EEG Based Emotion Recognition Using Long Short Term Memory Network with Improved Rat Swarm Optimization Algorithm
Bibliographic record
Abstract
The automatic human Emotion Recognition (ER) based on Electroencephalography (EEG) signal has gained more attention among the researcher communities with a rapid growth of Human Computer Interaction (HCI).Most of the prior models have not focused on the context-information of the EEG signals.In this research manuscript, a novel automated model is implemented for improving ER using EEG signals.In the initial phase, the signals are acquired from an online database: Database for Emotion Analysis using Physiological Signal (DEAP).Then, the data denoising is carried-out by implementing Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) filters.These filters aim in eliminating the artifacts and noises in the acquired raw EEG signals, and further, the feature extraction is carried-out utilizing 20 statistical features that extracts discriminative feature information from the decomposed EEG signals.In the last phase, the Long Short Term Memory network (LSTM) is used for human ER as arousal or valence.Additionally, the optimal hyper-parameters of the LSTM network are selected by proposing the Improved Rat Swarm Optimization Algorithm (IRSOA).As denoted in the resulting and discussion section, the IRSOA-LSTM network achieved a mean accuracy of 84.89%, sensitivity of 86.95%, specificity of 86%, precision of 83.68%, and f1-score of 85.28% on the DEAP database.The simulation outcomes state that the proposed IRSOA-LSTM network is better than the existing machine-learning models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".