Rapid detection of voluntary movements in a self-paced brain-computer interface via compressive sensing
Bibliographic record
Abstract
This study employs compressive sensing (CS) for accelerating the overall data processing in a brain-computer interface (BCI). CS is a joint signal acquisition and compression scheme. By projecting EEG data streams onto a lower dimensional basis that is incoherent with their inherent structure, CS compresses the data and preserves their salient information. This paper presents a novel self-paced BCI (SBCI) that extracts compact and relevant features from EEG using CS. To detect an intentional control (IC) state from user's EEG, the signal structure of a specific voluntary movement that is common in all trials is identified. This compressed subset of basis functions is constructed during the BCIs training mode and is then used to acquire features during its testing mode. Experimental results show that our proposed method can efficiently detect voluntary movements for a SBCI, with potential speed increases up to 12 times from the state-of-art prototype.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".