Sound EEG_to_Sound_modulated (EEG me, double baseFrequency, double channelBandwidth, const wchar_t *channelRanges) {
try {
long numberOfChannels;
autoNUMvector <long> channelNumbers (NUMstring_getElementsOfRanges (channelRanges, my d_numberOfChannels, & numberOfChannels, NULL, L"channel", true), 1);
double maxFreq = baseFrequency + my d_numberOfChannels * channelBandwidth;
double samplingFrequency = 2 * maxFreq;
samplingFrequency = samplingFrequency < 44100 ? 44100 : samplingFrequency;
autoSound thee = Sound_createSimple (1, my xmax - my xmin, samplingFrequency);
for (long i = 1; i <= numberOfChannels; i++) {
long ichannel = channelNumbers[i];
double fbase = baseFrequency;// + (ichannel - 1) * channelBandwidth;
autoSound si = Sound_extractChannel (my d_sound, ichannel);
autoSpectrum spi = Sound_to_Spectrum (si.peek(), 1);
Spectrum_passHannBand (spi.peek(), 0.5, channelBandwidth - 0.5, 0.5);
autoSpectrum spi_shifted = Spectrum_shiftFrequencies (spi.peek(), fbase, samplingFrequency / 2, 30);
autoSound resampled = Spectrum_to_Sound (spi_shifted.peek());
long nx = resampled -> nx < thy nx ? resampled -> nx : thy nx;
for (long j = 1; j <= nx; j++) {
thy z[1][j] += resampled -> z[1][j];
}
}
Vector_scale (thee.peek(), 0.99);
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, ": no playable sound created.");
}
}
static long *EEG_channelNames_to_channelNumbers (EEG me, wchar_t **channelNames, long numberOfChannelNames) {
try {
autoNUMvector<long> channelNumbers (1, numberOfChannelNames);
for (long i = 1; i <= numberOfChannelNames; i++) {
for (long j = 1; j <= my d_numberOfChannels; j++) {
if (Melder_wcsequ (channelNames[i], my d_channelNames[j])) {
channelNumbers[i] = j;
}
}
if (channelNumbers[i] == 0) {
Melder_throw ("Channel name \"", channelNames[i], "\" not found.");
}
}
return channelNumbers.transfer();
} catch (MelderError) {
Melder_throw (me, ": channelNames not found.");
}
}
EEG EEG_and_PCA_to_EEG_principalComponents (EEG me, PCA thee, long numberOfComponents) {
try {
if (numberOfComponents <= 0 || numberOfComponents > thy numberOfEigenvalues) {
numberOfComponents = thy numberOfEigenvalues;
}
numberOfComponents = numberOfComponents > my d_numberOfChannels ? my d_numberOfChannels : numberOfComponents;
autoNUMvector<long> channelNumbers (EEG_channelNames_to_channelNumbers (me, thy labels, thy dimension), 1);
autoEEG him = (EEG) Data_copy (me);
autoSound pc = Sound_and_PCA_to_Sound_pc_selectedChannels (my d_sound, thee, numberOfComponents, channelNumbers.peek(), thy dimension);
for (long i = 1; i <= thy dimension; i++) {
long ichannel = channelNumbers[i];
NUMvector_copyElements<double> (pc -> z[i], his d_sound -> z[ichannel], 1, his d_sound -> nx);
}
EEG_setChannelNames_selected (him.peek(), L"pc", channelNumbers.peek(), thy dimension);
return him.transfer();
} catch (MelderError) {
Melder_throw (me, ": not projected.");
}
}
EEG EEG_and_PCA_to_EEG_whiten (EEG me, PCA thee, long numberOfComponents) {
try {
if (numberOfComponents <= 0 || numberOfComponents > thy numberOfEigenvalues) {
numberOfComponents = thy numberOfEigenvalues;
}
numberOfComponents = numberOfComponents > my numberOfChannels ? my numberOfChannels : numberOfComponents;
autoNUMvector<long> channelNumbers (EEG_channelNames_to_channelNumbers (me, thy labels, thy dimension), 1);
autoEEG him = (EEG) Data_copy (me);
autoSound white = Sound_and_PCA_whitenSelectedChannels (my sound, thee, numberOfComponents, channelNumbers.peek(), thy dimension);
for (long i = 1; i <= thy dimension; i++) {
long ichannel = channelNumbers[i];
NUMvector_copyElements<double> (white -> z[i], his sound -> z[ichannel], 1, his sound -> nx);
}
EEG_setChannelNames_selected (him.peek(), U"wh", channelNumbers.peek(), thy dimension);
return him.transfer();
} catch(MelderError) {
Melder_throw (me, U": not whitened with ", thee);
}
}
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.");
}
}
