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name: preprocessing description: "Guides braindecode preprocessing pipelines for MNE and array-backed electrophysiology datasets, including typed operations, window ordering, and serialized execution." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Preprocessing

Use this route for filtering, resampling, scaling, channel selection, montage operations, cropping, preprocessing class composition, or preprocess execution on raw, epoch, concat, or window datasets.

Workflow

1. Record the input object type, channel order/types, sampling frequency, units, preload state, and whether the operation mutates the object.
2. Build a short ordered list of Preprocessor objects. Use typed classes such as Pick, Resample, Filter, Scale, Crop, and montage operations when they make units and validation explicit; use a callable for custom array operations only when apply_on_array is correct.
3. Apply continuous/raw operations before windowing when they require temporal context. Apply window-level normalization after window creation when it must not mix recordings.
4. Recalculate sample-based window parameters after resampling. Start with n_jobs=1; increase parallelism only after a one-recording result is correct.
5. Use save_dir/overwrite only with a deliberate cache key containing the preprocessing configuration.

Read API reference, workflow guidance, and troubleshooting. Run the local synthetic preprocessing smoke helper; it performs no data fetch, Hub operation, or model training. EEGPrep is an optional integration, not a baseline requirement.