MRMR based feature selection for the classification of stress using EEG
Bibliographic record
Abstract
Mental stress is a social concern causing functional disability during work routines. The evaluation of stress using electroencephalogram signals is a topic of contemporary research. EEG provides several different features and the selection of appropriate features becomes a question. This study presents the utilization of feature selection using maximum relevance and minimum redundancy (MRMR) based on mutual information (MI) on the obtained features from electroencephalogram (EEG) signals during stress and control tasks. We moved forward in recording EEG during stress which was induced by taking up an eminent experimental model based on the Montreal Imaging Stress Task (MIST). The induced stress was endorsed by the performance during the task and the response of the subjects. The methodology consist of EEG feature extraction such as the absolute power and relative power, feature selection (MI) and classification using the support vector machine. The results of the proposed methodology showed a maximum accuracy of 93.75% and above 85% accuracy throughout the experiment. The performance is better than the existing studies in the literature. In conclusion, the MRMR criterion of feature selection using MI gives reliable and consistent results for the classification of stress.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".