Reproduce Results#

The package notebooks and CLI share the same experiment settings, so you can move between an interactive workflow and a scripted run without changing the core configuration.

The rendered notebook examples are:

Each training notebook defaults to:

FAST_DEV_RUN = True
USE_GAN_AUGMENTATION = False

This runs a short smoke check. Set FAST_DEV_RUN = False for the full 100-epoch classifier reproduction. Set USE_GAN_AUGMENTATION = True to train a conditional GAN on the training split, append generated samples, and then train the selected classifier on real plus synthetic training data.

The default experiment settings are:

labels 769-772 are converted to 0-3
validation ratio is 0.17
seed is 1
batch size is 64
classifier epochs are 100
GAN epochs are 20
augmentation uses trim, max-pooling, averaging with Gaussian noise, and subsampling

GAN augmentation is available for CNN, LSTM, and CNN-LSTM. The GAN+CNN row below is the canonical GAN-augmented benchmark path for the package.

Reference accuracy targets:

Experiment	Reference test accuracy
CNN	70.49%
CNN-LSTM	60.95%
LSTM	39.62%
GAN+CNN	68.23%

Run the CLI equivalent:

python -m eegclassify.cli train --framework tensorflow --model cnn --fast-dev-run
python -m eegclassify.cli train --framework pytorch --model cnn --fast-dev-run
python -m eegclassify.cli train --framework jax --model cnn --fast-dev-run

For GPU runs, install the GPU extras from the installation page and do not set CUDA_VISIBLE_DEVICES=-1. TensorFlow GPU training uses the CLI's CUDA path setup; PyTorch and JAX use their framework CUDA packages directly.

For the GAN-augmented CNN path:

python -m eegclassify.cli train \
  --framework tensorflow \
  --model gan_cnn \
  --use-gan-augmentation

Each CLI run writes a metrics report under artifacts/runs/.

Run tests before long experiments:

pytest

Build docs locally:

mkdocs build --strict