Emotional brain network decoded by biological spiking neural network

Hubo Xu; Kexin Cao; Hongguang Chen; Awuti Abudusalamu; Wei Wu; Yanxue Xue

doi:10.3389/fnins.2023.1200701

Emotional brain network decoded by biological spiking neural network

Front Neurosci. 2023 Jul 11:17:1200701. doi: 10.3389/fnins.2023.1200701. eCollection 2023.

Authors

Hubo Xu^#^{1

2}, Kexin Cao^#^{1

2}, Hongguang Chen^#³, Awuti Abudusalamu^{1

2}, Wei Wu⁴, Yanxue Xue^{1

5

6}

Affiliations

¹ National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China.
² Department of Pharmacology, School of Basic Medical Sciences, Peking University, Beijing, China.
³ NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Institute of Mental Health, Peking University Sixth Hospital, Beijing, China.
⁴ State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
⁵ Chinese Institute for Brain Research, Beijing, China.
⁶ Key Laboratory for Neuroscience, Ministry of Education/National Health Commission, Peking University, Beijing, China.

^# Contributed equally.

Abstract

Introduction: 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 and biological markers are still unclear, and the dynamic connection mechanism is also unknown.

Methods: 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.

Results: 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 α 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.

Discussion: The introduction of the self-backpropagation mechanism effectively improves the performance of the spiking neural network model. Different emotions exhibit distinct EEG networks and neuro-oscillatory-based biological markers. These emotional brain networks and biological markers may provide important hints for brain-computer interface technique exploration to help related brain disease recovery.

Keywords: affective computing; brain network; brain-computer interface; emotion; neural oscillation; neuroregulation; self-backpropagation; spiking neural network.

Grants and funding

This study was supported by grants from the National Natural Science Foundation of China−32161143022 and 81871046, Chinese National Programs for Brain Science and Brain-like Intelligence Technology—STI2030-Major Projects 2022ZD0214500, as well as Capital Clinical Application Research—Z221100007422052.