Kirkuk Journal of Engineering Science

Multiclass EEG Classification Using Recurrent Neural Network and Feature Selection with PSO Algorithm for Emotion Recognition

Document Type : Original Article

Authors

Iman Mohammed Ayden Baswa 1
Mohammed Majid Abdulrazzaq 2
Abdullahi Abdu Ibrahim 3

1 Department of Information Technology, Altinbas University, Istanbul, Turkey

2 Department of Computer Engineering, University of Anbar, Anbar, Iraq

3 Department of Electrical and Computer Engineering, Collage of Engineering, Altinbas University, Istanbul, 34000, Turkey

Abstract
The recognition of emotion through Electroencephalogram data is essential for human computer interaction, mental health tracking, and emotion sensing computing. This paper integrates Recurrent Neural Networks (RNN) and a feature selection technique based on Particle Swarm Optimization (PSO) method within multiclass EEG classification paradigm. The method increases classification efficiency by feature selection from EEG signals which reduces the amount of computations needed in the first place. The span of emotion identification is aided by RNN model’s power in capturing the sequential dependencies of EEG signals. The experiments conducted demonstrated that the proposed solution performs better than traditional approaches in terms of accuracy and effectiveness. This opens avenues for the more precise and immediate applications of affective computing by providing a comprehensive solution for EEG emotion recognition.

Keywords

EEG classification
Emotion recognition
Recurrent neural network
Feature selection
Particle swarm optimization
Affective computing
Subjects
Kirkuk Journal of Engineering Science
Volume 1, Issue 1
Summer 2025
Pages 22-32

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