- 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 .
Function copied from cliffordlab/sleep-convolutions-tf and modified.
X (torch.Tensor) – EEG input example or batch.
y (torch.Tensor) – EEG labels for the example or batch.
phase_noise_magnitude (float) – Float between 0 and 1 setting the range over which the phase pertubation 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 | numpy.random.Generator, optional) – Used to draw the phase perturbation. Defaults to None.
torch.Tensor – Transformed inputs.
torch.Tensor – Transformed labels.
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.