EMO-CARE — Subject-Independent EEG Emotion Recognition
- PyTorch
- EEG
- emotion recognition
- deep learning
What it is
A deep-learning framework for EEG emotion recognition that generalizes to unseen subjects — the hard, deployment-relevant setting.
It uses 16 parallel Temporal Convolutional Network streams (kernel sizes 3–33, dilations 1/2/4) to capture emotional dynamics across timescales, then an 8-head self-attention over the 16 scale tokens to weight which temporal scales matter. Evaluated rigorously with Leave-One-Subject-Out cross-validation on SEED, SEED-V, and DREAMER (94.3% / 91.2% / 84.3%), with ~15 ms inference suitable for real-time use.
Stack
PyTorch 2.0 · multi-scale TCNs · self-attention · LOSO with statistical validation
Links
- GitHub repository
- Related paper: IEEE Open Journal of the Computer Society, 2026