A Comparative Study on Generating Training-Data for Self-Paced Brain Interfaces
Bibliographic record
Abstract
Direct brain interface (BI) systems provide an alternative communication and control solution for individuals with severe motor disabilities, bypassing impaired interface pathways. Most BI systems are aimed to be operated by individuals with severe disabilities. With these individuals, there is no observable indicator of their intent to control or communicate with the BI system. In contrast, able-bodied subjects can perform the desired physical movements such as finger flexion and one can observe the movement as the indicator of intent. Since no external knowledge of intention is available for individuals with severe motor disabilities, generating the data for system training is problematic. This paper introduces three methods for generating training-data for self-paced BI systems and compares their performances with four alternative methods of training-data generation. Results of the offline analysis on the electroencephalogram data of eight subjects during self-paced BI experiments show that two of the proposed methods increase true positive rates (at fixed false positive rate of 2%) over that of the four alternative methods from 50.8%-58.4% to about 62% which corresponds to 3.6%-11.2% improvement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".