Downsample

class torcheeg.transforms.Downsample(num_points: int, axis: int | None = -1, apply_to_baseline: bool = False)

Downsample the EEG signal to a specified number of data points.

transform = Downsample(num_points=32, axis=-1)
# normalize along the first dimension (electrode dimension)
transform(eeg=np.random.randn(32, 128))['eeg'].shape
>>> (32, 32)

Parameters:

• num_points (int) – The number of data points after downsampling.

• axis (int, optional) – The dimension to normalize, when no dimension is specified, the entire data is normalized. (default: -1)

• apply_to_baseline – (bool): Whether to act on the baseline signal at the same time, if the baseline is passed in when calling. (default: False)

__call__(*args, eeg: ndarray, baseline: ndarray | None = None, **kwargs) → Dict[str, ndarray]

Parameters:

• eeg (np.ndarray) – The input EEG signals or features.

• baseline (np.ndarray, optional) – The corresponding baseline signal, if apply_to_baseline is set to True and baseline is passed, the baseline signal will be transformed with the same way as the experimental signal.

Returns:

The normalized results.

Return type:

np.ndarray