braindecode.augmentation.MaskEncoding#
- class braindecode.augmentation.MaskEncoding(probability, max_mask_ratio=0.1, n_segments=1, random_state=None)[source]#
MaskEncoding from [1].
Replaces randomly chosen contiguous part (or parts) of all channels by zeros (if more than one segment, it may overlap).
Implementation based on [1]
- Parameters:
probability (float) – Float setting the probability of applying the operation.
max_mask_ratio (float, optional) – Signal ratio to zero out. Defaults to 0.1.
n_segments (int, optional) – Number of segments to zero out in each example. Defaults to 1.
random_state (int | numpy.random.Generator, optional) – Seed to be used to instantiate numpy random number generator instance. Defaults to None.
References
Methods
- get_augmentation_params(*batch)[source]#
Return transform parameters.
- Parameters:
X (tensor.Tensor) – The data.
y (tensor.Tensor) – The labels.
- Returns:
params – Contains …
- Return type:
- static operation(X, y, time_start, segment_length, n_segments)[source]#
Mark encoding from Ding et al. (2024) from [ding2024].
Replaces a contiguous part (or parts) of all channels by zeros (if more than one segment, it may overlap).
Implementation based on [ding2024]
- Parameters:
X (torch.Tensor) – EEG input example or batch.
y (torch.Tensor) – EEG labels for the example or batch.
time_start (torch.Tensor) – Tensor of integers containing the position (in last dimension) where to start masking the signal. Should have “n_segments” times the size of the first dimension of X (i.e. “n_segments” start positions per example in the batch).
segment_length (int) – Length of each segment to zero out.
n_segments (int) – Number of segments to zero out in each example.
- Returns:
torch.Tensor – Transformed inputs.
torch.Tensor – Transformed labels.
References