Enhancement of Hybrid Deep Neural Network Using Activation Function for EEG Based Emotion Recognition
Bibliographic record
Abstract
Deep Neural Network (DNN) is an advancing technology that improves our life by allowing machines to perform complex tasks.Hybrid Deep Neural Network (HDNN) is widely used for emotion recognition using EEG signals due to its increase in performance than DNN.Among several factors that improve the performance of the network, activation is an essential parameter that improves the model accuracy by introducing non-linearity into DNN.The activation function enables non-linear learning and solves the complexity between the input and output data.The selection of the activation function depends on the type of data that is used for computation.This paper investigates the model performance with respect to various activation functions like ReLU, ELU, and Leaky ReLU on a hybrid CNN with a Bi-LSTM and CNN model for emotion recognition.The model was tested on the DEAP dataset which is an emotion dataset that uses physiological and EEG signals.The experimental results have shown that the model has improved accuracy when the ELU function is used.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".