EEG Classification on TUH Abnormal EEG Corpus (test)
86.57AccuracyChrononet
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| Chrononet2025.07 | 86.57 | — | — | |
| EEG-VJEPAArchitecture=ViT-M/4 × 30 × 4, Evaluation Protocol=Fine-tuning2025.07 | 85.8 | 85.6 | — | |
| EEG-VJEPAArchitecture=ViT-M/4 × 30 × 4, Evaluation Protocol=Frozen evaluation2025.07 | 83.3 | 82.4 | — | |
| BSVT2025.07 | 82.67 | 77.32 | — | |
| CL ModelEvaluation Protocol=Fine-tuning2025.07 | 81.9 | 81.5 | — | |
| EEG-VJEPAArchitecture=ViT-B/4 × 30 × 2, Evaluation Protocol=Frozen evaluation2025.07 | 81.2 | 81 | — | |
| CL ModelEvaluation Protocol=Frozen evaluation2025.07 | 80.55 | 79.3 | — | |
| EEG-VJEPAArchitecture=ViT-B/4 × 30 × 2, Evaluation Protocol=Fine-tuning2025.07 | 80.55 | 80.1 | — | |
| Unrolled Graph Signal Denoising Network2025.10 | 0.9069 | 0.926 | 0.8976 | |
| DWT + CSP + CatBoost2025.10 | 0.9022 | 0.8889 | 0.8976 | |
| AlexNet + MLP2025.10 | 0.8913 | 0.8706 | 0.8802 | |
| WPD + CatBoostvariant=22025.10 | 0.8913 | 0.876 | 0.886 | |
| WaveNet-LSTM2025.10 | 0.8876 | 0.8832 | 0.8839 | |
| WPD + CatBoostvariant=12025.10 | 0.8768 | 0.8606 | 0.8724 | |
| Multilevel DWT + KNN2025.10 | 0.8768 | 0.8607 | 0.8724 | |
| AlexNet + SVM2025.10 | 0.8732 | 0.8497 | 0.8624 | |
| HT + RG2025.10 | 0.8586 | 0.834 | 0.8519 | |
| BD-Deep42025.10 | 0.854 | 0.8252 | 0.8408 | |
| LSTM + Attention2025.10 | 0.7905 | 0.79 | 0.79 |