Journal
SENSORS
Volume 21, Issue 5, Pages -Publisher
MDPI
DOI: 10.3390/s21051870
Keywords
emotion recognition; EEG; directed weighted horizontal visibility graph; feature fusion
Funding
- Open Research Fund of Key Laboratory of Ministry of Education [UASP2001]
- Fundamental Research Funds for the Central Universities [2242020k30044]
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In this study, complex network features were extracted from EEG signals for emotion recognition through the construction of two types of complex networks and fusion of feature matrices. The proposed method achieved high emotion recognition accuracies in valence and arousal dimensions, and further improved classification accuracies when combined with time-domain features.
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.
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