GameEmo-CapsNet: Emotion Recognition from Single-Channel EEG Signals Using the 1D Capsule Networks

Human emotion recognition with machine learning methods through electroencephalographic (EEG) signals has become a highly interesting subject for researchers. Although it is simple to define emotions that can be expressed physically such as speech, facial expressions, and gestures, it is more difficult to define psychological emotions that are expressed internally. The most important stimuli in revealing inner emotions are aural and visual stimuli. In this study, EEG signals using both aural and visual stimuli were examined and emotions were evaluated in both binary and multi-class emotion recognitions models. A general emotion recognition model was proposed for non-subject-based classification. Unlike in previous studies, a subject-based testing was performed for the first time in the literature. Capsule Networks, a new neural network model, has been developed for binary and multi-class emotion recognition. In the proposed method, a novel fusion strategy was introduced for binary-class emotion recognition and the model was tested using the GAMEEMO dataset. Binary-class emotion recognition achieved a classification accuracy which was 10% better than the classification performance achieved in other studies in the literature. Based on these findings, we suggest that the proposed method will bring a different perspective to emotion recognition.