Temporal and spatial variability of dynamic microstate brain network based on event-related potential analysis in...

Area of Science:

• Neuroscience
• Marine Biology
• Signal Processing

Background:

• Underwater target detection is crucial for ocean research, navigation, and fisheries.
• Environmental interference poses challenges to rapid and accurate underwater target recognition.
• Existing methods struggle with the complexity and dynamic nature of underwater environments.

Purpose of the Study:

• To develop an advanced underwater target recognition system.
• To investigate the spatiotemporal brain activity during underwater target recognition tasks.
• To leverage dynamic brain networks for improved EEG signal analysis.

Main Methods:

• Utilized electroencephalogram (EEG) data from 45 subjects performing underwater target recognition.
• Combined Event-Related Potential (ERP) analysis, microstates, and dynamic brain network construction.
• Extracted the overall change matrix of the dynamic brain network as a distinguishing feature.

Main Results:

• The proposed system achieved an average classification accuracy of 96.19% across all subjects.
• Demonstrated the effectiveness of dynamic brain network features derived from ERP microstates.
• Successfully identified EEG signals related to underwater target recognition tasks.

Conclusions:

• Dynamic brain networks offer a robust method for analyzing complex EEG data in underwater target recognition.
• This approach provides a promising foundation for future advancements in underwater target recognition technology.
• The findings highlight the potential of neuro-imaging techniques in marine applications.