# vlawhern/arl-eegmodels

This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow

Repository: https://github.com/vlawhern/arl-eegmodels
Canonical: https://ross.abutalabs.com/products/arl-eegmodels
Language: Python
License: NOASSERTION
License Family: other
Topics: eeg, deep-learning, convolutional-neural-networks, keras, tensorflow, brain-computer-interface, eeg-classification, time-series-classification, event-related-potentials, sensory-motor-rhythm
Last push: 2022-05-02T12:46:03+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3032, "days_push": 1584, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1552, forks 335 (observed 2026-08-28T04:05:02.735660+00:00)

## What it is
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
- artifact type: library
- maturity: maintenance
- function: deep-learning, machine-learning, nlp
- domain: deep-learning, machine-learning, healthcare
- platform: python
- tags: eeg, brain-computer-interface, keras, tensorflow, cnn, signal-classification, eegnet, neuroscience

## Member repositories
- vlawhern/arl-eegmodels (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:02.735660+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:30:14.131829+00:00, confidence not recorded.
  - readme: https://github.com/vlawhern/arl-eegmodels (fetched 2026-08-28T04:05:02.735660+00:00, sha 4c62e347bb3f)
- Data as of 2026-08-30T08:39:29.467469+00:00.