faster.py

import numpy as np
from scipy.stats import zscore
import mne

from mne.fixes import nanmean
from mne.utils import logger

def detect_bad_channels(raw, picks=None, thresh=3):
    """Detect bad channels
    """
    if picks is None:
        picks = mne.pick_types(raw.info, meg=False, eeg=True, exclude=[])
    x, _ = raw[picks]
    ch_names = [raw.ch_names[k] for k in picks]
    corrx = nanmean(
        np.ma.masked_array(np.corrcoef(x), np.identity(len(x), dtype=bool)),
        axis=0)
    bad_corr = np.where(np.abs(zscore(corrx)) > thresh)[0]
    logger.info('Bad by correlation:\n' +
                '\n\t'.join([ch_names[k] for k in bad_corr]))
    bad_var = np.where(np.abs(zscore(np.var(x, axis=1))) > thresh)[0]
    logger.info('Bad by variance:\n'
                '\n\t'.join([ch_names[k] for k in bad_var]))
    bad_idx = np.unique(np.concatenate((bad_corr, bad_var)))

    return [ch_names[k] for k in bad_idx]