Brain network features based on theta-gamma cross-frequency coupling connections in EEG for emotion recognition

Abstract

Emotion recognition is a hot topic in the field of cognitive neuroscience and interpersonal interaction, and EEG feature selection is an important classification technology. At present, the mainstream method of EEG feature selection is to extract non-interactive features of channels such as power spectral density, or correlation features among local multi-channels. With the application of complex network graph theory, the connection network between multiple brain regions is gradually included in feature selection. However, in the process of brain network construction, most of the current connections adopt simple signal phase or amplitude synchronization. In recent years, it has been found that in the process of emotion, memory, learning, and other advanced cognitive processes, the large-scale connection and communication between the brain regions are mainly completed by the cross-frequency coupling(CFC) between the low-frequency phase and the high-frequency amplitude of neural oscillations. Based on this, we use CFC to update the connection mode, reconstruct the brain network, and extract features for emotion recognition research. Our results show that the EEG network based on CFC performs better than other EEG synchronization networks in emotion classification. Moreover, the combination of global features and local features of the brain network, as well as the dynamic network features with continuous time-windows, can effectively improve the accuracy of emotion recognition. This study provides a new idea of network connection for the follow-up study of emotion recognition and other advanced cognitive activities and makes a pioneering exploration for further research on feature selection of emotion recognition and related neural circuits at the brain network level of functional connectivity.