BNCI
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| EEG Classification | BNCI2014002 | Benign Accuracy80.88 | 42 | |
| EEG Classification | BNCI 2014001 | Benign Accuracy69.59 | 42 | |
| Cross-subject Binary Classification | BNCI2014001 cross-subject | Accuracy79.91 | 26 | |
| BCI classification | BNCI2015001 (inter-subject) | Balanced Accuracy77 | 11 | |
| BCI classification | BNCI2015001 (inter-session) | Balanced Acc85.8 | 11 | |
| BCI classification | BNCI2014001 (inter-subject) | Balanced Accuracy51.6 | 11 | |
| BCI classification | BNCI2014001 (inter-session) | Balanced Accuracy71.3 | 11 | |
| MI | BNCI2014 Subject | Accuracy37.29 | 7 | |
| MI | BNCI Session 2014 | Accuracy61.21 | 7 | |
| P300 | BNCI Subject 2014 | Accuracy88.57 | 7 | |
| P300 | BNCI Session 2014 | Accuracy89.65 | 7 | |
| EEG Event Detection and Localization | BNCI 2014 | F1 Detection90.9 | 4 | |
| Motor Imagery Classification | BNCI Cross-Session A to B 2015_001 (Holdout) | Accuracy82.58 | 4 | |
| Motor Imagery Classification | BNCI Session B 2015_001 (10-Fold CV) | Accuracy85.58 | 4 | |
| Motor Imagery Classification | BNCI Session A 2015_001 (10-Fold CV) | Accuracy83.25 | 4 | |
| Class-conditional EEG generation | BNCI 2015-001 | Alpha Precision (α-P)0.93 | 3 | |
| Class-conditional EEG generation | BNCI 2014-002 | alpha-P75 | 3 | |
| EEG decoding | BNCI 2015001 | bAcc80.9 | 3 | |
| EEG decoding | BNCI2014001 | Balanced Accuracy83.9 | 2 | |
| EEG Event Detection and Localization | BNCI 2015 | F1 Detection Score84.3 | 2 |
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