autoSound Sound_to_Sound_BSS (Sound me, double startTime, double endTime, long ncovars, double lagStep, long maxNumberOfIterations, double tol, int method) {
try {
autoMixingMatrix him = Sound_to_MixingMatrix (me, startTime, endTime, ncovars, lagStep, maxNumberOfIterations, tol, method);
autoSound thee = Sound_and_MixingMatrix_unmix (me, him.peek());
return thee;
} catch (MelderError) {
Melder_throw (me, U": not separated.");
}
}
EEG EEG_to_EEG_bss (EEG me, double startTime, double endTime, long ncovars, double lagTime, const wchar_t *channelRanges, int whiteningMethod, int diagonalizerMethod, long maxNumberOfIterations, double tol) {
try {
// autowindow
if (startTime == endTime) {
startTime = my xmin; endTime = my xmax;
}
// don't allow times outside domain
if (startTime < my xmin) {
startTime = my xmin;
}
if (endTime > my xmax) {
endTime = my xmax;
}
long numberOfChannels;
autoNUMvector <long> channelNumbers (NUMstring_getElementsOfRanges (channelRanges, my d_numberOfChannels, & numberOfChannels, NULL, L"channel", true), 1);
autoEEG thee = my f_extractPart (startTime, endTime, true);
if (whiteningMethod != 0) {
bool fromCorrelation = whiteningMethod == 2;
autoPCA pca = EEG_to_PCA (thee.peek(), thy xmin, thy xmax, channelRanges, fromCorrelation);
autoEEG white = EEG_and_PCA_to_EEG_whiten (thee.peek(), pca.peek(), 0);
thee.reset (white.transfer());
}
autoMixingMatrix mm = Sound_to_MixingMatrix (thy d_sound, startTime, endTime, ncovars, lagTime, maxNumberOfIterations, tol, diagonalizerMethod);
autoEEG him = EEG_copyWithoutSound (me);
his d_sound = Sound_and_MixingMatrix_unmix (my d_sound, mm.peek());
EEG_setChannelNames_selected (him.peek(), L"ic", channelNumbers.peek(), numberOfChannels);
// Calculate the cross-correlations between eye-channels and the ic's
return him.transfer();
} catch (MelderError) {
Melder_throw (me, ": no independent components determined.");
}
}