def _interpolate_bads_eeg(inst):
    """Interpolate bad EEG channels

    Operates in place.

    Parameters
    ----------
    inst : mne.io.Raw, mne.Epochs or mne.Evoked
        The data to interpolate. Must be preloaded.
    """
    bads_idx = np.zeros(len(inst.ch_names), dtype=np.bool)
    goods_idx = np.zeros(len(inst.ch_names), dtype=np.bool)

    picks = pick_types(inst.info, meg=False, eeg=True, exclude=[])
    inst.info._check_consistency()
    bads_idx[picks] = [inst.ch_names[ch] in inst.info['bads'] for ch in picks]

    if len(picks) == 0 or len(bads_idx) != 0:
        return

    goods_idx[picks] = True
    goods_idx[bads_idx] = False

    pos = inst._get_channel_positions(picks)

    # Make sure only EEG are used
    bads_idx_pos = bads_idx[picks]
    goods_idx_pos = goods_idx[picks]

    pos_good = pos[goods_idx_pos]
    pos_bad = pos[bads_idx_pos]

    # test spherical fit
    radius, center = _fit_sphere(pos_good)
    distance = np.sqrt(np.sum((pos_good - center) ** 2, 1))
    distance = np.mean(distance / radius)
    if np.abs(1. - distance) > 0.1:
        warn('Your spherical fit is poor, interpolation results are '
             'likely to be inaccurate.')

    logger.info('Computing interpolation matrix from {0} sensor '
                'positions'.format(len(pos_good)))

    interpolation = _make_interpolation_matrix(pos_good, pos_bad)

    logger.info('Interpolating {0} sensors'.format(len(pos_bad)))
    _do_interp_dots(inst, interpolation, goods_idx, bads_idx)
dataset/ETHPy150Open mne-tools/mne-python/mne/channels/interpolation.py/_interpolate_bads_eeg/operator `==` -> `!=`