braindecode.augmentation.BandstopFilter¶

class braindecode.augmentation.BandstopFilter(probability, sfreq, bandwidth=1, max_freq=None, random_state=None)¶

Apply a band-stop filter with desired bandwidth at a randomly selected frequency position between 0 and max_freq.

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

Parameters

probabilityfloat: Float setting the probability of applying the operation.
bandwidthfloat: Bandwidth of the filter, i.e. distance between the low and high cut frequencies.
sfreqfloat, optional: Sampling frequency of the signals to be filtered. Defaults to 100 Hz.
max_freqfloat | None, optional: Maximal admissible frequency. The low cut frequency will be sampled so that the corresponding high cut frequency + transition (=1Hz) are below max_freq. If omitted or None, will default to the Nyquist frequency (sfreq / 2).
random_state: int | numpy.random.Generator, optional: Seed to be used to instantiate numpy random number generator instance. Defaults to None.

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.

Methods

get_params(*batch)¶

Return transform parameters.

Parameters

Returns

paramsdict

Contains

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).

static operation(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

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.