AUTHOR=Xu Hubo , Cao Kexin , Chen Hongguang , Abudusalamu Awuti , Wu Wei , Xue Yanxue 

TITLE=Emotional brain network decoded by biological spiking neural network

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1200701

DOI=10.3389/fnins.2023.1200701

ISSN=1662-453X

ABSTRACT=Emotional disorders are essential manifestations of many neurological and psychiatric diseases. Nowadays, researchers try to explore bi-directional brain-computer interface techniques to help the patients. However, the related functional brain areas are still unclear, and the dynamic connection mechanism is also unknown. To find effective regions related to different emotion recognition and intervention, our research focuses on finding emotional EEG brain networks using spiking neural network algorithm with binary coding. We collected EEG data while human participants watched emotional videos (fear, sadness, happiness, and neutrality), and analyzed the dynamic connections between the electrodes and the biological rhythms of different emotions. The analysis has shown that the local high-activation brain network of fear and sadness is mainly in the parietal lobe area. The local high-level brain network of happiness is in the prefrontal-temporal lobe-central area. Furthermore, the β frequency band could effectively represent negative emotions, while the α frequency band could be used as a biological marker of happiness. The decoding accuracy of the three emotions reached 86.36%, 95.18%, and 89.09%, respectively, fully reflecting the excellent emotional decoding performance of the spiking neural network with self-backpropagation. Thus, these emotional brain networks and features may provide important hints for brain-computer interface technique exploration to help related brain disease recovery.