Novel feature generation and classification for a 2-state Self-paced Brain Computer Interface system
Bibliographic record
Abstract
A two-state Self-paced Brain Computer Interface (SBCI) system enables users to control external devices at any time they desire by intentionally switching their mental states. In order to accurately control devices, distinguishing features representing different brain states must be present in the EEG signals. This paper introduces a novel feature generation method for EEG motor imagery data, based on Multivariate Empirical Mode Decomposition (MEMD), the Hilbert Transform and a phase synchronization index. Novelties of our approach are (1) MEMD is applied for decomposing an EEG signal into its narrow-band frequency components, from which features of the different brain states are calculated, and (2) a phase synchronization index is calculated without averaging over trials or time. We used a simple and fast classification scheme that employed an empirical threshold value obtained from the Receiver Operating Characteristic (ROC) curve of the training data. Applying the proposed method on only two mono-polar EEG channels, the SBCI system with the selected features yields a 93% True Positive rate, and a 5.8% False Positive rate using the BCI competition III data set Iva.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".