A self-paced brain–computer interface system with a low false positive rate
Why this work is in the frame
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Bibliographic record
Abstract
The performance of current EEG-based self-paced brain-computer interface (SBCI) systems is not suitable for most practical applications. In this paper, an improved SBCI that uses features extracted from three neurological phenomena (movement-related potentials, changes in the power of Mu rhythms and changes in the power of Beta rhythms) to detect an intentional control command in noisy EEG signals is proposed. The proposed system achieves a high true positive (TP) to false positive (FP) ratio. To extract features for each neurological phenomenon in every EEG signal, a method that consists of a stationary wavelet transform followed by matched filtering is developed. For each neurological phenomenon in every EEG channel, features are classified using a support vector machine classifier (SVM). For each neurological phenomenon, a multiple classifier system (MCS) then combines the outputs of the SVMs. Another MCS combines the outputs of MCSs designed for the three neurological phenomena. Various configurations for combining the outputs of these MCSs are considered. A hybrid genetic algorithm (HGA) is proposed to simultaneously select the features, the values of the classifiers' parameters and the configuration for combining MCSs that yield the near optimal performance. Analysis of the data recorded from four able-bodied subjects shows a significant performance improvement over previous SBCIs.
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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 | 0.000 |
| 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 it