braindecode.augmentation.AugmentedDataLoader#

class braindecode.augmentation.AugmentedDataLoader(dataset, transforms=None, device=None, n_augmentation=0, **kwargs)[source]#

A base dataloader class customized to applying augmentation Transforms.

Parameters:

dataset (RecordDataset) – The dataset containing the signals.
transforms (list | Transform, optional) – Transform or sequence of Transform to be applied to each batch.
device (str | torch.device | None, optional) – Device on which to transform the data. Defaults to None.
n_augmentation (int, optional) – Number of augmented copies to append to each batch (fixed expansion). When 0 (default) the transforms are applied in place and the batch keeps its size (stochastic-per-epoch augmentation, backwards-compatible). When > 0 each batch becomes (1 + n_augmentation) times larger: the clean originals are kept and n_augmentation independently transformed copies are appended. This expresses augmentations defined as a fixed set-expansion (e.g. the EEG-Inception MI 6x training set, n_augmentation=5). In this mode batches are returned as (X, y).
**kwargs (dict, optional) – keyword arguments to pass to standard DataLoader class.

Examples using `braindecode.augmentation.AugmentedDataLoader`#