Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.neunet.2019.04.003 | Neural Networks |
Keywords | Field | DocType |
Electroencephalography,Brain activity,Emotion recognition,Hypergraph,Decoding model | Arousal,Emotion recognition,Hypergraph,Speech recognition,Unsupervised learning,Artificial intelligence,Decoding methods,Electroencephalography,Machine learning,Mathematics,Human health | Journal |
Volume | Issue | ISSN |
116 | 1 | 0893-6080 |
Citations | PageRank | References |
3 | 0.39 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhen Liang | 1 | 3 | 0.73 |
| Shigeyuki Oba | 2 | 290 | 27.68 |
| Shin Ishii | 3 | 239 | 34.39 |