braindecode.augmentation.FTSurrogate¶
- class braindecode.augmentation.FTSurrogate(probability, phase_noise_magnitude=1, random_state=None)¶
FT surrogate augmentation of a single EEG channel, as proposed in [1].
- Parameters
- probability: float
Float setting the probability of applying the operation.
- phase_noise_magnitudefloat | torch.Tensor, optional
Float between 0 and 1 setting the range over which the phase pertubation is uniformly sampled:
[0, phase_noise_magnitude * 2 * pi]. Defaults to 1.- random_state: int | numpy.random.Generator, optional
Seed to be used to instantiate numpy random number generator instance. Used to decide whether or not to transform given the probability argument. Defaults to None.
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.
Methods
- get_params(*batch)¶
Return transform parameters.
- Parameters
- Xtensor.Tensor
The data.
- ytensor.Tensor
The labels.
- Returns
- paramsdict
Contains:
- phase_noise_magnitudefloat
The magnitude of the transformation.
- random_statenumpy.random.Generator
The generator to use.
- static operation(X, y, phase_noise_magnitude, random_state=None)¶
FT surrogate augmentation of a single EEG channel, as proposed in [1].
Function copied from https://github.com/cliffordlab/sleep-convolutions-tf and modified.
- Parameters
- Xtorch.Tensor
EEG input example or batch.
- ytorch.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].
- random_state: int | numpy.random.Generator, optional
Used to draw the phase perturbation. Defaults to None.
- 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.