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"
def main():
mne.utils.set_log_level('ERROR')
if MEG:
layout = None
else:
layout = load_layout()
filenames = sys.argv[1:]
components = []
for fname in filenames:
print "Opening " + fname
part = pickle.load(open(fname, "rb"))
components.extend(part)
plot_components(components, layout)
import pdb; pdb.set_trace()
print "kissa"
def main():
mne.utils.set_log_level('ERROR')
layout = load_layout(MEG)
filenames = sys.argv[1:]
components = []
for fname in filenames:
print "Opening " + fname
part = pickle.load(open(fname, "rb"))
components.extend(part)
tses = []
for component in components:
tses.append(_get_tse(component))
epochs = []
for i in range(len(components)):
component_epochs = _get_epochs(components[i], tses[i])
# normalize
for j, epoch in enumerate(component_epochs):
component_epochs[j] = epoch / np.mean(tses[i])
epochs.extend(component_epochs)
print "Total: " + str(len(epochs)) + " epochs."
average = np.mean(epochs, axis=0)
# smoothen?
# average = np.convolve(average, np.ones((3,))/3, mode='valid')
step = (LIMITS[0]+LIMITS[1]) / float(len(average))
times = np.array(range(len(average)))
times = times * step - LIMITS[0] + step/2
plt.plot(times, average)
plt.show()
# crop away eyes closed resting
raws[0].crop(tmin=0, tmax=90)
# drop bad and non-data channels
for raw in raws:
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'])
raw = mne.concatenate_raws(raws)
if MEG:
layout = None
else:
layout = load_layout()
wsize = 4096
sfreq = raw.info['sfreq']
states = [(0, 85), (95, len(raw.times)/sfreq)]
# calculate fourier-ica
fica = FourierICA(wsize=wsize, n_components=N_COMPONENTS,
sfreq=sfreq, hpass=4, 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
mne.io.Raw(sys.argv[-1], preload=True, add_eeg_ref=False),
]
for raw in raws:
raw.add_proj([], remove_existing=True)
# drop bad and non-data channels
for raw in raws:
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'])
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
