participant = "ug"
file_name = "../working/%s_%s_raw.fif" % (participant, exp_id)
raw = mne.io.read_raw_fif(file_name, preload=True)
raw.pick_types(ecog=True)
# -- get common average referenced activity
nr_channels = len(raw.ch_names)
filters_car = np.zeros((nr_channels, nr_channels)) - 1 / nr_channels
for i in range(nr_channels):
filters_car[i, i] = 1
raw_car = ssd.apply_filters(raw, filters_car, prefix="car")
# -- compute SSD
bin_width = 1.2
peak = 9.46
filters, patterns = ssd.run_ssd(raw, peak, bin_width)
patterns_car = filters_car.T @ patterns
# -- get SSS referenced signal
nr_components = 3
raw_ssd = ssd.apply_filters(raw, filters[:, :nr_components])
# -- detect bursts with bycycle
picks = [np.argsort(np.abs(patterns_car[:, 0]))[-1]]
nr_trials = 15
raw_car.pick(picks)
osc_param = {
"amplitude_fraction_threshold": 0.5,
"amplitude_consistency_threshold": 0.5,
"period_consistency_threshold": 0.5,
raw.pick_types(ecog=True)
# -- select 3 sEEG leads
picks = range(19)
ch_names = ["ecog%i" % i for i in picks]
raw.pick_channels(ch_names)
raw2 = raw.copy()
ch_names_leads = ["ecog3", "ecog13", "ecog18"]
picks1 = mne.pick_channels(raw2.ch_names, ch_names_leads, ordered=True)
plot_seeg_3dbrain(raw)
# -- apply SSD
peak1 = 4.0
bin_width1 = 1.25
filters1, patterns = ssd.run_ssd(raw, peak1, bin_width1)
peak2 = 8.15
bin_width2 = 2.5
filters2, patterns2 = ssd.run_ssd(raw, peak2, bin_width2)
# combine top-SNR filters for each peak frequency into one matrix
filters1[:, 1] = filters2[:, 0]
patterns[:, 1] = patterns2[:, 0]
raw_ssd = ssd.apply_filters(raw, filters1[:, :2])
raw_ssd.filter(1, None)
picks = range(8)
ch_names = ["ecog%i" % i for i in picks]
lead1 = raw.copy().pick_channels(ch_names)
lead1.set_eeg_reference("average")