pac.hfoIndex{chIdx,1} = find(analyAmp_offTheBoundary(chIdx,:)>critical);
end
% tmp = analyAmp_offTheBoundary(:);
% tmpSort = sort(tmp, 'descend');
% critical = tmpSort(floor(length(tmpSort)*EEG.pac.hfoTopRatio/100));
% globalHfoIndex = tmp >= critical;
%
% % obtain globalHFOmask
% globalHFOmask = false(length(tmpSort),1);
% globalHFOmask(globalHfoIndex) = 1;
% globalHFOmask = reshape(globalHFOmask, size(EEG.data));
% for chIdx = 1:EEG.nbchan
% EEG.pac.hfoIndex{chIdx,1} = find(globalHFOmask(chIdx,:));
% end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Handpicked window centers +/- halfWinLen, one event per channel (Mobilab-based event marking, depricated). %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
case 3 % handpicked
error('This option requires update of Mobilab. Email [email protected] if you want to use it.')
% % create a list that has channel and latency of the selected event
% eventTypes = unique({EEG.event.type});
% tmpMarker = eventTypes{whichMarker-1};
% hitIndex = strcmp({EEG.event.type}, tmpMarker);
% hitIndex = find(hitIndex);
% tmpChanList = cell2mat({EEG.event(1,hitIndex).channel})';
% tmpLatencyList = cell2mat({EEG.event(1,hitIndex).latency})';
% hfoList = cat(2, tmpChanList, tmpLatencyList);
%
% % create HFO index
% EEG.pac.hfoIndex{EEG.nbchan,1} = []; % prepare cell array
% for chIdx = 1:EEG.nbchan
% tmpHfoListIndex = hfoList(:,1)==chIdx;
% if any(tmpHfoListIndex)
% tmpHfoLatencyList = hfoList(tmpHfoListIndex, 2);
% tmpHfoListWin = zeros(1,length(tmpHfoLatencyList)*windowLength*2);
% for m = 1:length(tmpHfoLatencyList)
% tmpIndex = (m-1)*windowLength*2+1:m*windowLength*2;
% tmpHfoListWin(1,tmpIndex) = tmpHfoLatencyList(m)-windowLength:tmpHfoLatencyList(m)+windowLength-1;
% end
% tmpHfoListWin(tmpHfoListWin<1) =[]; % exclude negative latency
% tmpHfoListWin(tmpHfoListWin>EEG.pnts)=[]; % exclude out of range latency
% tmpHfoListWin = unique(tmpHfoListWin); % exclude overlap
% EEG.pac.hfoIndex{chIdx,1} = tmpHfoListWin;
% end
% end
%
% % create non-empty channel list
% nonEmptyChannelList = false(1,length(EEG.pac.hfoIndex));
% for chIdx = 1:length(EEG.pac.hfoIndex)
% nonEmptyChannelList(chIdx,1) = ~isempty(EEG.pac.hfoIndex{chIdx,1});
% end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% EEGLAB's event-based window selection defined by center +/- halfWinLen. %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
case 4 % Requested by Henrico Stam, Erasmus University.
% Obtain the target event times.
allEventTypes = unique({EEG.event.type});
%selectedEvent = allEventTypes{whichMarker-1};
selectedEvent = allEventTypes{whichMarkerInput};
eventIdx = find(strcmp({EEG.event.type}, selectedEvent));
latencyIdx = cell2mat({EEG.event(eventIdx).latency})';
% Obtain the target event window edges.
winddowLengthInFrame = round(windowLengthInput/(1000/EEG.srate));
selectedWindowEdges = [latencyIdx-winddowLengthInFrame latencyIdx+winddowLengthInFrame];
% Exclude out of data range windows.
outOfDataRangeWinIdx = selectedWindowEdges(:,1)<0 | selectedWindowEdges(:,2)>EEG.pnts;
selectedWindowEdges(outOfDataRangeWinIdx,:) = [];
% Obtain good window time indices in 1:EEG.pnts.
goodWindowIdx2D = zeros(winddowLengthInFrame*2, size(selectedWindowEdges,1));
for selectedWinIdx = 1:size(goodWindowIdx2D,2)
goodWindowIdx2D(:,selectedWinIdx) = selectedWindowEdges(selectedWinIdx,1)+1:selectedWindowEdges(selectedWinIdx,2);
end
periEventWindowIdx = round(goodWindowIdx2D(:));
% Obtain non-peri-boundary time indices in 1:EEG.pnts.
nonPeriBoundaryTimeIdx = setdiff(1:EEG.pnts, periBoundaryIdx);
% Obtain the inclusive mask.
[~, inclusiveMask] = intersect(nonPeriBoundaryTimeIdx, periEventWindowIdx);
fprintf('\n\n%.1f%% of data removed for being located within 1-s from a boundary.', 100*(1-length(inclusiveMask)/(size(goodWindowIdx2D,1)*size(goodWindowIdx2D,2))))
% Reshape the extractetd 3D tensor to 2D.
analyAmpSort = sort(analyAmp_offTheBoundary(:,inclusiveMask), 2, 'descend');
critical = floor(length(analyAmpSort)*hfoTopRatioInput/100);
criticalValues = analyAmpSort(:, critical);
logicalHasMask = analyAmp_offTheBoundary >= repmat(criticalValues, [1 size(analyAmp_offTheBoundary,2)]);
logicalHasMask(:,periBoundaryIdx) = false;
for chIdx = 1:EEG.nbchan
EEG.pac.hfoIndex{chIdx,1} = find(logicalHasMask(chIdx,:));
end
%% Another request for later visualization addressed here.
% Exclude peri-boundary windows.
periBoundaryWindowIdx = [];
for windowIdx = 1:size(selectedWindowEdges,1)
if any(intersect(periBoundaryIdx, selectedWindowEdges(windowIdx,1):selectedWindowEdges(windowIdx,2)))
periBoundaryWindowIdx = [periBoundaryWindowIdx; windowIdx]; %#ok
end
end
% Reject the windows.
fprintf('%d/%d windows rejected for being located within 1-s from boundaries.\n\n\n\n', length(periBoundaryWindowIdx), size(selectedWindowEdges,1))
winEdgesAfterRejection = selectedWindowEdges;
winEdgesAfterRejection(periBoundaryWindowIdx,:) = [];
windowLength = median(diff(winEdgesAfterRejection,1,2));
winAmpMatrix = zeros(EEG.nbchan, windowLength, size(winEdgesAfterRejection,1));
winPhaseMatrix = zeros(EEG.nbchan, windowLength, size(winEdgesAfterRejection,1));
for windowIdx = 1:size(winEdgesAfterRejection,1)
winAmpMatrix( :,:,windowIdx) = analyAmp( :, winEdgesAfterRejection(windowIdx,1)+1:winEdgesAfterRejection(windowIdx,2));
winPhaseMatrix(:,:,windowIdx) = analyPhase(:, winEdgesAfterRejection(windowIdx,1)+1:winEdgesAfterRejection(windowIdx,2));
end
meanAmp = mean(winAmpMatrix, 3);
stdAmp = std(winAmpMatrix, 0, 3);
meanPhase = circ_mean(winPhaseMatrix, [], 3);
stdPhase = circ_std( winPhaseMatrix, [], [], 3);
%{
figure
for chIdx = 1:32
subplot(5,7,chIdx)
plot(meanAmp(chIdx,:)); hold on; plot(meanPhase(chIdx,:))
end
%}
% Store the results.
EEG.pac.winEdgesAfterRejection = winEdgesAfterRejection;
EEG.pac.windowMeanAmp = meanAmp;
EEG.pac.windowStdAmp = stdAmp;
EEG.pac.windowMeanPhase = meanPhase;
EEG.pac.windowStdPhase = stdPhase;
end
%%%%%%%%%%%%%%%%%%%%%%%
%%% reserve results %%%
%%%%%%%%%%%%%%%%%%%%%%%
EEG.pac.angleMean = zeros(EEG.nbchan,1);
EEG.pac.angleStd = zeros(EEG.nbchan,1);
EEG.pac.vectLength = zeros(EEG.nbchan,1);
EEG.pac.vectLengthVar = zeros(EEG.nbchan,1);
EEG.pac.histBinMax = zeros(EEG.nbchan,1);
EEG.pac.mi = zeros(EEG.nbchan,1);
EEG.pac.moduInd95CI = zeros(EEG.nbchan,1);
EEG.pac.moduInd99CI = zeros(EEG.nbchan,1);
EEG.pac.phaseBinValues = zeros(EEG.nbchan, numPhaseBinInput+1); % 06/03/2014
EEG.pac.uncorrected.MIpval = ones(EEG.nbchan,1);
EEG.pac.uncorrected.phaseRayleighPval = ones(EEG.nbchan,1);
EEG.pac.uncorrected.phaseOmniTestPval = ones(EEG.nbchan,1);
EEG.pac.uncorrected.phaseRaoSpacePval = ones(EEG.nbchan,1);
EEG.pac.uncorrected.phaseWtsnWillPval = ones(EEG.nbchan,1);
EEG.pac.uncorrected.ampChi2GofPval = ones(EEG.nbchan,1);
EEG.pac.uncorrected.ampKstestPval = ones(EEG.nbchan,1);
EEG.pac.Bonferroni.MIpval = ones(EEG.nbchan,1);
EEG.pac.Bonferroni.phaseRayleighPval = ones(EEG.nbchan,1);
EEG.pac.Bonferroni.phaseOmniTestPval = ones(EEG.nbchan,1);
EEG.pac.Bonferroni.phaseRaoSpacePval = ones(EEG.nbchan,1);
EEG.pac.Bonferroni.phaseWtsnWillPval = ones(EEG.nbchan,1);
EEG.pac.Bonferroni.ampChi2GofPval = ones(EEG.nbchan,1);
EEG.pac.Bonferroni.ampKstestPval = ones(EEG.nbchan,1);
EEG.pac.BonfHolm.MIpval = ones(EEG.nbchan,1);
EEG.pac.BonfHolm.phaseRayleighPval = ones(EEG.nbchan,1);
EEG.pac.BonfHolm.phaseOmniTestPval = ones(EEG.nbchan,1);
EEG.pac.BonfHolm.phaseRaoSpacePval = ones(EEG.nbchan,1);
EEG.pac.BonfHolm.phaseWtsnWillPval = ones(EEG.nbchan,1);
EEG.pac.BonfHolm.ampChi2GofPval = ones(EEG.nbchan,1);
EEG.pac.BonfHolm.ampKstestPval = ones(EEG.nbchan,1);
EEG.pac.FDR.MIpval = ones(EEG.nbchan,1);
EEG.pac.FDR.phaseRayleighPval = ones(EEG.nbchan,1);
EEG.pac.FDR.phaseOmniTestPval = ones(EEG.nbchan,1);
EEG.pac.FDR.phaseRaoSpacePval = ones(EEG.nbchan,1);
EEG.pac.FDR.phaseWtsnWillPval = ones(EEG.nbchan,1);
EEG.pac.FDR.ampChi2GofPval = ones(EEG.nbchan,1);
EEG.pac.FDR.ampKstestPval = ones(EEG.nbchan,1);
EEG.pac.phaseProbability = cell(EEG.nbchan,1);
EEG.pac.phaseSortedAmp = cell(EEG.nbchan,1);
EEG.pac.phaseSortedAmpSe = cell(EEG.nbchan,1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Compute PAC and perform statistics %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
waitBar = waitbar(0,'Computing start');
for chIdx = 1:EEG.nbchan
% update waitbar
waitbar(chIdx/EEG.nbchan, waitBar, ['Processing Channel ' num2str(chIdx)]);
hfoIndex = EEG.pac.hfoIndex{chIdx,1};
if isempty(hfoIndex)
disp(['Channel ' num2str(chIdx) ' has no HFO marker.'])
else
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Compute HFO-indexed data %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if hfoPoolInput == 1 || hfoPoolInput == 2
analyAmp_offTheBoundary_included = abs( EEG.pac.analyticEEG(chIdx,:));
analyPhase_offTheBoundary_included = angle(EEG.pac.analyticEEG(chIdx,:));
elseif hfoPoolInput == 4
analyAmp_offTheBoundary_included = analyAmp_offTheBoundary( chIdx, :);
analyPhase_offTheBoundary_included = analyPhase_offTheBoundary(chIdx, :);
end
hfoPreselectedAmp = analyAmp_offTheBoundary_included( hfoIndex);
hfoPreselectedPhase = analyPhase_offTheBoundary_included(hfoIndex);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Compute Modulation Index of this HFO %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
trueMI = abs(mean(hfoPreselectedAmp.*exp(1i*hfoPreselectedPhase)));
EEG.pac.mi(chIdx,1) = trueMI;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Compute descriptive statistics %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% vector length mean
EEG.pac.vectLength(chIdx,1) = circ_r(hfoPreselectedPhase');
% vector length variance
EEG.pac.vectLengthVar(chIdx,1) = circ_var(hfoPreselectedPhase');
% angle mean
tmpAngle = circ_mean(hfoPreselectedPhase');
% angle std
tmpAngleStd = circ_std(hfoPreselectedPhase');
% converting angle scale from -pi<x<pi to 0<x<2pi
if tmpAngle < 0
tmpAngle = 2*pi+tmpAngle;
end
% store results
EEG.pac.angleMean(chIdx,1) = tmpAngle;
EEG.pac.angleStd(chIdx,1) = tmpAngleStd;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Statistical test for MI %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
surroPhaseTensor = zeros(length(hfoIndex), numSurroInput);
for surroIterIdx = 1:numSurroInput
surroPhaseIdx = randperm(size(analyPhase_offTheBoundary,2), length(hfoIndex));
surroPhaseTensor(:,surroIterIdx) = analyPhase_offTheBoundary(chIdx, surroPhaseIdx);
end
trueAmpTensor = repmat(hfoPreselectedAmp', [1 numSurroInput]);
SurroMI = abs(mean(trueAmpTensor.*exp(1i*surroPhaseTensor), 1));
% 08/14/2024 Makoto.
% I found my previous approach does not make much sense. Why is the range of phase rotation limited to the length of hfoPhase, rather than to the entire valid data?
%{
minskip = 1;
maxskip = length(hfoPhase)-1;
skipIdx = ceil(length(hfoPhase).*rand(numSurro*3,1));
skipIdx(skipIdx>maxskip) = [];
skipIdx(skipIdx<minskip) = [];
skipIdx = skipIdx(1:numSurro,1);
surroPhase = zeros(length(hfoIndex), numSurro);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% run matrix computation (much faster) %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for surroIdx = 1:numSurro
tmpSurroPhase = [analyPhase(skipIdx(surroIdx):end) analyPhase(1:skipIdx(surroIdx)-1)]; % randomize phase instead of amp- consider circular shifts
surroPhase(:,surroIdx) = tmpSurroPhase(hfoIndex)';
end
%}
%%%%%%%%%%%%%%%
%%% p-value %%%
%%%%%%%%%%%%%%%
currentPval = stat_surrogate_pvals(SurroMI, trueMI, 'right');
%{
% Parametric statistics just for interest.
meanSurro = mean(SurroMI);
stdSurro = std(SurroMI);
Y = normpdf(double(trueMI), meanSurro, stdSurro)
%}
EEG.pac.uncorrected.MIpval(chIdx,1) = currentPval;
%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Confidence interval %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%
EEG.pac.moduInd95CI(chIdx,1) = prctile(SurroMI, 95);
EEG.pac.moduInd99CI(chIdx,1) = prctile(SurroMI, 99);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Statistical test for circular distribution of HFO angles %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 01/31/2025 Makoto. Subsampling part fixed.
hfoPreselectedPhaseSorted = sort(hfoPreselectedPhase);
binSize = floor(length(hfoPreselectedPhaseSorted)/(numPhaseBinInput+1));
% If hfoPreselectedPhaseSorted is shorter than the number of phase bins
if binSize==0
error('Length of identified data is shorter than the number of phase bins. Change input parameters.')
end
hfoPreselectedPhaseTrimmed = hfoPreselectedPhaseSorted(1:binSize*(numPhaseBinInput+1));
subsamplingIdx = 1:binSize:length(hfoPreselectedPhaseTrimmed);
representativeBinValueVec = hfoPreselectedPhaseSorted(subsamplingIdx);
% % 07/23/2019 Makoto. Upon fix request by Brian Kavanaugh.
% sortHfoPhase = sort(hfoPreselectedPhase);
% binSize = length(hfoPreselectedPhase)/size(EEG.pac.phaseBinValues,2);
% nonIntegerIdx = 1:binSize:length(hfoPreselectedPhase);
% integerIdx = round(nonIntegerIdx);
% binValue = sortHfoPhase(integerIdx);
% % Sample every N points. Note that 'binValue' must contain
% % sortHfoPhase(1) and sortHfoPhase(end), but the latter one can be
% % missed by this indexing -- fixed (05/10/2019 Makoto)
% binValue = sortHfoPhase(1:binSize:length(hfoPreselectedInstPhase));
% if length(binValue) == size(EEG.pac.phaseBinValues,2)-1
% binValue(end+1) = sortHfoPhase(end);
% end
% % I thought it is this kind of problem, but it is not (07/22/2019)
% minusPiToPiEdges = linspace(-pi, pi, size(EEG.pac.phaseBinValues,2));
% [binValue,EDGES] = histcounts(sortHfoPhase, minusPiToPiEdges);
% binSize = floor(length(hfoPreselectedInstPhase)/numPhaseBin);
% sortHfoPhase = sort(hfoPreselectedInstPhase);
% binValue = sortHfoPhase(1:binSize:length(hfoPreselectedInstPhase));
% if length(binValue) == size(EEG.pac.phaseBinValues,2)-1
% binValue(end+1) = sortHfoPhase(end);
% end
%
% % sample every N points
% binSize = floor(length(hfoPreselectedInstPhase)/numPhaseBin);
% sortHfoPhase = sort(hfoPreselectedInstPhase);
% binValue = sortHfoPhase(1:binSize:length(hfoPreselectedInstPhase));
% store bin values
EEG.pac.phaseBinValues(chIdx,:) = representativeBinValueVec';
% uniform distribution statistics (3 circular statistics)
EEG.pac.uncorrected.phaseRayleighPval(chIdx,1) = circ_rtest(representativeBinValueVec); % Rayleigh test
EEG.pac.uncorrected.phaseOmniTestPval(chIdx,1) = circ_otest(representativeBinValueVec); % omnibus or Hodges-Ajne test test
EEG.pac.uncorrected.phaseRaoSpacePval(chIdx,1) = circ_raotest(representativeBinValueVec); % Rao's spacing test
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Compute phase-sorted amplitude %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
edges = linspace(-pi,pi,numPhaseBinInput+1);
[phaseProbability, ~, binIndex] = histcounts(hfoPreselectedPhase, edges);
phaseSortedAmp = zeros(numPhaseBinInput,1);
phaseSortedAmpSe = zeros(numPhaseBinInput,1);
for binIdxIdx = 1:numPhaseBinInput
phaseSortedAmp(binIdxIdx,1) = mean(hfoPreselectedAmp(binIndex==binIdxIdx));
phaseSortedAmpSe(binIdxIdx,1) = std(hfoPreselectedAmp(binIndex==binIdxIdx))/sqrt(length(find(binIndex==binIdxIdx)));
end
% sort from 0 to 2pi
phaseSortOrder = [numPhaseBinInput/2+1:numPhaseBinInput 1:numPhaseBinInput/2];
EEG.pac.phaseProbability{chIdx,1} = phaseProbability(phaseSortOrder);
EEG.pac.phaseSortedAmp{chIdx,1} = phaseSortedAmp(phaseSortOrder);
EEG.pac.phaseSortedAmpSe{chIdx,1} = phaseSortedAmpSe(phaseSortOrder);
% statistics (chi-square test)
edges = linspace(min(phaseSortedAmp),max(phaseSortedAmp),numPhaseBinInput+1);
expectedCounts = repmat(numPhaseBinInput/(length(edges)-1), [1 length(edges)-1]);
[~,p] = chi2gof(phaseSortedAmp, 'edges', edges, 'expected', expectedCounts);
EEG.pac.uncorrected.ampChi2GofPval(chIdx,1) = p;
% statistics (kstest)
tmpCdf(:,1) = linspace(min(phaseSortedAmp), max(phaseSortedAmp), numPhaseBinInput);
tmpCdf(:,2) = linspace(0,1,numPhaseBinInput);
[~,p] = kstest(phaseSortedAmp, tmpCdf);
EEG.pac.uncorrected.ampKstestPval(chIdx,1) = p;
% keep record of max bin value
EEG.pac.histBinMax(chIdx,1) = max(phaseProbability);
end
end
%%%%%%%%%%%%%%%%%%
%%% Angle test %%%
%%%%%%%%%%%%%%%%%%
for chIdx = 1:EEG.nbchan
[EEG.pac.uncorrected.phaseWtsnWillPval(chIdx,1),~] = circ_wwtest(EEG.pac.phaseBinValues(chIdx,:), EEG.pac.angleMean);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Multiple comparisons %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Bonferroni
EEG.pac.Bonferroni.MIpval = EEG.pac.uncorrected.MIpval*EEG.nbchan;
EEG.pac.Bonferroni.phaseRayleighPval = EEG.pac.uncorrected.phaseRayleighPval*EEG.nbchan;
EEG.pac.Bonferroni.phaseOmniTestPval = EEG.pac.uncorrected.phaseOmniTestPval*EEG.nbchan;
EEG.pac.Bonferroni.phaseRaoSpacePval = EEG.pac.uncorrected.phaseRaoSpacePval*EEG.nbchan;
EEG.pac.Bonferroni.phaseWtsnWillPval = EEG.pac.uncorrected.phaseWtsnWillPval*EEG.nbchan;
EEG.pac.Bonferroni.ampChi2GofPval = EEG.pac.uncorrected.ampChi2GofPval*EEG.nbchan;
EEG.pac.Bonferroni.ampKstestPval = EEG.pac.uncorrected.ampKstestPval*EEG.nbchan;
% Bonferroni-Holm
EEG.pac.BonfHolm.MIpval = bonf_holm(EEG.pac.uncorrected.MIpval);
EEG.pac.BonfHolm.phaseRayleighPval = bonf_holm(EEG.pac.uncorrected.phaseRayleighPval);
EEG.pac.BonfHolm.phaseOmniTestPval = bonf_holm(EEG.pac.uncorrected.phaseOmniTestPval);
EEG.pac.BonfHolm.phaseRaoSpacePval = bonf_holm(EEG.pac.uncorrected.phaseRaoSpacePval);
EEG.pac.BonfHolm.phaseWtsnWillPval = bonf_holm(EEG.pac.uncorrected.phaseWtsnWillPval);
EEG.pac.BonfHolm.ampChi2GofPval = bonf_holm(EEG.pac.uncorrected.ampChi2GofPval);
EEG.pac.BonfHolm.ampKstestPval = bonf_holm(EEG.pac.uncorrected.ampKstestPval);
% False-discovery Rate
EEG.pac.FDR.MIpval = fdr(EEG.pac.uncorrected.MIpval);
EEG.pac.FDR.phaseRayleighPval = fdr(EEG.pac.uncorrected.phaseRayleighPval);
EEG.pac.FDR.phaseOmniTestPval = fdr(EEG.pac.uncorrected.phaseOmniTestPval);
EEG.pac.FDR.phaseRaoSpacePval = fdr(EEG.pac.uncorrected.phaseRaoSpacePval);
EEG.pac.FDR.phaseWtsnWillPval = fdr(EEG.pac.uncorrected.phaseWtsnWillPval);
EEG.pac.FDR.ampChi2GofPval = fdr(EEG.pac.uncorrected.ampChi2GofPval);
EEG.pac.FDR.ampKstestPval = fdr(EEG.pac.uncorrected.ampKstestPval);
% set default stats options
EEG.pac.phaseTestType = 1;
EEG.pac.multiCompType = 1;
% close waitber
close(waitBar)
%%%%%%%%%%%%%%%%%%%%%%%
%%% Display Pacman. %%%
%%%%%%%%%%%%%%%%%%%%%%%
disp(' %%%%%%%%% ');
disp(' %%%% %%%% ');
disp(' %%% %%% ');
disp(' %%% %%% %%% ');
disp(' %% %%% % % ');
disp(' % %%% %%%% ');
disp(' % %%%%% ');
disp(' % %%%% %%% %%% %%% ');
disp(' % %%% %%%%% %%%%% %%%%%');
disp(' % %%%% %%% %%% %%% ');
disp(' % %%%%% ');
disp(' %% %%%% ');
disp(' %%% % ');
disp(' %%% %%%% ');
disp(' %%%% %%%%% ');
disp(' %%%%%%%%% ');
disp(newline)
disp(' I computed them all! ');
disp(newline)
disp('PAC is computed and stored in EEG.pac')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
%%%%%%%%%%%%%%%%%%%%
%%% Subfunctions %%%
%%%%%%%%%%%%%%%%%%%%
function output = vararginReader(strings,varargin)
varargin = varargin{1,1};
for n = 1:length(varargin)
if strcmp(varargin{1,n}, strings)
output = varargin{1,n+1};
end
end