Research Area:  Machine Learning

Abstract:

Emotion recognition, as a challenging and active research area, has received considerable awareness in recent years. In this study, an attempt was made to extract complex network features from electroencephalogram (EEG) signals for emotion recognition. We proposed a novel method of constructing forward weighted horizontal visibility graphs (FWHVG) and backward weighted horizontal visibility graphs (BWHVG) based on angle measurement. The two types of complex networks were used to extract network features. Then, the two feature matrices were fused into a single feature matrix to classify EEG signals. The average emotion recognition accuracies based on complex network features of proposed method in the valence and arousal dimension were 97.53% and 97.75%. The proposed method achieved classification accuracies of 98.12% and 98.06% for valence and arousal when combined with time-domain features.

Keywords:  
EEG-Based Emotion Recognition
Weighted Horizontal Visibility Graph
electroencephalogram (EEG) signals
Deep Learning
Machine Learning

Author(s) Name:  Tianjiao Kong,Jie Shao,Jiuyuan Hu,Xin Yang,Shiyiling Yang and Reza Malekian

Publisher name:  MDPI

DOI:  10.3390/s21051870

Volume Information:  Volume 21 Issue 5

Paper Link:   https://www.mdpi.com/1424-8220/21/5/1870