def eeg2fif(filename, interactive=False, outdir=None):
"""
Brain Products EEG format
"""
fdir, fname, fext = qc.parse_path_list(filename)
if outdir is None:
outdir = fdir
elif outdir[-1] != '/':
outdir += '/'
eegfile = fdir + fname + '.eeg'
matfile = fdir + fname + '.mat'
markerfile = fdir + fname + '.vmrk'
fiffile = outdir + fname + '.fif'
# convert to mat using MATLAB
if not os.path.exists(matfile):
print('Converting input to mat file')
run = "[sig,header]=sload('%s'); save('%s','sig','header');" % (eegfile, matfile)
qc.matlab(run)
if not os.path.exists(matfile):
qc.print_c('>> ERROR: mat file convertion error.', 'r')
sys.exit()
else:
print('MAT file already exists. Skipping conversion.')
# extract events
events = []
for l in open(markerfile):
if 'Stimulus,S' in l:
# event, sample_index= l.split(' ')[-1].split(',')[:2]
data = l.split(',')[1:3]
event = int(data[0][1:]) # ignore 'S'
sample_index = int(data[1])
events.append([sample_index, 0, event])
# load data and create fif header
mat = scipy.io.loadmat(matfile)
# headers= mat['header']
sample_rate = int(mat['header']['SampleRate'])
signals = mat['sig'].T # channels x samples
nch, t_len = signals.shape
ch_names = ['TRIGGER'] + ['CH%d' % (x + 1) for x in range(nch)]
ch_info = ['stim'] + ['eeg'] * (nch)
info = mne.create_info(ch_names, sample_rate, ch_info, montage='standard_1005')
# add event channel
eventch = np.zeros([1, signals.shape[1]])
signals = np.concatenate((eventch, signals), axis=0)
# create Raw object
raw = mne.io.RawArray(signals, info)
# add events
raw.add_events(events, 'TRIGGER')
# save and close
raw.save(fiffile, verbose=False, overwrite=True, fmt='double')
print('Saved to', fiffile)
def cva_features(datadir):
"""
(DEPRECATED FUNCTION)
"""
for fin in qc.get_file_list(datadir, fullpath=True):
if fin[-4:] != '.gdf': continue
fout = fin + '.cva'
if os.path.exists(fout):
logger.info('Skipping', fout)
continue
logger.info("cva_features('%s')" % fin)
qc.matlab("cva_features('%s')" % fin)
def convert2mat(filename, matfile):
"""
Convert to mat using MATLAB BioSig sload().
"""
basename = '.'.join(filename.split('.')[:-1])
# extension= filename.split('.')[-1]
matfile = basename + '.mat'
if not os.path.exists(matfile):
print('>> Converting input to mat file')
run = "[sig,header]=sload('%s'); save('%s.mat','sig','header');" % (filename, basename)
qc.matlab(run)
if not os.path.exists(matfile):
qc.print_c('>> ERROR: mat file convertion error.', 'r')
sys.exit()
def bdf2fif_matlab(filename, interactive=False, outdir=None):
"""
BioSemi bdf reader using BioSig toolbox of MATLAB.
"""
# convert to mat using MATLAB (MNE's edf reader has an offset bug)
fdir, fname, fext = qc.parse_path_list(filename)
if outdir is None:
outdir = fdir
elif outdir[-1] != '/':
outdir += '/'
fiffile = outdir + fname + '.fif'
matfile = outdir + fname + '.mat'
if not os.path.exists(matfile):
logger.info('Converting input to mat file')
run = "[sig,header]=sload('%s'); save('%s','sig','header');" % (
filename, matfile)
qc.matlab(run)
if not os.path.exists(matfile):
logger.error('mat file convertion error.')
sys.exit()
mat = scipy.io.loadmat(matfile)
os.remove(matfile)
sample_rate = int(mat['header']['SampleRate'])
nch = mat['sig'].shape[1]
# assume Biosemi always has the same number of channels
if nch == 73:
ch_names = CAP['BIOSEMI_64']
extra_ch = nch - len(CAP['BIOSEMI_64_INFO'])
extra_names = []
for ch in range(extra_ch):
extra_names.append('EXTRA%d' % ch)
ch_names = ch_names + extra_names
ch_info = CAP['BIOSEMI_64_INFO'] + ['misc'] * extra_ch
else:
logger.warning('Unrecognized number of channels (%d)' % nch)
logger.warning(
'The last channel will be assumed to be trigger. Press Enter to continue, or Ctrl+C to break.'
)
if interactive:
input()
# Set the trigger to be channel 0 because later we will move it to channel 0.
ch_names = ['TRIGGER'] + ['CH%d' % (x + 1) for x in range(nch - 1)]
ch_info = ['stim'] + ['eeg'] * (nch - 1)
signals_raw = mat['sig'].T # -> channels x samples
# Note: Biosig's sload() sometimes returns bogus event values so we use the following for events
bdf = mne.io.read_raw_edf(filename, preload=True)
events = mne.find_events(bdf,
stim_channel='TRIGGER',
shortest_event=1,
consecutive=True)
# signals_raw[-1][:]= bdf._data[-1][:] # overwrite with the correct event values
# Move the event channel to 0 (for consistency)
signals = np.concatenate(
(signals_raw[-1, :].reshape(1, -1), signals_raw[:-1, :]))
signals[0] *= 0 # init the event channel
info = mne.create_info(ch_names,
sample_rate,
ch_info,
montage='standard_1005')
# create Raw object
raw = mne.io.RawArray(signals, info)
# add events
raw.add_events(events, 'TRIGGER')
# save and close
raw.save(fiffile, verbose=False, overwrite=True, fmt='double')
logger.info('Saved to %s' % fiffile)
saveChannels2txt(outdir, ch_names)
