def find_best_voxels_epochs(stcs, rois, bands, job_num=1, verbose=True):
# Returns the indices of the voxels with maximum power per band in each ROI, in the format roi x band. stc is SourceEstimate, rois is a list of Labels, and bands is a list of length 2 vectors
fs = 1. / (stcs[0].times[1] - stcs[0].times[0])
best_voxels = np.empty([len(rois), len(bands)])
best_voxels[:] = np.NaN
for idr, roi in enumerate(rois):
if verbose:
print '\nLooking for best voxel in label ' + str(idr+1) + '/' + str(len(rois))
# in each label, we sum the power over all epochs
psd = 0
bad_label = False
for stc in stcs:
try:
roi_activity = stc.in_label(roi)
tmp, freqs = dsp.compute_psd(roi_activity.data, fs, n_jobs=job_num)
# psd is voxels x freqs
psd += tmp
except ValueError:
print "Oops! No vertices in this label!"
bad_label = True
# then find the voxel with biggest power in each band. If there are no voxels in the label, replace it by NAN
if bad_label:
best_voxels[idr, :] = np.NaN
else:
for idb, band in enumerate(bands):
index = np.logical_and(freqs >= band[0], freqs <= band[1])
# taking care of the weird case in which there's only one voxel in the label
freq_axis = len(psd.shape) - 1
band_psd = np.mean(psd[:, index], axis=freq_axis)
best_voxels[idr, idb] = band_psd.argmax()
return best_voxels
def find_best_voxels(stc, rois, bands, job_num=1):
# Returns the indices of the voxels with maximum power per band in each ROI, in the format roi x band. stc is SourceEstimate, rois is a list of Labels, and bands is a list of length 2 vectors
fs = 1. / stc.tstep
best_voxels = np.zeros([len(rois), len(bands)])
for idr, roi in enumerate(rois):
roi_activity = stc.in_label(roi)
psds, freqs = dsp.compute_psd(roi_activity.data, fs, n_jobs=job_num)
for idb, band in enumerate(bands):
index = np.logical_and(freqs >= band[0], freqs <= band[1])
band_psd = np.mean(psds[:, index], axis=1)
best_voxels[idr, idb] = band_psd.argmax()
return best_voxels
events = fg.get_good_events(markers[s], time, window_length)
epochs = mne.Epochs(raw,
events,
None,
0,
window_length,
preload=True,
baseline=None,
detrend=0,
picks=picks)
sols = [np.dot(weights, epoch) for epoch in epochs]
# in each ROI, choose the VE with highest power in that band
psd = 0
for sol in sols:
psd, freqs = dsp.compute_psd(sol, raw.info['sfreq'])
psd += psd
band_signal = []
for band in bands:
index = np.logical_and(freqs >= band[0], freqs <= band[1])
band_psd = np.mean(psd[:, index], axis=1)
# generate a list of (n_signals, n_times) epochs, where each signal represents one ROI
roi_series = []
for vox in roi_voxels:
roi_psd = band_psd[np.array(vox)]
# the process of normalization introduces NaNs, so we need to be careful here. If all voxels in the ROI are NaN, return NaNs
try:
max_vox = np.array(vox)[np.nanargmax(roi_psd)]
except:
max_vox = vox[0]
roi_series.append([sol[max_vox, :] for sol in sols])
# For each subject, we use the weight matrix to compute virtual electrodes
for s in subjs:
weights = np.load(home + '/sam%s/'%variant + s + '_weights.npz')['weights']
# normalize each beamformer weight by its vector norm
normed = np.linalg.norm(weights, axis=1)
weights = np.divide(weights.T,normed).T
raw_fname = fifs_dir + '/%s_rest_LP100_CP3_DS300_raw.fif'%s
raw = mne.fiff.Raw(raw_fname, preload=True, compensation=3)
picks = mne.fiff.pick_channels_regexp(raw.info['ch_names'], 'M..-*')
data, time = raw[picks, :]
events = fg.get_good_events(markers[s], time, window_length)
epochs = mne.Epochs(raw, events, None, 0, window_length, preload=True, baseline=None, detrend=0, picks=picks)
sols = [np.dot(weights, epoch) for epoch in epochs]
# average the power across epochs. Here we need to average instead of adding up because otherwise subjects with higher number of epochs would always have higher power
psd = 0
for sol in sols:
psd, freqs = dsp.compute_psd(sol, raw.info['sfreq'])
psd += psd
psd /= len(epochs)
# Convert PSDs to dB
psd = 10 * np.log10(psd)
band_pow = []
for band in bands:
index = np.logical_and(freqs >= band[0], freqs <= band[1])
vox_in_band_psd = np.mean(psd[:, index], axis=1)
# average the power within rois
roi_pow = [np.mean(vox_in_band_psd[np.array(vox)]) for vox in roi_voxels]
band_pow.append(roi_pow)
np.save(out_dir + s + '_roi_power', band_pow)
