Stress Detection and Audio-Visual Stimuli Classification From Electroencephalogram
Bibliographic record
Abstract
Electroencephalogram (EEG) is the graphical representation of Brain’s electrical activity. Mental stress can be detected in many ways and EEG is one of them. Regular mental stress gives rise to many mental disorders and it may cause various physiological and psychological diseases. As a result, early-stage detection of stress is very important. In this research, brain activity was recorded through EEG headset during inducing different levels of stress from audio-visual stimulus. Again, for better interaction between humans and machines, it is essential to analyze the power spectrum of the brain in response to different audio and visual stimulus. To better evaluate visual and auditory stress, an automated system is designed to differentiate among various audio and visual evoked potentials. This may further help for designing different assistive devices for the people having visual and hearing disability. In this paper, we proposed a framework to classify different levels of stress in response to audio and visual stimuli and also classified between these two stimuli by analyzing EEG signals. Raw EEG data was collected in lab environment and the necessary pre-processing steps were applied for denoising. By extracting robust features from the denoised audio and visual data, binary and multi-level stress were classified. A binary classification between audio and visual stimuli was also successfully done in this research. We achieved highest accuracy for binary stress classification 97.14% from visual stimuli, whereas we achieved 94.51% accuracy for auditory stimuli. Again, we achieved the accuracy for four level stress classification 89.59% for visual stimuli and 82.63% for audio stimuli.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".