Temporal and spatial variability of dynamic microstate brain network based on event-related potential analysis in underwater target recognition task

• Published in: Physiology & behavior Vol. 299; p. 114971
• Main Authors: Zhang, Jiaqi, Shi, Zhangsong, Xu, Huihui, Zhang, Ning, Gao, Junfeng
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2025
• Subjects: Adult; Brain - physiology; Dynamic brain network; Electroencephalogram; Electroencephalography; Evoked Potentials - physiology; Female; Humans; Male; Microstates; Oddball paradigm; Recognition, Psychology - physiology; Underwater target recognition; Young Adult; Microstates; Oddball paradigm; Dynamic brain network; Electroencephalogram; Underwater target recognition
• ISSN: 0031-9384; 1873-507X; 1873-507X
• DOI: 10.1016/j.physbeh.2025.114971

•A new dynamic brain network window partition method is defined.•The brain's spatiotemporal interaction network varies among different targets.•State switching causes changes in the brain's information transmission network.•This theory provides a new method for underwater target detection. Underwater target detection is closely related to ocean research, underwater navigation, and marine fisheries. However, due to the interference of underwater environment, rapid recognition of underwater targets is still a difficult task. This study proposes an underwater target recognition system based on dynamic brain networks to address this issue. A dynamic brain network accurately represents a brain functional network by reflecting the state transitions over time. This study proposed a method combining the Event-Related Potential (ERP) analysis, microstates, and dynamic brain networks to investigate the spatiotemporal variability of the brain during underwater target recognition tasks. The electroencephalogram (EEG) data from 45 subjects were analyzed, and the overall change matrix of the dynamic brain network as a feature. The method achieved an average classification accuracy of 96.19 % across all the subjects. This approach demonstrated the efficacy of constructing dynamic brain network features based on the ERP microstates to identify the EEG signals across various tasks. Furthermore, it could offer new insights for the development of the underwater target recognition technology in the future.