Baswa, I., Abdulrazzaq, M., Ibrahim, A. (2025). Multiclass EEG Classification Using Recurrent Neural Network and Feature Selection with PSO Algorithm for Emotion Recognition. , 1(1), 22-32.
Iman Mohammed Ayden Baswa; Mohammed Majid Abdulrazzaq; Abdullahi Abdu Ibrahim. "Multiclass EEG Classification Using Recurrent Neural Network and Feature Selection with PSO Algorithm for Emotion Recognition". , 1, 1, 2025, 22-32.
Baswa, I., Abdulrazzaq, M., Ibrahim, A. (2025). 'Multiclass EEG Classification Using Recurrent Neural Network and Feature Selection with PSO Algorithm for Emotion Recognition', , 1(1), pp. 22-32.
Baswa, I., Abdulrazzaq, M., Ibrahim, A. Multiclass EEG Classification Using Recurrent Neural Network and Feature Selection with PSO Algorithm for Emotion Recognition. , 2025; 1(1): 22-32.

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

Kirkuk Journal of Engineering Science
Volume 1, Issue 1, September 2025, Pages 22-32    PDF (649.27 K)
Document Type: Original Article
Authors
Iman Mohammed Ayden Baswa* 1; Mohammed Majid Abdulrazzaq2; Abdullahi Abdu Ibrahim3
1Department of Information Technology, Altinbas University, Istanbul, Turkey
2Department of Computer Engineering, University of Anbar, Anbar, Iraq
3Department 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
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