def test_eog_clean():
# test with exported csv
eog_signal = nk.data("eog_200hz")["vEOG"]
eog_cleaned = nk.eog_clean(eog_signal, sampling_rate=200)
assert eog_cleaned.size == eog_signal.size
# test with mne.io.Raw
raw = mne.io.read_raw_fif(mne.datasets.sample.data_path() +
"/MEG/sample/sample_audvis_raw.fif",
preload=True)
sampling_rate = raw.info["sfreq"]
eog_channels = nk.mne_channel_extract(raw, what="EOG", name="EOG")
eog_cleaned = nk.eog_clean(eog_channels,
sampling_rate,
method="agarwal2019")
assert eog_cleaned.size == eog_channels.size
# compare with mne filter
eog_cleaned_mne = nk.eog_clean(eog_channels, sampling_rate, method="mne")
mne_clean = mne.filter.filter_data(
eog_channels,
sfreq=sampling_rate,
l_freq=1,
h_freq=10,
filter_length="10s",
l_trans_bandwidth=0.5,
h_trans_bandwidth=0.5,
phase="zero-double",
fir_window="hann",
fir_design="firwin2",
verbose=False,
)
assert np.allclose((eog_cleaned_mne - mne_clean).mean(), 0)
def test_eog_findpeaks():
eog_signal = nk.data('eog_100hz')
eog_cleaned = nk.eog_clean(eog_signal, sampling_rate=100)
# Test with Neurokit
nk_peaks = nk.eog_findpeaks(eog_cleaned,
sampling_rate=100,
method="neurokit",
threshold=0.33,
show=False)
assert nk_peaks.size == 19
# Test with MNE
mne_peaks = nk.eog_findpeaks(eog_cleaned, method="mne")
assert mne_peaks.size == 44
# Test with brainstorm
brainstorm_peaks = nk.eog_findpeaks(eog_cleaned, method="brainstorm")
assert brainstorm_peaks.size == 28
blinker_peaks = nk.eog_findpeaks(eog_cleaned,
method="blinker",
sampling_rate=100)
assert blinker_peaks.size == 14
columns=["loc", "a", "scale", "Participant", "Task"])
params_scr = pd.DataFrame(
columns=["time_peak", "rise", "decay1", "decay2", "Participant", "Task"])
for i in range(4):
print("Task: " + str(i))
data = pd.read_csv("../../data/eogdb/eogdb_task" + str(i + 1) + ".csv")
for j, participant in enumerate(np.unique(data["Participant"])[1:3]):
print(" - " + str(j + 1))
segment = data[data["Participant"] == participant]
signal = segment["vEOG"]
cleaned = nk.eog_clean(signal, sampling_rate=200, method='neurokit')
blinks = nk.signal_findpeaks(cleaned, relative_height_min=1.5)["Peaks"]
events = nk.epochs_create(cleaned,
blinks,
sampling_rate=200,
epochs_start=-0.4,
epochs_end=0.6)
events = nk.epochs_to_array(events) # Convert to 2D array
x = np.linspace(0, 100, num=len(events))
p_gamma = np.full((events.shape[1], 3), np.nan)
p_bateman = np.full((events.shape[1], 3), np.nan)
p_scr = np.full((events.shape[1], 4), np.nan)