Update hmrR_tCCA.m

% SYNTAX:

% [Aaux, rcMap] = hmrR_tCCA(data, aux, probe, runIdx, flagtCCA, flagICRegressors, tCCAparams, tCCAaux_inx, rhoSD_ssThresh, runIdxResting)

% [Aaux, rcMap] = hmrR_tCCA(data, aux, probe, runIdx, flagtCCA, flagICRegressors, tCCAparams, tCCAaux_inx, rhoSD_ssThresh, runIdxResting, tResting)

%

% UI NAME:

% hmrR_tCCA

% probe - SNIRF probe type containing source/detector geometry data (See SNIRF Spec for more details)

% runIdx - the index of the run in a multi-run session

% flagtCCA - turns the function on / off

% flagICRegressors - selects regressor generation strategy:

%            (0/false): common set of regressors for all fNIRS channels

%            (default), (1/true): one individual regressor per fNIRS channel

% flagICRegressors - selects regressor generation strategy. (0/false) chooses a common set 

%           of regressors for all fNIRS channels. (1/true) generates one

%           individual regressor per fNIRS channel.

% tCCAparams - These are the parameters for tCCA function

%            1 - 1 timelag [s]

%            2 - 2 step size [samples]

%          Follows the static estimate procedure described in Gagnon et al (2011).

%          NeuroImage, 56(3), 1362?1371.

% runIdxResting - resting state run index

% tResting - start/stop time [s] to use from resting data for tCCA training

%

% OUTPUTS:

% Aaux - A matrix of auxilliary regressors (#time points x #Aux regressors)

%           Only relevant when flagICRegressors = 1, otherwise rcMap is empty.

%

% USAGE OPTIONS:

% hmrR_tCCA_Concentration_Data: [Aaux, rcMap] = hmrR_tCCA(dc, aux, probe, iRun, flagtCCA, flagICRegressors, tCCAparams, tCCAaux_inx, rhoSD_ssThresh, runIdxResting)

% hmrR_tCCA_OD_Data: [Aaux, rcMap] = hmrR_tCCA(dod, aux, probe, iRun, flagtCCA, flagICRegressors, tCCAparams, tCCAaux_inx, rhoSD_ssThresh, runIdxResting)

% hmrR_tCCA_Concentration_Data: [Aaux, rcMap] = hmrR_tCCA(dc, aux, probe, iRun, flagtCCA, flagICRegressors, tCCAparams, tCCAaux_inx, rhoSD_ssThresh, runIdxResting, tResting)

% hmrR_tCCA_OD_Data: [Aaux, rcMap] = hmrR_tCCA(dod, aux, probe, iRun, flagtCCA, flagICRegressors, tCCAparams, tCCAaux_inx, rhoSD_ssThresh, runIdxResting, tResting)

%

%

% PARAMETERS:

% tCCAaux_inx: [1 2 3 4 5 6 7 8]

% rhoSD_ssThresh: 15.0

% runIdxResting: 1

% tResting: [30 210]

%

function [Aaux, rcMap] = hmrR_tCCA(data, aux, probe, runIdx, flagtCCA, flagICRegressors, tCCAparams, tCCAaux_inx, rhoSD_ssThresh, runIdxResting)

function [Aaux, rcMap] = hmrR_tCCA(data, aux, probe, runIdx, flagtCCA, flagICRegressors, tCCAparams, tCCAaux_inx, rhoSD_ssThresh, runIdxResting, tResting)

%% COMMENTS/THOUGHTS/QUESTIONS ALEX

% 2) Output canonical correlation coefficients as quality metric?

% correlation used outside of the rtcca function

ctr = tCCAparams(3);

%checksum for tCCA filter file

chksm = int16(7*flagICRegressors + sum(11*tCCAparams(:)) + sum(13*tCCAaux_inx(:)) + 17*rhoSD_ssThresh + 19*runIdxResting + sum(23*tResting(:)));

if flagtCCA

    SrcPos  = probe.GetSrcPos();

        param.NumOfEmb = ceil(timelag*fq / tCCAparams(2));

        %% DO THE TCCA WORK

        filterFilename = sprintf('./tCCAfilter_%d.txt', iBlk);

        tCCAexists = isfile(sprintf('./tCCAfilter_%d.txt', iBlk));

        filterFilename = sprintf('./tCCAfilter_%d_%d.txt', iBlk, chksm);

        tCCAexists = isfile(sprintf('./tCCAfilter_%d_%d.txt', iBlk, chksm));

        isTrainingRun = runIdxResting == runIdx;

        if ~tCCAexists && isTrainingRun

            dotCCA = 'train';

                %       regressor. Otherwise, columns correspond to common regressors

                %       for all channels in descending order ranked by canonical correlation coefficient

                %       number of embeddings

                % cut data to selected time window before training

                % warning if resting segment is shorter than 1min

                if diff(tResting)<60 

                    msgbox('WARNING: tCCA training with less than 60s of data is not recommended!')

                end

                cstrt = find(t>=tResting(1));

                cstp = find(t<=tResting(2));

                cIdx = [cstrt(1):cstp(end)];

                % cut

                d_long = d_long(cIdx,:);

                AUX = AUX(cIdx,:);

                if flagICRegressors %% learn channel specific filters and regressors

                    for cc = 1:size(d_long,2)

                        %% perform tCCA with shrinkage for each fNIRS channel

                Aaux = [];

                rcMap = [];

                %% put a user warning

                %% if runIdxResting ~=1 put out warning to run the session again. 

                if runIdx == runIdxResting-1

                    msgbox('tCCA raining (resting run) is not the first run. Other runs skipped. Please re-run the session for complete results.')

                elseif runIdx >= runIdxResting

                    msgbox('no tCCA filter trained. Please run training resting run or whole session first.')

                end

        end

    end