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name: augmentation-and-sampling description: "Guides braindecode signal transforms, composed augmentation loaders, and sequence, relative-positioning, and self-supervised samplers with shape-safe validation." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Augmentation and sampling

Use this route when augmentations, Transform composition, AugmentedDataLoader, n_augmentation, sequence sampling, relative positioning, or self-supervised sampling is the main task.

Workflow

1. Confirm batches are (X, y) with X shaped (batch, channels, time) and that labels remain aligned after each transform.
2. Build a Transform or Compose with an explicit probability and random state. Test it on one batch and verify shape, dtype, device, and label invariants.
3. Use AugmentedDataLoader for training batches. Remember that n_augmentation=0 preserves the original batch; positive values append transformed copies after clean originals and tile labels accordingly.
4. Choose sequence/relative-positioning/self-supervised samplers only when the dataset contains the required trial/session/window metadata. Verify indices and split boundaries before training.
5. Use CPU first. Move both model and batches to CUDA only after a bounded allocation succeeds; a CUDA availability flag is not a memory guarantee.

Read API reference, sampling workflows, and troubleshooting. Run the deterministic augmentation smoke, which uses a local TensorDataset and no network or credentials.