ArticleEntropy (Basel, Switzerland)2021
EEG-Based Emotion Recognition by Exploiting Fused Network Entropy Measures of Complex Networks across Subjects.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Cross-Group EEG Emotion Recognition Based on Phase Space Reconstruction Topology.Entropy (Basel, Switzerland) · 2025Article
- Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier.Brain informatics · 2024Article
- Multidimensional Feature in Emotion Recognition Based on Multi-Channel EEG Signals.Entropy (Basel, Switzerland) · 2022Article
- Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition.Entropy (Basel, Switzerland) · 2022Article
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5 authors.
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Abstract
It is well known that there may be significant individual differences in physiological signal patterns for emotional responses. Emotion recognition based on electroencephalogram (EEG) signals is still a challenging task in the context of developing an individual-independent recognition method. In our paper, from the perspective of spatial topology and temporal information of brain emotional patterns in an EEG, we exploit complex networks to characterize EEG signals to effectively extract EEG information for emotion recognition. First, we exploit visibility graphs to construct complex networks from EEG signals. Then, two kinds of network entropy measures (nodal degree entropy and clustering coefficient entropy) are calculated. By applying the AUC method, the effective features are input into the SVM classifier to perform emotion recognition across subjects. The experiment results showed that, for the EEG signals of 62 channels, the features of 18 channels selected by AUC were significant (
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