Separable common spatio-spectral pattern algorithm for classification of EEG signals
Bibliographic record
Abstract
This paper proposes a novel method for extraction of discriminant spatio-spectral EEG features in motor imagery brain-computer interfaces. Considering a heteroscedastic binary classification setup, this method extracts the spatio-spectral features whose variance is maximized for one brain task and minimized for the other task. Therefore, our method can be considered as a spatio-spectral generalization of the conventional common spatial patterns (CSP) algorithm. In comparison to the similar solutions in the literature, such as filter-bank CSP (FBCSP) method, the proposed method benefits from joint processing of both spatial and spectral features, which improves the overall performance of the BCI while reducing its computational cost. Furthermore, our algorithm provides a simple measure that allows for ranking the discriminant power of extracted spatio-spectral features, which is not possible in FBCSP method. The experimental results demonstrate that the proposed method outperforms FBCSP for both raw EEG and preprocessed EEG data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.002 |
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".