def main():
mne.utils.set_log_level('ERROR')
layout = load_layout(MEG)
print "Reading and processing data from files.."
filenames = sys.argv[1:]
new_components = []
for idx, fname in enumerate(filenames):
print "Handling " + str(idx+1) + ". subject"
raw = mne.io.read_raw_fif(fname, preload=True)
picks = mne.pick_types(raw.info, eeg=True, meg=True)
raw.drop_channels([ch_name for ix, ch_name in
enumerate(raw.info['ch_names']) if ix not in picks])
raw.drop_channels(raw.info['bads'])
fica = FourierICA(wsize=WSIZE, n_components=COMPONENTS, maxiter=7000,
sfreq=raw.info['sfreq'], hpass=BAND[0], lpass=BAND[1])
fica.fit(raw._data)
components = get_components(fica, raw.info, len(raw.times), layout)
handle = plot_components(components, layout)
input_ = int(raw_input("Which component to use: "))
if input_ == -1:
continue
new_components.append(components[input_ - 1])
import pdb; pdb.set_trace()
print "kissa"
# find triggers
try:
triggers = [event[0] for event in mne.find_events(raw) if event[1] == 0]
except:
print "No triggers found"
triggers = []
# drop bad and non-data channels
picks = mne.pick_types(raw.info, eeg=True, meg=True)
raw.drop_channels([name for idx, name in enumerate(raw.info['ch_names'])
if idx not in picks])
raw.drop_channels(raw.info['bads'])
# calculate fourier-ica
fica = FourierICA(wsize=wsize, n_components=n_components,
sfreq=raw.info['sfreq'], hpass=5, lpass=30,
maxiter=7000)
fica.fit(raw._data[:, raw.first_samp:raw.last_samp])
source_stft = fica.source_stft
freqs = fica.freqs
# plot components in sensor space on head topographies
fig_ = plt.figure()
for i in range(source_stft.shape[0]):
sensor_component = np.abs(fica.component_in_sensor_space(i))
tfr_ = mne.time_frequency.AverageTFR(raw.info, sensor_component,
range(sensor_component.shape[2]), freqs, 1)
axes = fig_.add_subplot(source_stft.shape[0], 1, i+1)
mne.viz.plot_tfr_topomap(tfr_, layout=layout, axes=axes, show=False)
raw.drop_channels(raw.info['bads'])
raw = mne.concatenate_raws(raws)
layout = load_layout(MEG=True)
wsize = 4096
sfreq = raw.info['sfreq']
states = []
events = mne.find_events(raw)
# calculate fourier-ica
fica = FourierICA(wsize=wsize, n_components=N_COMPONENTS,
sfreq=sfreq, hpass=4, lpass=30,
maxiter=7000)
fica.fit(raw._data)
source_stft = fica.source_stft
freqs = fica.freqs
# plot components in sensor space on head topographies
fig_ = plt.figure()
for i in range(source_stft.shape[0]):
sensor_component = np.abs(fica.component_in_sensor_space(i))
tfr_ = mne.time_frequency.AverageTFR(raw.info, sensor_component,
range(sensor_component.shape[2]), freqs, 1)
axes = fig_.add_subplot(source_stft.shape[0], 1, i+1)
mne.viz.plot_tfr_topomap(tfr_, layout=layout, axes=axes, show=False)