Comparison of Using Mono-Polar and Bipolar Electroencephalogram (EEG) Electrodes for Detection of Right and Left Hand Movements in a Self-Paced Brain Computer Interface (BCI)
Bibliographic record
Abstract
Unlike synchronous brain computer interfaces (BCI), self-paced (asynchronous) ones have the advantage of being operational at all times and not only at specific system-defined periods. A 3-state self-paced BCI is capable of detecting two different brain states (e.g. two movements) from the ongoing EEG. However, a 2-state one can only detect one brain state from the ongoing EEG. This study evaluates the performance of a 3-state self-paced BCI in detecting right and left hand movements. At first, we compare the performance of the system in differentiating between right and left hand movements using two different inputs: (1) mono-polar, and (2) bipolar electrode setting. Using bipolar electrode setting which yielded better performance than a mono-polar one, we evaluate the overall performance of the 3-state BCI system in a self-paced testing paradigm. Using data collected from two able-bodied individuals, it is shown that the average performance (true positive rate) of the system in detecting the presence of movements is 54.7% at a fixed false positive rate of 1%, and the average performance of the system in differentiating between right and left hand movements is 70.25%.
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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 | 0.005 |
| 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 | 0.001 | 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".