EEG Based Emotion Recognition Using Long Short Term Memory Network with Improved Rat Swarm Optimization Algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it