GaussTime

class dpeeg.transforms.GaussTime(std: float, only_train: bool = True, strict: bool = True, seed: int = 42)

Randomly add white noise to all channels.

Gaussian white noise with a mean of 0 is directly added to the raw EEG signal as the generated new data [1]. By default, augmentation is performed on edata and label. Ensure the availability of the data.

Parameters:

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

• only_train (bool) – If True, data augmentation is performed only on the training set.

• strict (bool) – If False, allow the input data to be unsplit. In this case, data augmentation will be applied to all data. Please ensure that this operation does not cause data leakage according to the method of data augmentation.

• seed (int) – Seed to be used to instantiate numpy random number generator instance.

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).