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Graph Convolutional Neural Network Based Emotion Recognition with Brain Functional Connectivity Network

Authors :
Pengzhi Gao
Xiangwei Zheng
Tao Wang
Yuang Zhang
Source :
International Journal of Crowd Science, Vol 8, Iss 4, Pp 195-204 (2024)
Publication Year :
2024
Publisher :
Tsinghua University Press, 2024.

Abstract

Emotion recognition plays an important role in Human Computer Interaction (HCI) and the evaluation of human behavior based on emotional state is an important research topic. The purpose of emotion recognition is to automatically identify human’s emotional states by analyzing physiological or non-physiological signals. The conventional emotion classification methods cannot comprehensively leverage global and local features which are extracted from Electroencephalogram (EEG) signal generated after being stimulated. Therefore, we propose the graph convolutional neural network based emotion recognition with brain functional connectivity network (GERBN). Firstly, raw EEG data of the public DEAP and SEED datasets is preprocessed and adopted in this study. Secondly, emotion-related brain functional connection pattern is constructed using Phase-Locking Value (PLV) adjacency matrix to measure connectivity between the signals of different EEG channels according to phase synchronization. A novel graph structure is constructed where the EEG electrode channels are defined as the vertex, and the edge is strong connection of the binary brain network. Thirdly, the GERBN model that includes six layers is designed to classify and recognize emotional states on the two-dimensional emotional models of valence and arousal. Finally, extensive experiments are conducted on DEAP and SEED datasets. Experimental results demonstrate that the proposed method can improve classification accuracies, in which average accuracies of 80.43% and 88.47% on DEAP are attained on valence and arousal dimensions, respectively. On the SEED dataset, the accuracy reaches 92.37% higher than some of the other methods.

Details

Language :
English
ISSN :
23987294
Volume :
8
Issue :
4
Database :
Directory of Open Access Journals
Journal :
International Journal of Crowd Science
Publication Type :
Academic Journal
Accession number :
edsdoj.509a03ca1f4444f685a4b5893d3a5f5d
Document Type :
article
Full Text :
https://doi.org/10.26599/IJCS.2024.9100022