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name: models-and-training description: "Guides braindecode model selection, signal-parameter initialization, skorch classifier or regressor training, cropped decoding, prediction, and local checkpoint use." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Models and training

Use this route when a task asks for a Braindecode model, model registry lookup, EEGClassifier, EEGRegressor, cropped decoding, fine-tuning, evaluation, regression, or local pretrained weights.

Workflow

1. Start with a validated dataset/window contract: channels, samples, sfreq, dtype, target type, and output count. Run a model forward before training.
2. Choose a representative model family from the overview, then pass explicit n_chans/n_times/n_outputs/sfreq or let the skorch wrapper infer signal parameters from a compatible dataset. Use final_conv_length="auto" only when the model supports it and verify the resulting shape.
3. Configure EEGClassifier or EEGRegressor with an optimizer, criterion, split, batch size, callbacks, and explicit device. module may be a class, name from the registry, or initialized module depending on the route; verify the wrapper's accepted form.
4. Distinguish trialwise output from dense/cropped output. Use CroppedLoss and compatible aggregation only for window/crop workflows; inspect predictions and targets before reporting a metric.
5. For pretrained models, validate checkpoint keys and tensor shapes locally, keep the classification head distinction explicit, and never download or upload a checkpoint without authorization.

Read model overview, API reference, training workflows, pretrained models, and troubleshooting. Run the bounded local training smoke before an expensive experiment.