Hybrid Optimized Feature Selection and Deep Learning Method for Emotion Recognition That Uses EEG Data

Introduction: This study represents an important development in human–machine interactions. It aims to utilize the potential of electroencephalograms (EEGs) in recognizing emotions, which is a complex and variable task. This study presents a complete framework for enhancing emotion identification. I...

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Bibliographic Details
Main Authors:	asmaa Bashar Hmaza, Rajaa K. Hasoun
Format:	Article
Language:	Arabic
Published:	University of Information Technology and Communications 2024-03-01
Series:	Iraqi Journal for Computers and Informatics
Subjects:	: : human interaction, gated recurrent unit (gru), electroencephalogram (eeg), recurrent neural networks (rnn) , emotion identification, long short-term memory (lstm) , particle swarm optimization (pso).
Online Access:	https://ijci.uoitc.edu.iq/index.php/ijci/article/view/545
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Hybrid Optimized Feature Selection and Deep Learning Method for Emotion Recognition That Uses EEG Data

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