SuperBruceJia/EEG-DL

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1171 stars · 228 forks observed · 2026-09-30

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

EEG-DL is a deep learning library built on TensorFlow for classifying EEG signals, supporting many architectures including CNNs, RNNs, GCNs, ResNets, Transformers, and attention mechanisms. It is aimed at neuroscience and brain-computer interface research, particularly motor imagery task decoding.

Use cases

• classify EEG motor imagery signals with deep learning
• decode four-class motor imagery tasks from EEG
• apply CNNs or GCNs to raw EEG signals
• experiment with transformers and attention models on EEG data
• build brain-computer interface classifiers
• benchmark deep learning models on EEG datasets

When to choose

• you need ready-to-use TensorFlow models for EEG classification
• you want to compare many DL architectures (CNN, RNN, GCN, Transformer) on EEG tasks
• you are doing BCI or motor imagery research in Python

When to avoid

• you need real-time EEG processing or a production BCI system
• you work with PyTorch instead of TensorFlow
• your signals are not EEG/neurophysiological

Facets

library · maturity active

deep-learning machine-learning nlp deep-learning machine-learning healthcare artificial-intelligence python cross-platform eeg tensorflow brain-computer-interface motor-imagery signal-classification cnn rnn graph-convolutional-networks transformer neuroscience

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