Suppressing flashes of items surrounding targets during calibration of a P300-based brain–computer interface improves performance
Bibliographic record
Abstract
Since the introduction of the P300 brain-computer interface (BCI) speller by Farwell and Donchin in 1988, the speed and accuracy of the system has been significantly improved. Larger electrode montages and various signal processing techniques are responsible for most of the improvement in performance. New presentation paradigms have also led to improvements in bit rate and accuracy (e.g. Townsend et al (2010 Clin. Neurophysiol. 121 1109-20)). In particular, the checkerboard paradigm for online P300 BCI-based spelling performs well, has started to document what makes for a successful paradigm, and is a good platform for further experimentation. The current paper further examines the checkerboard paradigm by suppressing items which surround the target from flashing during calibration (i.e. the suppression condition). In the online feedback mode the standard checkerboard paradigm is used with a stepwise linear discriminant classifier derived from the suppression condition and one classifier derived from the standard checkerboard condition, counter-balanced. The results of this research demonstrate that using suppression during calibration produces significantly more character selections/min ((6.46) time between selections included) than the standard checkerboard condition (5.55), and significantly fewer target flashes are needed per selection in the SUP condition (5.28) as compared to the RCP condition (6.17). Moreover, accuracy in the SUP and RCP conditions remained equivalent (∼90%). Mean theoretical bit rate was 53.62 bits/min in the suppression condition and 46.36 bits/min in the standard checkerboard condition (ns). Waveform morphology also showed significant differences in amplitude and latency.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".