What a run means
A run is a full training + evaluation pass for one subject using fixed-length EEG windows. The model predicts both an action class and a finger class for each window, then we measure accuracy on a held-out test split. The finger accuracy highlighted here is reported on non-REST windows, meaning it excludes windows labeled as rest. This makes the finger metric more relevant to active movement identification. These results are window-level snapshots for a single run and are best interpreted alongside additional runs.
Comparison: 1-M16 vs 2-M16
| Metric | 1-M16 | 2-M16 |
|---|---|---|
| Test action accuracy | 77.38% | 89.31% |
| Test finger accuracy on non-REST windows | 86.27% | 89.90% |
| Test windows | 2,648 | 2,040 |
| Test non-REST windows | 2,534 | 1,871 |
| Train vs test action gap (pp) | +5.98 pp | +1.68 pp |
| Train avg loss | 0.7421 | 0.5174 |
What changed / what we learned
- 1-M16 has lower test action accuracy than 2-M16.
- 1-M16 has lower finger accuracy on non-REST windows than 2-M16.
- 1-M16 evaluated more test windows and more non-REST test windows.
- The train-test action gap is larger for 1-M16.
- Train average loss is higher for 1-M16.
Since this update was first published, live robot-hand actuation testing has been completed for subject 2-M16. We are continuing to collect additional runs to better quantify variation across subjects and sessions.