Journal
NEURAL NETWORKS
Volume 116, Issue -, Pages 257-268Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2019.04.003
Keywords
Electroencephalography; Brain activity; Emotion recognition; Hypergraph; Decoding model
Funding
- New Energy and Industrial Technology Development Organization (NEDO)
- Post-K Project from Ministry of Education, Sports, Science and Technology (MEXT)
- JSPS KAKENHI Grant [JP19H04180, 17H06310]
- Grants-in-Aid for Scientific Research [17H06310] Funding Source: KAKEN
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Emotion plays a vital role in human health and many aspects of life, including relationships, behaviors and decision-making. An intelligent emotion recognition system may provide a flexible method to monitor emotion changes in daily life and send warning information when unusual/unhealthy emotional states occur. Here, we proposed a novel unsupervised learning-based emotion recognition system in an attempt to decode emotional states from electroencephalography (EEG) signals. Four dimensions of human emotions were examined: arousal, valence, dominance and liking. To better characterize the trials in terms of EEG features, we used hypergraph theory. Emotion recognition was realized through hypergraph partitioning, which divided the EEG-based hypergraph into a specific number of clusters, with each cluster indicating one of the emotion classes and vertices (trials) in the same cluster sharing similar emotion properties. Comparison of the proposed unsupervised learning-based emotion recognition system with other recognition systems using a well-known public emotion database clearly demonstrated the validity of the proposed system. (C) 2019 Elsevier Ltd. All rights reserved.
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