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Deep Learning Strategies for Analyzing EEG Signals in Emotion Recognition
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Author(s): K. V. Kavitha (Department of Computer Science and Engineering, Annamalai University, India), L. R. Sudha (Department of Computer Science and Engineering, Annamalai University, India)and J. S. Jayasudha (Department of Computer Science, Central University, India)
Copyright: 2025
Pages: 24
Source title:
Signal and Image Processing Techniques for Defense, Security, and Healthcare
Source Author(s)/Editor(s): B. Omkar Lakshmi Jagan (Vignan's Institute of Information Technology, India), Amrit Mukherjee (University of South Bohemia, Czech Republic), Thayyaba Khatoon Mohammed (Malla Reddy University, India)and Vustikayala Sivakumar Reddy (Malla Reddy University, India)
DOI: 10.4018/979-8-3693-3840-7.ch005
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Abstract
Emotions are vital in human communication, and EEG signals have become a potential method for detecting emotional states in real-time. Nevertheless, attaining a high level of precision in the identification of emotions continues to be a significant obstacle. Due to the emergence of deep learning techniques, there has been a growing desire to utilize these approaches for acquiring advanced characteristics in EEG emotion identification. This research makes a valuable contribution to the field of Human-Computer Interaction (HCI) medical applications. The purpose of this chapter is to offer a thorough examination of EEG emotion identification, specifically emphasizing the use of deep learning techniques. The initial focus of our study is a comprehensive exploration of the underlying principles and ideas presented in the existing literature. The primary objective of our research is to enhance the efficiency of emotion recognition models to provide more precise outcomes. This chapter offers insights into the most recent breakthroughs and potential areas for further inquiry.
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