A New Design of the Asynchronous Brain Computer Interface Using the Knowledge of the Path of Features
Bibliographic record
Abstract
The low-frequency asynchronous switch design (LF-ASD) was introduced as a direct brain computer interface (BCI) technology for asynchronous control applications. The LF-ASD has the advantage that it is operational at any time and not only at specific defined periods. It is activated only when a user intends control, otherwise it maintains an inactive state output. The intended control results from a certain movement attempt such as finger flexion. This paper presents the evaluation of a modified LF-ASD design with data collected from individuals with high-level spinal cord injuries and able-bodied subjects. The modifications are related to incorporating into the system more knowledge about the movement attempt. Specifically, the past values of the features extracted from the EEG signal related to the movement attempts are used. The error characteristics of this new asynchronous brain switch design are significantly better than the previous LF-ASD design, with true positive rate increases of approximately 8.5% for false positive rates in the range of 1-2%
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".