PCAanalysis3_pupil.m

% Run this script to perform PCA analyses.

%% LOAD PRE-PROCESSED DATA
load(dataFile);


%% INITIALISE PARAMETERS
lists
pcaParams


%% LOAD RECORDINGS AND EXTRACT LFP MEASURES
% LOOP THROUGH DB ENTRIES
fnsData = fieldnames(dataStruct.seriesData);
for dbCount = dbStart:numel(fnsData)
  if dbCount > dbEnd
    break
  end
  dbStruct = dataStruct.seriesData.(fnsData{dbCount});
  
  
  % TEST FOR EYE DATA
  if isfield(dbStruct, 'lfpPowerData') && ~isfield(dbStruct.lfpPowerData, 'areaInterpFilt')
    continue
  end
  
  
  % TEST FOR EXCEPTIONS
  [seriesName, animal] = seriesFromEntry(dbStruct.db(dbCount).entryName);
  if numel(seriesName) <= 14
    continue
  elseif numel(seriesName) == 15 &&...
      (strcmpi(seriesName(15), '3') || strcmpi(seriesName(15), '4') || strcmpi(seriesName(15), '5'))
    continue
  elseif numel(seriesName) >= 16
    continue
  end
  
  
  % TEST FOR RIPPLES
  ripplesExist = rippleTest(fnsData{dbCount}, fullSeries);
  
  
  % IDENTIFY AREA
  area = determineArea(seriesName);
  if ~area
    continue
  end
  
  
  % PICK UP LFP CHANNEL
  if ~(numel(seriesName) == 16)
    chOI = dbStruct.lfpPowerData.chOIDB;
    iCh = pickChan(area, animal, chOI);
    if isempty(iCh)
      continue
    end
  end
  
  
  % LOAD THE CONTENTS OF THE DB STRUCTURE VARIABLE
  params = dbStruct.conf.params;
  probe = dbStruct.conf.probe;
  if strcmpi(probe, 'Neuropixels')
    strInd = strfind(dbStruct.io.baseFilename, 'ap');
    baseFilename = [dbStruct.io.baseFilename(1:strInd-1) 'lf.bin'];
    if ~exist(baseFilename, 'file')
      baseFilename = [dbStruct.io.baseFilename(1:strInd-1) 'lf.dat'];
    end
  else
    baseFilename = [dbStruct.io.baseFilename '.dat'];
  end
  if ~exist(baseFilename, 'file')
    error(['The supplied LFP file does not exist. Please check the location folder: ' dbStruct.io.dataDir]);
  end
  chN = dbStruct.db(dbCount).chN;
  entryName = dbStruct.db(dbCount).entryName;
  
  
  % LOAD EXISTING LFP ANALYSIS DATA
  if strcmpi(seriesName(15), '1')
    neuralActivity = [];
    LFPs = [];
    PRs = [];
    areas = [];
    entryNames = {};
  end
  if isfield(dbStruct,'lfpPowerData')
    if (area == 5 || area == 10) && medianSubtracted % It's ok to use the existing ripple rate because it is based on median subtracted LFP
      rippleRate = dbStruct.lfpPowerData.rippleRate;
    else
      rippleRate = {};
      rippleRate{iCh} = []; %#ok<*SAGROW>
    end
    theta2deltaRatio = dbStruct.lfpPowerData.theta2deltaRatio;
    chOIDB = dbStruct.lfpPowerData.chOIDB;
    dssrLFPinit = dbStruct.lfpPowerData.dssrLFPinit;
    lfpTimes = dbStruct.lfpPowerData.lfpTimes;
    rippleDuration = dbStruct.lfpPowerData.rippleDuration;
    sdGaussian = dbStruct.lfpPowerData.sdGaussian;
    wGaussian = dbStruct.lfpPowerData.wGaussian;
  else
    error(['No LFP analysis data exist for series: ' fnsData{dbCount} '. Please run lfpLoad.m']);
  end
  
  
  % RESAMPLE EXISTING LFP DATA IF NECESSARY
  if params.srData ~= dssrLFPinit
    interpTimes = 1/params.srData:1/params.srData:lfpTimes(end);
    if (area == 5 || area == 10) && medianSubtracted
      rippleRate{iCh} = interp1(lfpTimes, rippleRate{iCh}, interpTimes)';
    end
    theta2deltaRatio{iCh} = interp1(lfpTimes, theta2deltaRatio{iCh}, interpTimes)';
  else
    interpTimes = lfpTimes;
  end
  
  
  % RECALCULATE RIPPLE RATE IF EXISTING ONE IS NOT BASED ON MEDIAN-SUBTRACTED LFP
  if (area == 5 || area == 10) && ~medianSubtracted && ripplesExist
    srRippleRate = 1000;
    if strcmpi(probe, 'Neuropixels')
      [LFPbandPower] = lfpFileLoad(baseFilename, 1, chunkSize, chN, chOIDB(iCh), LFPbands, params.srRecordingLFP, srRippleRate, true, deleteChans, false);
    else
      [LFPbandPower] = lfpFileLoad(baseFilename, 1, chunkSize, chN, chOIDB(iCh), LFPbands, params.srRecording, srRippleRate, true, deleteChans, false);
    end
    rippleBandPower{iCh} = LFPbandPower{1}(end,:);
    rippleRate{iCh} = rippleRateCalculator({rippleBandPower{iCh}}, rippleDuration, sdGaussian, wGaussian, chOIDB(iCh), bandNames, srRippleRate); %#ok<CCAT1>
    rippleRate{iCh} = rippleRate{iCh}{1};
    rippleRateTimes = 1/srRippleRate:1/srRippleRate:numel(rippleRate{iCh})/srRippleRate;
    rippleRate{iCh} = interp1(rippleRateTimes, rippleRate{iCh}, interpTimes)';
  elseif (area == 5 || area == 10) && ~ripplesExist
    rippleRate{iCh} = zeros(size(theta2deltaRatio{iCh}));
  end
  
  
  % LOAD, DOWN-SAMPLE LFP DATA, AND CALCULATE WAVELET TRANSFORMS
  if strcmpi(probe, 'Neuropixels')
    [LFPbandPower, ~, ~, LFP] = lfpFileLoad(...
      baseFilename, min(LFPbands{1}), chunkSize, chN, chOIDB, LFPbands, params.srRecordingLFP, params.srData, medianSubtracted, deleteChans, false);
  else
    [LFPbandPower, ~, ~, LFP] = lfpFileLoad(...
      baseFilename, min(LFPbands{1}), chunkSize, chN, chOIDB, LFPbands, params.srRecording, params.srData, medianSubtracted, deleteChans, false);
  end
  
  
  % LOAD SPIKING DATA
  PR = sum(dbStruct.popData.MUAsAll,1);
  
  
  % CONCATENATE DATA
  PR = torow(PR);
  LFP{iCh} = torow(LFP{iCh});
  rippleRate{iCh} = torow(rippleRate{iCh});
  theta2deltaRatio{iCh} = torow(theta2deltaRatio{iCh});
  dataDuration = max([size(neuralActivity,2) size(LFPbandPower{iCh},2) numel(rippleRate{iCh}) numel(theta2deltaRatio{iCh})...
    numel(LFP{iCh}) size(LFPs,2) numel(PR) size(PRs,2)]);
  if isempty(PR)
    PR = zeros(1,dataDuration);
  end
  if ~isempty(neuralActivity) && size(neuralActivity,2) < dataDuration
    zeroPad = zeros(size(neuralActivity,1), dataDuration - size(neuralActivity,2));
    neuralActivity = [neuralActivity zeroPad];
  end
  if size(LFPbandPower{iCh},2) < dataDuration
    zeroPad = zeros(size(LFPbandPower{iCh},1), dataDuration - size(LFPbandPower{iCh},2));
    LFPbandPower{iCh} = [LFPbandPower{iCh} zeroPad];
  end
  if (area == 5 || area == 10) && size(rippleRate{iCh},2) < dataDuration
    zeroPad = zeros(size(rippleRate{iCh},1), dataDuration - size(rippleRate{iCh},2));
    rippleRate{iCh} = [rippleRate{iCh} zeroPad];
  end
  if size(theta2deltaRatio{iCh},2) < dataDuration
    zeroPad = zeros(size(theta2deltaRatio{iCh},1), dataDuration - size(theta2deltaRatio{iCh},2));
    theta2deltaRatio{iCh} = [theta2deltaRatio{iCh} zeroPad];
  end
  if size(LFP{iCh},2) < dataDuration
    zeroPad = zeros(size(LFP{iCh},1), dataDuration - size(LFP{iCh},2));
    LFP{iCh} = [LFP{iCh} zeroPad];
  end
  if size(PR,2) < dataDuration
    zeroPad = zeros(size(PR,1), dataDuration - size(PR,2));
    PR = [PR zeroPad];
  end
  if (area == 5 || area == 10)
    neuralActivity = [neuralActivity; LFPbandPower{iCh}; rippleRate{iCh}; theta2deltaRatio{iCh}; LFP{iCh}; PR];
  else
    neuralActivity = [neuralActivity; LFPbandPower{iCh}; theta2deltaRatio{iCh}; LFP{iCh}; PR];
  end
  addedVectors = size(LFPbandPower{iCh},1) + ~isempty(rippleRate{iCh}) + size(theta2deltaRatio{iCh},1) + size(LFP{iCh},1) + ~isempty(PR);
  neuralActivity(isnan(neuralActivity)) = 0;
  neuralActivity(isnan(neuralActivity)) = 0;
  
  if size(LFPs,2) < dataDuration
    zeroPad = zeros(size(LFPs,1), dataDuration - size(LFPs,2));
    LFPs = [LFPs zeroPad];
  end
  LFPs = [LFPs; LFP{iCh}];
  
  if size(PRs,2) < dataDuration
    zeroPad = zeros(size(PRs,1), dataDuration - size(PRs,2));
    PRs = [PRs zeroPad];
  end
  PRs = [PRs; PR];
  
  areas = [areas; ones(addedVectors,1)*area];
  
  entryNames{numel(entryNames) + 1} = entryName;
  
  
  if dbCount < numel(fnsData)
    seriesNameNext = seriesFromEntry(dbStruct.db(dbCount+1).entryName);
  end
  if dbCount < numel(fnsData) - 1
    seriesNameNext2 = seriesFromEntry(dbStruct.db(dbCount+2).entryName);
  end
  if dbCount == numel(fnsData) || ~strcmpi(seriesName(1:14), seriesNameNext(1:14)) ||...
      (dbCount == numel(fnsData)-1 && (area == 5 || area == 10)) || ((area == 5 || area == 10) && ~strcmpi(seriesName(1:14), seriesNameNext2(1:14)))
    
    
    % LOAD AROUSAL MEASURES
    pupilArea = torow(dbStruct.lfpPowerData.areaInterpFilt);
    pupilAreaTimes = torow(dbStruct.lfpPowerData.areaInterpTimes);
    
    % Truncate time vectors
    [~, iPCs2pupilAreaStart] = min(abs(interpTimes - pupilAreaTimes(1)));
    [~, iPCs2pupilAreaEnd] = min(abs(interpTimes - pupilAreaTimes(end)));
    PCs2pupilAreaTimes = interpTimes(iPCs2pupilAreaStart:iPCs2pupilAreaEnd);
    
    % Resample arousal measures if necessary
    if params.srData ~= dssrLFPinit
      pupilArea = torow(interp1(pupilAreaTimes, pupilArea, PCs2pupilAreaTimes));
    end
    
    
    % ADD AROUSAL DATA
    neuralActivity = neuralActivity(:,iPCs2pupilAreaStart:iPCs2pupilAreaEnd);
    neuralActivity = [neuralActivity; pupilArea];
    neuralActivity(isnan(neuralActivity)) = 0;
    areas = [areas; 0];
    
    
    % SUBTRACT THE MEAN AND Z-SCORE THE DATA
    neuralActivity = neuralActivity - mean(neuralActivity,2);
    neuralActivity = zscore(neuralActivity')';
    
    
    % RUN PCA
    [pcaCoef, explained, PCs, nPCs, prob, individualVarExplained] = pcaGeneric(neuralActivity);
    
    % Calculate a number of variables needed to explain proportions of variance
    [explained25, explained50, explained75, explained95, explainedThird, explainedTwoThirds] = explainedProportions(explained);
    
    
    % CALCULATE EXPLAINED AREA FOR AROUSAL DATA
    pcaCoef_arousal = pcaCoef(end);
    explained_arousal = 100*mean(individualVarExplained(:,end), 2)';
      
    % Calculate a number of variables needed to explain proportions of variance for arousal data
    [explained25_arousal, explained50_arousal, explained75_arousal, explained95_arousal,...
      explainedThird_arousal, explainedTwoThirds_arousal] = explainedProportions(explained_arousal);
    
    
    % CALCULATE EXPLAINED VARIANCE FOR EACH AREA
    areasOI = unique(areas);
    areasOI(areasOI == 0) = [];
    for iArea = 1:numel(areasOI)
      dataOI = areas;
      dataOI(dataOI ~= areasOI(iArea)) = 0;
      dataOI = logical(dataOI);
      pcaCoef_area = pcaCoef(dataOI);
      explained_area = 100*mean(individualVarExplained(:,dataOI), 2)';
      
      % Calculate a number of variables needed to explain proportions of variance for the area
      [explained25_area, explained50_area, explained75_area, explained95_area,...
        explainedThird_area, explainedTwoThirds_area] = explainedProportions(explained_area);
      
      
      % CORRELATE PCs WITH LFP
      [rPCs2lfpPearson_area, pvalPCs2lfpPearson_area] = corrMulti(LFPs(iArea,iPCs2pupilAreaStart:iPCs2pupilAreaEnd), PCs, 'Pearson');
      [rPCs2lfpSpearman_area, pvalPCs2lfpSpearman_area] = corrMulti(LFPs(iArea,iPCs2pupilAreaStart:iPCs2pupilAreaEnd), PCs, 'Spearman');
    
    
      % CORRELATE PCs WITH PR
      [rPCs2prPearson_area, pvalPCs2prPearson_area] = corrMulti(PRs(iArea,iPCs2pupilAreaStart:iPCs2pupilAreaEnd), PCs, 'Pearson');
      [rPCs2prSpearman_area, pvalPCs2prSpearman_area] = corrMulti(PRs(iArea,iPCs2pupilAreaStart:iPCs2pupilAreaEnd), PCs, 'Spearman');
    
    
      % SAVE DATA
      % PCA data for all areas
      pcaData.pcaCoef = pcaCoef;
      pcaData.explained = explained;
      pcaData.nPCs = nPCs;
      pcaData.prob = prob;
      pcaData.explained25 = explained25;
      pcaData.explained50 = explained50;
      pcaData.explained75 = explained75;
      pcaData.explained95 = explained95;
      pcaData.explainedThird = explainedThird;
      pcaData.explainedTwoThirds = explainedTwoThirds;
      pcaData.explained_arousal = explained_arousal;
      pcaData.explained25_arousal = explained25_arousal;
      pcaData.explained50_arousal = explained50_arousal;
      pcaData.explained75_arousal = explained75_arousal;
      pcaData.explained95_arousal = explained95_arousal;
      pcaData.explainedThird_arousal = explainedThird_arousal;
      pcaData.explainedTwoThirds_arousal = explainedTwoThirds_arousal;
      
      % PCA data for the area of interest
      pcaData.pcaCoef_area = pcaCoef_area;
      pcaData.explained_area = explained_area;
      pcaData.explained25_area = explained25_area;
      pcaData.explained50_area = explained50_area;
      pcaData.explained75_area = explained75_area;
      pcaData.explained95_area = explained95_area;
      pcaData.explainedThird_area = explainedThird_area;
      pcaData.explainedTwoThirds_area = explainedTwoThirds_area;
      pcaData.rPCs2lfpPearson_area = rPCs2lfpPearson_area;
      pcaData.pvalPCs2lfpPearson_area = pvalPCs2lfpPearson_area;
      pcaData.rPCs2lfpSpearman_area = rPCs2lfpSpearman_area;
      pcaData.pvalPCs2lfpSpearman_area = pvalPCs2lfpSpearman_area;
      pcaData.rPCs2prPearson_area = rPCs2prPearson_area;
      pcaData.pvalPCs2prPearson_area = pvalPCs2prPearson_area;
      pcaData.rPCs2prSpearman_area = rPCs2prSpearman_area;
      pcaData.pvalPCs2prSpearman_area = pvalPCs2prSpearman_area;
      
      dataString = ['dataStruct.seriesData.' entryNames{iArea} '.pcaData3_pupil = pcaData;'];
      eval(dataString);
      if intermediateSaving
        save(dataFile,'dataStruct','-v7.3'); %#ok<*UNRCH>
      end
    end
  end
end

if ~intermediateSaving
  save(dataFile,'dataStruct','-v7.3');
end
clearvars -except dataFile dbStart dbEnd


function [LFPbandPower, wtSpectrogram, fSpectrogram, interpDat] = lfpFileLoad(...
  baseFilename, minF, chunkSize, chN, chOI, LFPbands, srRecording, srInterp, medianSubtracted, deleteChans, spectrogram)

% File loading parameters
fid = fopen(baseFilename, 'r');
d = dir(baseFilename);
nSampsTotal = d.bytes/chN/2;
nChunksTotal = ceil(nSampsTotal/chunkSize);

% Load the file
chunkInd = 1;
LFPbandPower = {};
wtSpectrogram = {};
interpDat = {};
while 1
  fprintf(1, 'chunk %d/%d\n', chunkInd, nChunksTotal);
  dat = fread(fid, [chN chunkSize], '*int16');
  
  % Subtract median and/or delete channels if necessary
  if medianSubtracted
    if deleteChans
      chans2include = ones(1,size(dat,1));
      chans2include(deleteChans) = zeros(1,numel(deleteChans));
      chm = zeros(size(dat,1),1);
      chm(logical(chans2include)) = median(dat(logical(chans2include),:),2);
      dat = bsxfun(@minus, dat, int16(chm)); % subtract median of each channel
      tm = int16(median(dat(logical(chans2include),:),1));
    else
      chm = median(dat,2);
      dat = bsxfun(@minus, dat, chm); % subtract median of each channel
      tm = median(dat,1);
    end
    dat = bsxfun(@minus, dat, tm); % subtract median of each time point
  end
  
  if ~isempty(dat)
    
    % Interpolate data
    originalTimes = 1/srRecording:1/srRecording:size(dat,2)/srRecording;
    interpTimes = 1/srInterp:1/srInterp:size(dat,2)/srRecording;
    interpLFP = interp1(originalTimes, double(dat'), interpTimes)';
    for iCh = 1:numel(chOI)
      
      % Calculate wavelet transforms
      fb = cwtfilterbank('SignalLength',size(interpLFP,2),'SamplingFrequency',srInterp,...
        'FrequencyLimits',[minF 200],'WaveletParameters',[3 16],'VoicesPerOctave',20);
      [wt,f] = cwt(interpLFP(chOI(iCh),:),'FilterBank',fb); % Continuous wavelet transform
      LFPbandPowerChunk = zeros(numel(LFPbands),size(wt,2));
      for iBand = 1:numel(LFPbands)
        for iLim = 1:2
          [~, LFPbandLimits{iBand}(iLim)] = min(abs(f-LFPbands{iBand}(iLim))); %#ok<*AGROW>
        end
        LFPbandPowerChunk(iBand,:) = sum(abs(wt(LFPbandLimits{iBand}(2):LFPbandLimits{iBand}(1),:)).^2,1);
      end
      if chunkInd == 1
        LFPbandPower{iCh} = LFPbandPowerChunk;
        interpDat{iCh} = interpLFP(chOI(iCh),:);
      else
        LFPbandPower{iCh} = [LFPbandPower{iCh} LFPbandPowerChunk]; %#ok<*AGROW>
        interpDat{iCh} = [interpDat{iCh} interpLFP(chOI(iCh),:)];
      end
      
      % Spectrogram
      if spectrogram
        fb = cwtfilterbank('SignalLength',size(interpLFP,2),'SamplingFrequency',srInterp,...
          'FrequencyLimits',[minF 200],'WaveletParameters',[3 16],'VoicesPerOctave',10);
        [wtSpectrogramChunk,fSpectrogram] = cwt(interpLFP(chOI(iCh),:),'FilterBank',fb); % Continuous wavelet transform
        %helperCWTTimeFreqPlot(wt,interpTimes,f,'surf','Spectrogram for CA1 channel','Seconds','Hz');
        %set(gca, 'YScale', 'log')
        if chunkInd == 1
          wtSpectrogram{iCh} = abs(wtSpectrogramChunk).^2;
        else
          wtSpectrogram{iCh} = [wtSpectrogram{iCh} abs(wtSpectrogramChunk).^2];
        end
      else
        fSpectrogram = [];
      end
      
    end
  else
    break
  end
  chunkInd = chunkInd+1;
end
end

function [rippleRate, meanRippleRate] = rippleRateCalculator(rippleBandPower, rippleDuration, sdGaussian, wGaussian, chOI, bandNames, srRippleRate)

for iCh = 1:numel(chOI)
  % CALCULATE RIPPLE RATE AS IN MCGINLEY ET AL. (2015)
  % Descriptive measures
  for iName = 1:numel(bandNames)
    if strcmpi(bandNames{iName}, 'ripples/uf')
      iRipples = iName;
      break
    end
  end
  %       figure; plot(timeInit, LFPbandPower{iCh}(iRipples,:)); hold on
  medianPower = median(rippleBandPower{iCh},'omitnan');
  stdPower = std(rippleBandPower{iCh},'omitnan');
  %       stdPowerTop = medianPower+1.96*stdPower;
  %       stdPowerBottom = medianPower-1.96*stdPower;
  stdPowerTop = medianPower+5*stdPower;
  stdPowerBottom = medianPower-5*stdPower; %#ok<*NASGU>
  %plot(medianPower*ones(1,size(LFPbandPower{iCh},2)))
  %plot(stdPowerTop*ones(1,size(LFPbandPower{iCh},2)))
  %plot(stdPowerBottom*ones(1,size(LFPbandPower{iCh},2)))
  
  % Detect initial ripples
  [ripplePowerPeaks, locations] = findpeaks(rippleBandPower{iCh});
  locations = locations(ripplePowerPeaks > stdPowerTop);
  totalRipplesInit = numel(locations);
  %       ripplePowerPeaks = ripplePowerPeaks(ripplePowerPeaks > stdPowerTop);
  %       plot(timeInit(locations),ripplePowerPeaks, 'r.', 'MarkerSize',5)
  
  % Mark ripple initiation and termination
  ripplePowerPeaksInit = zeros(1,size(rippleBandPower{iCh},2));
  ripplePowerPeaksInit(locations) = 1;
  ripplePowerPeaks = ripplePowerPeaksInit;
  for dtRipple = 1:round(rippleDuration/2)
    ripplePowerPeaks = ripplePowerPeaks + [ripplePowerPeaksInit(1+dtRipple:end) zeros(1,dtRipple)];
    ripplePowerPeaks = ripplePowerPeaks + [zeros(1,dtRipple) ripplePowerPeaksInit(1:end-dtRipple)];
  end
  ripplePowerPeaks(ripplePowerPeaks > 0) = 1;
  locations = 1:size(rippleBandPower{iCh},2);
  locations = locations(logical(ripplePowerPeaks));
  ripplePowerPeaksInit = ripplePowerPeaks;
  %       ripplePowerPeaks = ripplePowerPeaks(ripplePowerPeaks > 0);
  %       plot(timeInit(locations),ripplePowerPeaks, 'k.', 'MarkerSize',10); hold on
  
  % Total ripple event count
  ripplePowerPeaks2 = findpeaks(ripplePowerPeaksInit);
  totalRipples = numel(ripplePowerPeaks2);
  
  % Convolve with Gaussian
  w = gausswin(wGaussian*sdGaussian*srRippleRate, (wGaussian*sdGaussian*srRippleRate-1)/(2*sdGaussian*srRippleRate));
  w = w/sum(w);
  ripplePowerPeaks = filtfilt(w,1,ripplePowerPeaksInit);
  ripplePowerPeaks = ripplePowerPeaks/sum(ripplePowerPeaks)*sum(rippleBandPower{iCh});
  %plot(ripplePowerPeaks)
  %rippleRateInit = ripplePowerPeaks/mean(ripplePowerPeaks)*((totalRipplesInit/size(rippleBandPower{iCh},2))*srRippleRate); % Hz
  rippleRate{iCh} = ripplePowerPeaks/mean(ripplePowerPeaks)*((totalRipples/size(rippleBandPower{iCh},2))*srRippleRate); % Hz
  meanRippleRate{iCh} = mean(rippleRate{iCh});
  %timeInit = 1/srRippleRate:1/srRippleRate:size(rippleBandPower{iCh},2)/srRippleRate;
  %       plot(timeInit,rippleRate{iCh});
  
  % Down-sample again
  %interpTimesFinal = 1/dssrLFPfinal:1/dssrLFPfinal:size(rippleBandPower{iCh},2)/srRippleRate;
  %rippleRate{iCh} = torow(interp1(timeInit, rippleRate{iCh}, interpTimesFinal));
  %timeFinal = 1/dssrLFPfinal:1/dssrLFPfinal:size(rippleBandPower{iCh},2)/srRippleRate;
  %plot(timeFinal,rippleRate{iCh}); hold off
end
end

function [explained25, explained50, explained75, explained95, explainedThird, explainedTwoThirds] = explainedProportions(explained)

explainedCumulitive = cumsum(explained);
explainedCumulitive25 = explainedCumulitive - 25;
explained25 = find(explainedCumulitive25 >= 0);
explainedCumulitive50 = explainedCumulitive - 50;
explained50 = find(explainedCumulitive50 >= 0);
explainedCumulitive75 = explainedCumulitive - 75;
explained75 = find(explainedCumulitive75 >= 0);
explainedCumulitive95 = explainedCumulitive - 95;
explained95 = find(explainedCumulitive95 >= 0);
explainedCumulitiveThird = explainedCumulitive - 100/3;
explainedThird = find(explainedCumulitiveThird >= 0);
explainedCumulitiveTwoThirds = explainedCumulitive - 200/3;
explainedTwoThirds = find(explainedCumulitiveTwoThirds >= 0);
end