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

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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https://ijci.uoitc.edu.iq/index.php/ijci/article/view/545

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