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Group-member selection for RSVP-based collaborative brain-computer interfaces.

Yuan Si1,2, Zhenyu Wang1, Guiying Xu1,2

  • 1Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China.

Frontiers in Neuroscience
|September 5, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a group-member selection strategy for collaborative brain-computer interface (cBCI) systems. The method optimizes group performance and reduces costs by intelligently pairing users in rapid serial visual presentation (RSVP) tasks.

Keywords:
brain-computer interfaces (BCIs)collaborative brain-computer interfaces (cBCIs)electroencephalogram (EEG)event-related potentials (ERP)group-member selectionrapid serial visual presentation (RSVP)

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Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Brain-computer interface (BCI) systems, particularly those using rapid serial visual presentation (RSVP), are vital for image detection.
  • Collaborative BCI (cBCI) enhances single-user performance by fusing electroencephalogram (EEG) data from multiple users.
  • Optimizing group modes in multi-user cBCI is crucial for improving performance and reducing costs, but key factors remain unclear.

Purpose of the Study:

  • To propose and validate a group-member selection strategy for optimizing group modes in RSVP-based cBCI systems.
  • To enhance collaborative performance and reduce system costs by intelligently selecting group members.
  • To address limitations in single-trial event-related potential (ERP) detection within multi-user cBCI.

Main Methods:

  • Introduced Maximum Individual Capability and Maximum Collaborative Capability (MIMC) for optimal pair selection.
  • Employed Sequential Forward Floating Selection (SFFS) combined with MIMC to select optimal sub-groups, reducing hardware and labor costs.
  • Utilized Hierarchical Discriminant Component Analysis (HDCA) for within-session classification and Euclidean Space Data Alignment (EA) for cross-session analysis.

Main Results:

  • The proposed MIMC strategy significantly outperformed random grouping and other selection methods in a two-user matching task, showing higher AUC and TPR, and lower FPR.
  • SFFS with MIMC demonstrated an effective trade-off between maintaining classification performance and reducing the number of users.
  • Validation was performed on a public RSVP-based cBCI dataset.

Conclusions:

  • The MIMC strategy effectively optimizes group modes in RSVP-based multi-user cBCI systems.
  • Enhanced classification performance was achieved in two-user matching tasks.
  • The approach successfully reduces redundant information by selecting optimal sub-groups, leading to more efficient cBCI systems.