BandDifferentialEntropy

class torcheeg.transforms.BandDifferentialEntropy(sampling_rate: int = 128, order: int = 5, band_dict: Dict[str, Tuple[int, int]] = {'alpha': [8, 14], 'beta': [14, 31], 'gamma': [31, 49], 'theta': [4, 8]}, apply_to_baseline: bool = False)

A transform method for calculating the differential entropy of EEG signals in several sub-bands with EEG signals as input. It is a widely accepted differential entropy calculation method by the community, which is often applied to the DEAP and DREAMER datasets. It is relatively easy to understand and has a smaller scale and more gradual changes than the BandDifferentialEntropyV1 calculated based on average power spectral density.

• Related Paper: Fdez J, Guttenberg N, Witkowski O, et al. Cross-subject EEG-based emotion recognition through neural networks with stratified normalization[J]. Frontiers in neuroscience, 2021, 15: 626277.

• Related Project: https://github.com/javiferfer/cross-subject-eeg-emotion-recognition-through-nn/

• Related Paper: Li D, Xie L, Chai B, et al. Spatial-frequency convolutional self-attention network for EEG emotion recognition[J]. Applied Soft Computing, 2022, 122: 108740.

• Related Project: https://github.com/qeebeast7/SFCSAN/

In most cases, choosing BandDifferentialEntropy and BandDifferentialEntropyV1 does not make much difference. If you have other comments, please feel free to pull request.

from torcheeg import transforms

t = transforms.BandDifferentialEntropy()
t(eeg=np.random.randn(32, 128))['eeg'].shape
>>> (32, 4)

Parameters:

• sampling_rate (int) – The original sampling rate of EEG signals in Hz. (default: 128)

• order (int) – The order of the filter. (default: 5)

• band_dict – (dict): Band name and the critical sampling rate or frequencies. By default, the differential entropy of the four sub-bands, theta, alpha, beta and gamma, is calculated. (default: {...})

• 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 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 differential entropy of several sub-bands for all electrodes.

Return type:

np.ndarray[number of electrodes, number of sub-bands]