EEGClassify

eegclassify is a standalone Python package for reproducible EEG task classification. It combines BCI Competition IV-2a data preparation, experiment-ready preprocessing, and deep learning classifiers across TensorFlow, PyTorch, and JAX/Flax.

The package is designed for users who want to regenerate the processed dataset, run quick framework smoke tests, and scale the same configuration into full training runs.

What You Can Do

• Download and convert public BCI Competition IV-2a raw data.
• Load the packaged processed .npy arrays for local examples.
• Train CNN, LSTM, and CNN-LSTM classifiers in three frameworks.
• Add conditional GAN-generated samples to the training split.
• Compare local runs against the package reference targets.
• Use notebooks for guided workflows or the CLI for repeatable runs.

Start Here

1. Install the package with the extras you need in Installation.
2. Download or regenerate the EEG arrays in Data.
3. Walk through the rendered Notebook Examples.
4. Run CLI or notebook experiments with Reproduce Results.
5. Check target metrics in Results.
6. Build on the package through the API Reference.

The default examples use FAST_DEV_RUN=True, so they are meant to be approachable first and expandable when you are ready for longer runs.