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EEG Emotion Recognition for Depression Detection
Results (updated in real-time)
| Model | 30ch Acc | 19ch Acc |
|---|---|---|
| ML (LightGBM) | 0.7311±0.0170 | - |
| Augmented EEGNetLite | training... | - |
| EEGNetLite | 0.6734±0.0038 | 0.6723±0.0037 |
| ResNet1D | 0.6569±0.0036 | 0.6539±0.0054 |
| TemporalAttn | 0.6495±0.0022 | 0.6510±0.0039 |
| MultiScaleCNN | 0.6454±0.0033 | 0.6465±0.0033 |
| DualPath | 0.6376±0.0028 | 0.6413±0.0017 |
| FreqAttn | 0.6233±0.0029 | 0.6258±0.0046 |
Evaluation: 5 seeds × 5-fold GroupKFold CV, 48 subjects (24 HC + 24 DEP)
Quick Start
pip install torch numpy h5py scikit-learn lightgbm scipy
cd EEGNetLite_30ch && python test.py --data_path /path/to/emotion-data-depression
Structure
Each folder contains: test.py + weights/ + README.md
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