DETECTION OF HUMAN PSYCHOLOGICAL STRESS WITH DEEP CONVOLUTIONAL NEURAL NETWORK USING DIFFERENT CRITERIAS FOR FEATURE SELECTION ON BASIS OF CONFIDENCE VALUE OF PAIRED t-TEST
Bibliographic record
Abstract
Psychological stress has begun to be a social concern and is a major source of dysfunction in day-to-day life.Many neuropsychology ailments are implicated due to chronic stress.Sadness, seizures, cardiac arrest, and stroke are all increased by stress.The key prey of psychological stress is the human cerebrum, as revealed by a recent study of neuroscience, as the human cerebrum has the ability to recognize whether circumstances are terrifying or stressful.As an example, if an objective method for recognizing stress is used while considering the human cerebrum, the associated negative effect could be significantly reduced.As a result, a deep convolutional neural network involving raw EEG signals and a study of stressed subjects is suggested in this paper.Stress is induced in the experimental setting by using a mental arithmetic task (MAT) tool based on an established prototype, the Montreal imaging stress task (MIST).Task performance and subjective feedback confirmed the initiation of stress.EEG feature extraction, feature selection (paired t-test and four criteria), and classification (Deep Convolutional Neural Network) are all part of the proposed system.The proposed work gives good results when criteria 1, 2, and 3 are chosen as compared to criteria 4. Maximum accuracy is obtained from the Change in Power feature through the AF7 channel.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".