PhysioNet-MI
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Motor Imagery Classification | Physionet-MI | Balanced Accuracy64.57 | 27 | |
| Motor Imagery | PhysioNet-MI | Accuracy63.9 | 20 | |
| Motor imagery classification | PhysioNet-MI 4-class (online) | Balanced Accuracy61.74 | 12 | |
| Motor Imagery classification | PhysioNet MI 109 subj. | Balanced Accuracy67.8 | 12 | |
| EEG Classification | PhysioNet-MI 4-Class 9,837 Samples | Balanced Accuracy63.05 | 11 | |
| Unsupervised Continual Learning | PhysioNet-MI | Accuracy61.7 | 10 | |
| Motor Imagery Classification | PhysioNet-MI 4-Class (test) | Balanced Accuracy64.17 | 9 | |
| Motor Imagery Classification | Physionet-MI (test) | Balanced Accuracy64.57 | 6 | |
| Motor Imagery Classification | PhysioNet-MI 4-class original | Accuracy67.6 | 3 |
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