braindecode.augmentation.functional.ft_surrogate#
- braindecode.augmentation.functional.ft_surrogate(X, y, phase_noise_magnitude, channel_indep, random_state=None)[source]#
FT surrogate augmentation of a single EEG channel, as proposed in [1].
Function copied from cliffordlab/sleep-convolutions-tf and modified.
- Parameters:
X (
Tensor) – EEG input example or batch.y (
Tensor) – EEG labels for the example or batch.phase_noise_magnitude (
float) – Float between 0 and 1 setting the range over which the phase perturbation is uniformly sampled: [0, phase_noise_magnitude * 2 * pi].channel_indep (
bool) – Whether to sample phase perturbations independently for each channel or not. It is advised to set it to False when spatial information is important for the task, like in BCI.random_state (
int|RandomState|None) – Used to draw the phase perturbation. Defaults to None.
- Return type:
- Returns:
torch.Tensor – Transformed inputs.
torch.Tensor – Transformed labels.
References
[1]Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., & Clifford, G. D. (2018). Addressing Class Imbalance in Classification Problems of Noisy Signals by using Fourier Transform Surrogates. arXiv preprint arXiv:1806.08675.