braindecode.augmentation.GaussianNoise

class braindecode.augmentation.GaussianNoise(probability, std=0.1, random_state=None)

Randomly add white noise to all channels.

Suggested e.g. in [1], [2] and [3]

Parameters:

• probability (float) – Float setting the probability of applying the operation.

• std (float, optional) – Standard deviation to use for the additive noise. Defaults to 0.1.

• random_state (int | numpy.random.Generator, optional) – Seed to be used to instantiate numpy random number generator instance. Defaults to None.

References

[1]

Wang, F., Zhong, S. H., Peng, J., Jiang, J., & Liu, Y. (2018). Data augmentation for eeg-based emotion recognition with deep convolutional neural networks. In International Conference on Multimedia Modeling (pp. 82-93).

[2]

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

[3]

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_augmentation_params(*batch)

Return transform parameters.

Parameters:

• X (tensor.Tensor) – The data.

• y (tensor.Tensor) – The labels.

Returns:

params – Contains

• stdfloat

  Standard deviation to use for the additive noise.

• random_statenumpy.random.Generator

  The generator to use.

Return type:

dict

static operation(X, y, std, random_state=None)

Randomly add white Gaussian noise to all channels.

Suggested e.g. in [1], [2] and [3]

Parameters:

• X (torch.Tensor) – EEG input example or batch.

• y (torch.Tensor) – EEG labels for the example or batch.

• std (float) – Standard deviation to use for the additive noise.

• random_state (int | numpy.random.Generator, optional) – Seed to be used to instantiate numpy random number generator instance. Defaults to None.

Returns:

• torch.Tensor – Transformed inputs.

• torch.Tensor – Transformed labels.

References

[1]

Wang, F., Zhong, S. H., Peng, J., Jiang, J., & Liu, Y. (2018). Data augmentation for eeg-based emotion recognition with deep convolutional neural networks. In International Conference on Multimedia Modeling (pp. 82-93).

[2]

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

[3]

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