To2d¶
- class torcheeg.transforms.To2d(apply_to_baseline: bool = False)[source][source]¶
Taking the electrode index as the row index and the temporal index as the column index, a two-dimensional EEG signal representation with the size of [number of electrodes, number of data points] is formed. While PyTorch performs convolution on the 2d tensor, an additional channel dimension is required, thus we append an additional dimension.
transform = To2d() transform(eeg=np.random.randn(32, 128))['eeg'].shape >>> (1, 32, 128)
- __call__(*args, eeg: ndarray, baseline: Optional[ndarray] = None, **kwargs) Dict[str, ndarray][source][source]¶
- Parameters
eeg (np.ndarray) – The input EEG signals in shape of [number of electrodes, number of data points].
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 transformed results with the shape of [1, number of electrodes, number of data points].
- Return type
np.ndarray
