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name: datasets-and-windowing description: "Guides braindecode dataset construction, MNE and array conversion, metadata-aware windowing, splitting, and safe serialization for electrophysiological signals." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Datasets and windowing

Use this route when the task starts with NumPy arrays, MNE Raw/Epochs, BIDS-like recordings, or a braindecode dataset and asks for targets, metadata, windows, splits, concatenation, or persistence.

Workflow

1. Normalize X as (trials, channels, time) and record sfreq, channel names, units, subject/session identifiers, and target semantics.
2. For local arrays use create_from_X_y; for MNE objects use the MNE conversion helpers. Inspect one item and its description before windowing.
3. Choose event windows when annotations/events define trial boundaries, fixed windows for continuous recordings, and target-channel windows only when the target is represented as a channel in the raw object.
4. Split by subject/session before overlapping windows. Use descriptions to filter/partition rather than random row splits when assessing generalization.
5. Persist only after validating metadata and choosing a writable, disposable path. Treat Hub/BIDS/MOABB/TUH/Sleep integrations as optional data acquisition workflows, not offline smoke tests.

Read API reference for constructors and shapes, workflows for local conversion/windowing and serialization recipes, and optional integrations when a named external dataset or Hub is required. Use troubleshooting before retrying a failed conversion or window operation. Run the synthetic smoke helper to validate local behavior without network access.