Concatenate

class torcheeg.transforms.Concatenate(transforms: Sequence[Callable], axis: int = -1, apply_to_baseline: bool = False)

Merge the calculation results of multiple transforms, which are used when feature fusion is required.

from torcheeg import transforms

t = transforms.Concatenate([
    transforms.BandDifferentialEntropy(),
    transforms.BandMeanAbsoluteDeviation()
])
t(eeg=np.random.randn(32, 128))['eeg'].shape
>>> (32, 8)

Parameters:

• transforms (list, tuple) – a sequence of transforms.

• axis (int) – The axis along which the arrays will be joined. If axis is None, arrays are flattened before use (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, **kwargs) → Dict[str, ndarray]

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 combined results of multiple transforms.

Return type:

np.ndarray