RandomFrequencyShift

class torcheeg.transforms.RandomFrequencyShift(p: float = 0.5, sampling_rate: int = 128, shift_min: float | int = -2.0, shift_max: float | int = 2.0, series_dim: int = 0, apply_to_baseline: bool = False)

Apply a frequency shift with a specified probability, after which the EEG signals of all channels are equally shifted in the frequency domain.

transform = RandomFrequencyShift()
transform(eeg=torch.randn(32, 128))['eeg'].shape
>>> (32, 128)

transform = RandomFrequencyShift(sampling_rate=128, shift_min=4.0)
transform(eeg=torch.randn(1, 32, 128))['eeg'].shape
>>> (1, 32, 128)

transform = RandomFrequencyShift(p=1.0, series_dim=0)
transform(eeg=torch.randn(128, 9, 9))['eeg'].shape
>>> (128, 9, 9)

Parameters:

• sampling_rate (int) – The original sampling rate in Hz (default: 128)

• shift_min (float or int) – The minimum shift in the random transformation. (default: -2.0)

• shift_max (float or int) – The maximum shift in random transformation. (default: 2.0)

• series_dim (int) – Dimension of the time series in the input tensor. (default: -1)

• p (float) – Probability of applying random mask on EEG signal samples. Should be between 0.0 and 1.0, where 0.0 means no mask is applied to every sample and 1.0 means that masks are applied to every sample. (default: 0.5)

• apply_to_baseline – (bool): Whether to act on the baseline signal at the same time, if the baseline is passed in when calling. (default: False)

__call__(*args, eeg: Tensor, baseline: Tensor | None = None, **kwargs) → Dict[str, Tensor]

Parameters:

• eeg (torch.Tensor) – The input EEG signal.

• baseline (torch.Tensor, optional) – The corresponding baseline signal, if apply_to_baseline is set to True and baseline is passed, the baseline signal will be transformed with the same way as the experimental signal.

Returns:

The output EEG signal after applying a random sampling_rate shift.

Return type:

torch.Tensor