def test_read_ica_eeglab():
"""Test read_ica_eeglab function."""
fname = op.join(test_base_dir, "EEGLAB", "test_raw.set")
fname_cleaned_matlab = op.join(test_base_dir, "EEGLAB",
"test_raw.cleaned.set")
raw = read_raw_eeglab(fname, preload=True)
raw_eeg = _check_load_mat(fname, None)
raw_cleaned_matlab = read_raw_eeglab(fname_cleaned_matlab,
preload=True)
mark_to_remove = ["manual"]
comp_info = raw_eeg.marks["comp_info"]
if len(comp_info["flags"].shape) > 1:
ind_comp_to_drop = [np.where(flags)[0]
for flags, label in zip(comp_info["flags"],
comp_info["label"])
if label in mark_to_remove]
ind_comp_to_drop = np.unique(np.concatenate(ind_comp_to_drop))
else:
ind_comp_to_drop = np.where(comp_info["flags"])[0]
ica = read_ica_eeglab(fname)
_assert_ica_attributes(ica)
raw_cleaned = ica.apply(raw.copy(), exclude=ind_comp_to_drop)
assert_allclose(raw_cleaned_matlab.get_data(), raw_cleaned.get_data(),
rtol=1e-05, atol=1e-08)
def add_bad_segment_annot(raw, file_name, mark_to_remove=("manual", )):
raw_eeg = _check_load_mat(file_name, None)
time_info = raw_eeg.marks["time_info"]
if len(np.array(time_info["flags"]).shape) > 1:
ind_time_to_drop = np.unique(
np.concatenate([
np.where(flags)[0]
for flags, label in zip(time_info["flags"], time_info["label"])
if label in mark_to_remove
]))
else:
ind_time_to_drop = np.where(time_info["flags"])[0]
ind_starts = np.concatenate(
[[ind_time_to_drop[0]],
ind_time_to_drop[np.where(np.diff(ind_time_to_drop) > 1)[0] + 1]])
ind_ends = np.concatenate([
ind_time_to_drop[np.where(np.diff(ind_time_to_drop) > 1)[0]],
[ind_time_to_drop[-1]]
])
durations = (ind_ends + 1 - ind_starts) / raw.info["sfreq"]
onsets = ind_starts / raw.info["sfreq"]
for onset, duration in zip(onsets, durations):
raw.annotations.append(onset, duration, description="bad_lossless_qc")
def remove_rejected_ica_components(raw, file_name, inplace=True):
raw_eeg = _check_load_mat(file_name, None)
mark_to_remove = ["manual"]
comp_info = raw_eeg.marks["comp_info"]
if len(np.array(comp_info["flags"]).shape) > 1:
ind_comp_to_drop = np.unique(np.concatenate([np.where(flags)[0] for flags, label in zip(comp_info["flags"],
comp_info["label"])
if label in mark_to_remove]))
else:
ind_comp_to_drop = np.where(comp_info["flags"])[0]
if inplace:
read_ica_eeglab(file_name).apply(raw, exclude=ind_comp_to_drop)
else:
read_ica_eeglab(file_name).apply(raw.copy(), exclude=ind_comp_to_drop)
def mark_bad_channels(raw, file_name, mark_to_remove=("manual", "rank")):
raw_eeg = _check_load_mat(file_name, None)
info, _, _ = _get_info(raw_eeg)
print("############ file_name", file_name, type(file_name))
print("############ raw_eeg", raw_eeg.keys(), type(raw_eeg))
chan_info = raw_eeg.marks["chan_info"]
mat_chans = np.array(info["ch_names"])
assert (len(chan_info["flags"][0]) == len(mat_chans))
if len(np.array(chan_info["flags"]).shape) > 1:
ind_chan_to_drop = np.unique(np.concatenate([np.where(flags)[0] for flags, label in zip(chan_info["flags"],
chan_info["label"])
if label in mark_to_remove]))
else:
ind_chan_to_drop = np.where(chan_info["flags"])[0]
bad_chan = [chan for chan in mat_chans[ind_chan_to_drop]]
raw.info['bads'].extend(bad_chan)