Note

Go to the end to download the full example code.

Interpolate Channel.

Channel interpolation example.

import os

import matplotlib.pyplot as plt
import mne
import numpy as np

from eegrasp import EEGrasp

current_dir = os.getcwd()
os.chdir(os.path.dirname(current_dir))
data_dir = './datasets'
#os.makedirs(data_dir, exist_ok=True)
#os.environ['MNE_EEGBCI_PATH'] = data_dir

subjects = np.arange(1, 10)
runs = [4, 8, 12]

# Download eegbci dataset through MNE
# Comment the following line if already downloaded

raw_fnames = [
    mne.datasets.eegbci.load_data(s, runs, path=data_dir, update_path=True)
    for s in subjects
]
raw_fnames = np.reshape(raw_fnames, -1)
raws = [mne.io.read_raw_edf(f, preload=True) for f in raw_fnames]
raw = mne.concatenate_raws(raws)
mne.datasets.eegbci.standardize(raw)
raw.annotations.rename(dict(T1='left', T2='right'))

montage = mne.channels.make_standard_montage('standard_1005')
raw.set_montage(montage)
eeg_pos = np.array(
    [pos for _, pos in raw.get_montage().get_positions()['ch_pos'].items()])
ch_names = montage.ch_names

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Attempting to create new mne-python configuration file:
/home/docs/.mne/mne-python.json
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Downloading EEGBCI data
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L_FREQ = 1  # Hz
H_FREQ = 30  # Hz
raw.filter(L_FREQ, H_FREQ, fir_design='firwin', skip_by_annotation='edge')
raw, ref_data = mne.set_eeg_reference(raw)

events, events_id = mne.events_from_annotations(raw)

Filtering raw data in 27 contiguous segments
Setting up band-pass filter from 1 - 30 Hz

FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal bandpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 1.00
- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)
- Upper passband edge: 30.00 Hz
- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
- Filter length: 529 samples (3.306 s)

[Parallel(n_jobs=1)]: Done  17 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done  64 out of  64 | elapsed:    0.0s finished
EEG channel type selected for re-referencing
Applying average reference.
Applying a custom ('EEG',) reference.
Used Annotations descriptions: [np.str_('T0'), np.str_('left'), np.str_('right')]

Exclude bad channels

TMIN, TMAX = -1.0, 3.0
picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
                       exclude='bads')
epochs = mne.Epochs(raw, events, events_id, picks=picks, tmin=TMIN, tmax=TMAX,
                    baseline=(-1, 0), detrend=1)

data = epochs.average().get_data()

Not setting metadata
810 matching events found
Applying baseline correction (mode: mean)
0 projection items activated

  1. Define index of the missing channel

MISSING_IDX = 5
lost_ch = data[MISSING_IDX, :].copy()
data[MISSING_IDX, :] = np.nan  # delete channel info from array
# 2. Initialize instance of EEGrasp
eegsp = EEGrasp(data, eeg_pos, ch_names)
# 3. Compute the electrode distance matrix
dist_mat = eegsp.compute_distance(normalize=True)
# 4. Compute the graph weights and make graph structure
graph = eegsp.compute_graph(epsilon=0.5, sigma=0.1)
W = eegsp.graph_weights
# 5. Interpolate missing channel
interpolated = eegsp.interpolate_channel(missing_idx=MISSING_IDX, graph=graph)

fs = epochs.info['sfreq']
t = np.arange(0, len(lost_ch) / fs, 1 / fs)
plt.close('all')
plt.figure(figsize=(6.5, 3.5))
plt.plot(t, lost_ch * 1e6, label='Original')
plt.plot(t, interpolated[MISSING_IDX] * 1e6, label='Interpolated')
plt.legend()
plt.xlabel('tiempo [s]')
plt.ylabel('voltaje [uV]')
plt.title('Imputación de datos faltantes de un electrodo')
plt.tight_layout()
plt.show()
Imputación de datos faltantes de un electrodo

Total running time of the script: (5 minutes 18.805 seconds)

Estimated memory usage: 1230 MB

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