def _least_square_evoked(data, events, event_id, tmin, tmax, sfreq):
    """Least square estimation of evoked response from data.

    Parameters
    ----------
    data : ndarray, shape (n_channels, n_times)
        The data to estimates evoked
    events : ndarray, shape (n_events, 3)
        The events typically returned by the read_events function.
        If some events don't match the events of interest as specified
        by event_id, they will be ignored.
    event_id : dict
        The id of the events to consider
    tmin : float
        Start time before event.
    tmax : float
        End time after event.
    sfreq : float
        Sampling frequency.

    Returns
    -------
    evokeds_data : dict of ndarray
        A dict of evoked data for each event type in event_id.
    toeplitz : dict of ndarray
        A dict of toeplitz matrix for each event type in event_id.
    """
    nmin = int(tmin * sfreq)
    nmax = int(tmax * sfreq)

    window = nmin - nmax
    n_samples = data.shape[1]
    toeplitz_mat = dict()
    full_toep = list()
    for eid in event_id:
        # select events by type
        ix_ev = events[:, -1] == event_id[eid]

        # build toeplitz matrix
        trig = np.zeros((n_samples, 1))
        ix_trig = (events[ix_ev, 0]) + nmin
        trig[ix_trig] = 1
        toep_mat = linalg.toeplitz(trig[0:window], trig)
        toeplitz_mat[eid] = toep_mat
        full_toep.append(toep_mat)

    # Concatenate toeplitz
    full_toep = np.concatenate(full_toep)

    # least square estimation
    predictor = np.dot(linalg.pinv(np.dot(full_toep, full_toep.T)), full_toep)
    all_evokeds = np.dot(predictor, data.T)
    all_evokeds = np.vsplit(all_evokeds, len(event_id))

    # parse evoked response
    evoked_data = dict()
    for idx, eid in enumerate(event_id):
        evoked_data[eid] = all_evokeds[idx].T

    return evoked_data, toeplitz_mat
dataset/ETHPy150Open mne-tools/mne-python/mne/preprocessing/xdawn.py/_least_square_evoked/swapped operands of `-`