Brain Rhythm Sequencing and Its Application for EEG-based Emotion Recognition
Bibliographic record
Abstract
A technique based on five brain rhythms (δ, θ, α, β, and γ) presented in a sequential format has been proposed for Electroencephalography (EEG)-based emotion recognition. Its production employs the prominent rhythm having maximum instantaneous power at each 0.2 s timestamp. For this purpose, smoothed pseudo Wigner-Ville distribution (RSPWVD) method is used. In total, 32 subjects from the emotional EEG database (DEAP) are applied for experimental validation, and for each subject, 640 rhythmic features derived from the time-related properties are extracted from 32 channels. After performance evaluation through support vector machine (SVM) classifier, the one that offers the highest accuracy can be found and then denoted as the optimal feature. By this means, the accuracies of EEG-based emotion recognition accomplish 78.36 ± 5.56% for arousal and 75.78 ± 3.73% for valence. Therefore, the results disclosed that a single optimal feature from a representative channel is competent to recognize the emotional 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".