Akademik Veri Yönetim Sistemi
SIGNAL IMAGE AND VIDEO PROCESSING, cilt.18, sa.10, ss.6903-6910, 2024 (SCI-Expanded)
Multi-channel Electroencephalogram (EEG) based emotion recognition is focused on several analysis of frequency bands of the acquired signals. In this paper, spectral properties appeared on five EEG bands (delta\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta $$\end{document}, theta\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta $$\end{document}, alpha\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document}, beta\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta $$\end{document}, gamma\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma $$\end{document}) and gated transformer network (GTN) based emotion recognition using EEG signal are proposed. Spectral energies and differential entropies of 62-channel signals are converted to 3D (sequence-channel-trial) form to feed the GTN. The GTN with enhanced gated two tower based transformer architecture is fed by 3D sequences extracted from SEED and SEED-IV emotional datasets. 15 participants' states in session 1-3 are evaluated using the proposed GTN based sequence classification, and the results are repeated by 3x\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3\small \times $$\end{document} shuffling. Totally, 135 times training and testing are performed on each dataset, and the results are presented. The proposed GTN model achieves mean accuracy rates of 98.82% on the SEED dataset and 96.77% on the SEED-IV dataset for three and four emotional state recognition tasks, respectively. The proposed emotion recognition model can be employed as a promising approach for EEG emotion recognition.