vlawhern/arl-eegmodels

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1552 stars · 335 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of well-validated convolutional neural network models (EEGNet, DeepConvNet, ShallowConvNet) for EEG signal classification, implemented in Keras and TensorFlow. It is maintained by the Army Research Laboratory to support reproducible brain-computer interface research.

Use cases

• classify EEG signals with deep learning
• implement EEGNet in Keras
• classify event-related potentials from EEG
• decode sensory motor rhythm from EEG recordings
• classify SSVEP signals with a CNN
• compare EEG classification models on my own dataset
• build a brain-computer interface classifier

When to choose

• you need ready-made, published CNN architectures for EEG classification
• you work in Python with TensorFlow/Keras and EEG data
• you want reproducible baselines for BCI research

When to avoid

• you need PyTorch instead of TensorFlow/Keras
• you need general-purpose time-series classification beyond EEG
• you require recent TensorFlow versions or active maintenance

Facets

library · maturity maintenance

deep-learning machine-learning nlp deep-learning machine-learning healthcare python eeg brain-computer-interface keras tensorflow cnn signal-classification eegnet neuroscience

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