An Integrated Framework for EEG-Based Emotion Recognition Using Jelly Fish Optimized Attention-LSTM Approach
Bibliographic record
Abstract
Stress has become a serious hazard to mental health in today's fast-paced environment, having a substantial impact on cognitive performance and general well-being. For psychiatric treatments and preventative healthcare, accurate and prompt stress identification is essential. In order to conquer this, the research presented uses a Jellyfish Optimized (JFO) Attention- Long Short-Term Memory (LSTM) model to present a reliable framework for EEG-based stress detection. The system starts by gathering EEG signals, which are then preprocessed using a band-pass filter to improve signal quality and eliminate noise. Orthogonal Wavelet Decomposition (OWD) is then used for feature extraction in order to efficiently capture both temporal and frequency domain properties. To ensure the best learning performance, these features are then fed into an Attention-based LSTM network, whose parameters are adjusted using the Jellyfish Optimization Algorithm (JOA). EEG signals are effectively classified into normal and stress states using the proposed approach. The performance of this model is validated via python software, which proves that the accuracy of stress recognition is greatly increased by this integrated method by accomplishing higher accuracy of (95.10%) with minimal execution time compare to the other classical approaches.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".