Document Type : Original/Review Paper

Authors

• Yashar Chehardahcherikigheisari ¹
• Hadi Grailu ²

¹ Master's student

² Assistant Professor Faculty of Electrical Engineering

Abstract

Human emotion recognition based on electroencephalogram signals remains a significant challenge in computational neuroscience and artificial intelligence. Convolutional neural networks have been widely adopted to address this challenge due to their strong capabilities in feature extraction and representation learning. Empirical tuning of hyperparameters, however, is often inefficient and prone to suboptimal solutions. To overcome this limitation, the Grey Wolf Optimizer and Whale Optimization Algorithm—two swarm intelligence–based metaheuristic methods—were employed to refine the architecture and learning hyperparameters of a baseline convolutional neural network. Performance was evaluated on the SEED EEG dataset for emotion recognition. Experimental results showed that the optimized model achieved an accuracy of 86% compared to 83% for the baseline, with the confusion matrix confirming reduced misclassification errors and improved recognition of emotional states. Beyond accuracy, the optimization process also lowered the number of trainable parameters and computational overhead, thereby enhancing efficiency. These findings highlight that swarm intelligence–based methods provide superior exploration and exploitation capabilities, enabling systematic hyperparameter tuning and delivering a balanced engineering approach that combines predictive performance with resource efficiency.

Keywords

• Brain signals
• Emotion recognition
• EEG signal processing
• Metaheuristic algorithm

Main Subjects

• F.2.7. Optimization