Function _simulate_erplike_mixed_data

mne/preprocessing/tests/test_xdawn.py:314–349

(n_epochs=100, n_channels=10)

def _simulate_erplike_mixed_data(n_epochs=100, n_channels=10):
    rng = np.random.RandomState(42)
    tmin, tmax = 0.0, 1.0
    sfreq = 100.0
    informative_ch_idx = 0

    y = rng.randint(0, 2, n_epochs)
    n_times = int((tmax - tmin) * sfreq)
    epoch_times = np.linspace(tmin, tmax, n_times)

    target_template = 1e-6 * (epoch_times - tmax) * np.sin(2 * np.pi * epoch_times)
    nontarget_template = (
        0.7e-6 * (epoch_times - tmax) * np.sin(2 * np.pi * (epoch_times - 0.1))
    )

    epoch_data = rng.randn(n_epochs, n_channels, n_times) * 5e-7
    epoch_data[y == 0, informative_ch_idx, :] += nontarget_template
    epoch_data[y == 1, informative_ch_idx, :] += target_template

    mixing_mat = _safe_svd(rng.randn(n_channels, n_channels))[0]
    mixed_epoch_data = np.dot(mixing_mat.T, epoch_data).transpose((1, 0, 2))

    events = np.zeros((n_epochs, 3), dtype=int)
    events[:, 0] = np.arange(0, n_epochs * n_times, n_times)
    events[:, 2] = y

    info = create_info(
        ch_names=[f"C{i:02d}" for i in range(n_channels)],
        ch_types=["eeg"] * n_channels,
        sfreq=sfreq,
    )
    epochs = EpochsArray(
        mixed_epoch_data, info, events, tmin=tmin, event_id={"nt": 0, "t": 1}
    )

    return epochs, mixing_mat


def test_xdawn_decoding_performance():