An unsupervised EEG decoding system for human emotion recognition.

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.