braindecode.augmentation.functional.bandstop_filter

braindecode.augmentation.functional.bandstop_filter(X, y, sfreq, bandwidth, freqs_to_notch)

Apply a band-stop filter with desired bandwidth at the desired frequency position.

Suggested e.g. in [1] and [2]

Parameters

Xtorch.Tensor

EEG input example or batch.

ytorch.Tensor

EEG labels for the example or batch.

sfreqfloat

Sampling frequency of the signals to be filtered.

bandwidthfloat

Bandwidth of the filter, i.e. distance between the low and high cut frequencies.

freqs_to_notcharray-like | None

Array of floats of size (batch_size,) containing the center of the frequency band to filter out for each sample in the batch. Frequencies should be greater than bandwidth/2 + transition and lower than sfreq/2 - bandwidth/2 - transition (where transition = 1 Hz).

Returns

torch.Tensor

Transformed inputs.

torch.Tensor

Transformed labels.

References

1

Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., & Azemi, E. (2020). Subject-aware contrastive learning for biosignals. arXiv preprint arXiv:2007.04871.

2

Mohsenvand, M. N., Izadi, M. R., & Maes, P. (2020). Contrastive Representation Learning for Electroencephalogram Classification. In Machine Learning for Health (pp. 238-253). PMLR.