Emotion Discrimination through Electrode Network Connectivity Pattern Recognition

Real-time emotional state recognition from neural activities has great potentials, e.g., enabling closed-loop systems to treat neuropsychiatric disorders. Besides its utility in identifying epileptogenic brain regions, electrocorticography (ECoG) provides the opportunity to study brain activities during various emotional events. In this study, we aim to test if the second order statistics such as the electrode network connectivity are relevant in discriminating different emotions. Specifically, we adopt the statistical dependence as the connectivity measure and use sparse logistic regression for classification based on the connectivity features. The practical issues of limited samples and imbalanced data are addressed. We show that the data with the highest synchronization in the γ band provide the most competitive performance. The identified brain regions involved in the most discriminative connectivity patterns are consistent with the previous findings in the literature.