MinMaxNormalize

class torcheeg.transforms.MinMaxNormalize(min: ndarray | None | float = None, max: ndarray | None | float = None, axis: int | None = None, apply_to_baseline: bool = False)

Perform min-max normalization on the input data. This class allows the user to define the dimension of normalization and the used statistic.

from torcheeg import transforms

t = transforms.MinMaxNormalize(axis=0)
# normalize along the first dimension (electrode dimension)
t(eeg=np.random.randn(32, 128))['eeg'].shape
>>> (32, 128)

from torcheeg import transforms

t = transforms.MinMaxNormalize(axis=1)
# normalize along the second dimension (temproal dimension)
t(eeg=np.random.randn(32, 128))['eeg'].shape
>>> (32, 128)

Parameters:

• min (np.array, optional) – The minimum used in the normalization process, allowing the user to provide minimum statistics in np.ndarray format. When statistics are not provided, use the statistics of the current sample for normalization.

• max (np.array, optional) – The maximum used in the normalization process, allowing the user to provide maximum statistics in np.ndarray format. When statistics are not provided, use the statistics of the current sample for normalization.

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

• 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