Ternary ECOC classifiers coupled with optimized spatio-spectral patterns for multiclass motor imagery classification
Bibliographic record
Abstract
Modeling and representation of multiple tasks from brain signals is a crucial task in Electroencephalogram (EEG) based Brain-Computer Interfaces (BCIs). The motivation of this work comes from the need for a BCI system, intended to operate in real world scenarios, to discriminate multiple tasks and activities simultaneously. In this regard, the paper proposes a novel multi-class EEG-based BCI system via utilization of error correcting output coding (ECOC) classifiers. To best of our knowledge, the ECOC classifiers have not ever been applied to the EEG classification problems. In the ECOC method, the classification problem is modeled as communication over a noisy channel where the miss-classification error is corrected by error correcting techniques borrowed from information theory. In this work, we propose to utilize a modified version of the ECOC classifiers adopted to EEG classification problems which deploys ternary class codewords. Therefore, we analyze more combinations of the classes and greater number of classifiers vote for the final result. The proposed classifier is coupled with a Bayesian framework to compute the optimized spatio-spectral filters to extract the most discriminative feature sets of different classes. The proposed framework is applied to a motor imagery classification problem and evaluated over BCI Competition IV-2a dataset where the results indicate a noticeable enhancement over other methods developed for multi-class EEG classification.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".