Electrode Fusion for the Prediction of Self-Initiated Fine Movements from Single-Trial Readiness Potentials
Why this work is in the frame
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Bibliographic record
Abstract
Current human-machine interfaces (HMIs) for users with severe disabilities often have difficulty distinguishing between intentional and inadvertent activations. Pre-movement neuro-cortical activity may aid in this elusive discrimination task but has not been exploited in HMIs. This work investigates the utility of the readiness potential (RP), a slow negative cortical potential preceding voluntary movement, for detecting the intention of self-initiated fine movements prior to their motoric realization. We recorded electroencephalography from the frontal, central, parietal and occipital lobes of 10 participants using a self-initiated switch activation protocol. Eye movement artifacts were removed by regression and the RP was detected on a single-trial basis, in a narrow frequency range (0.1-1 Hz). Common average reference was applied prior to windowed-averaging for feature extraction. Electrodes were selected according to a separability measure based on Fisher projection. Our findings demonstrate that feature fusion from an optimal number of electrodes achieves a statistically significant lower classification error than the best single classifier. Finally, voluntary fine movement intention was detected on a single-trial basis at above-chance levels approximately 396 ms before physical switch activation. These findings encourage the development of rapid-response, intention-aware HMIs for individuals with severe disabilities who struggle with executing voluntary fine motor movements.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it