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

  • 0College of Weaponry Engineering, Naval University of Engineering, Wuhan 430030, China.
Physiology & Behavior +

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June 7, 2025

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June 7, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel underwater target recognition system using dynamic brain networks derived from electroencephalogram (EEG) data. The system achieved high accuracy, offering new insights for marine technology development.

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.

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