Abstract
Electroencephalogram (EEG) data depict various emotional states and reflect brain activity. There has been increasing interest in EEG emotion recognition in brain–computer interface systems (BCIs). In the World Robot Contest (WRC), the BCI Controlled Robot Contest successfully staged an emotion recognition technology competition. Three types of emotions (happy, sad, and neutral) are modeled using EEG signals. In this study, 5 methods employed by different teams are compared. The results reveal that classical machine learning approaches and deep learning methods perform similarly in offline recognition, whereas deep learning methods perform better in online cross-subject decoding.
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