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Optimization of Task Allocation for Collaborative Brain-Computer Interface Based on Motor Imagery.

Bin Gu1, Minpeng Xu1,2, Lichao Xu2

  • 1Neural Engineering & Rehabilitation Laboratory, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.

Frontiers in Neuroscience
|July 19, 2021
PubMed
Summary
This summary is machine-generated.

A new division-of-work strategy for collaborative brain-computer interfaces (cBCIs) significantly improved classification accuracy. This approach optimizes task allocation, enhancing system performance and reducing collaborator workload.

Keywords:
collaborative brain-computer interfacescommon-workdivision-of-workmotor imagerytask allocation

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Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Collaborative brain-computer interfaces (cBCIs) enhance credibility by decoding concurrent brain signals.
  • Current cBCIs often use a common-work strategy where all collaborators perform identical tasks.
  • The effectiveness of a division-of-work strategy in cBCIs remains largely unexplored.

Purpose of the Study:

  • To investigate a novel task allocation scheme for cBCIs using a division-of-work strategy.
  • To compare the classification accuracy of the division-of-work strategy against the traditional common-work strategy.
  • To assess the impact of task distribution on cBCI performance and individual workload.

Main Methods:

  • Developed an electroencephalograph (EEG)-based cBCI with six distinct motor imagery (MI) tasks.
  • Implemented a common-work strategy where all five subjects performed the same task simultaneously.
  • Introduced a division-of-work strategy, assigning unique subsets of tasks to each subject, ensuring the union covered all six tasks.
  • Categorized division-of-work into '2 Tasks' through '5 Tasks' based on subset size.
  • Validated strategies using pseudo-online classification on EEG data from 19 subjects performing six MI tasks.

Main Results:

  • The classification accuracy of the cBCI exhibited a peak performance with the division-of-work strategy at '4 Tasks' (four instructions per subject).
  • The '4 Tasks' division-of-work strategy achieved significantly higher classification accuracy (72.29 ± 4.43%) compared to the common-work strategy (58.53 ± 4.36%).
  • Accuracy performance generally increased with task distribution, peaking when collaborators handled four tasks each.

Conclusions:

  • The proposed division-of-work task allocation strategy demonstrably enhances cBCI classification performance.
  • This strategy effectively improves system accuracy while simultaneously reducing the workload for individual collaborators.
  • Optimizing task distribution is crucial for maximizing the efficiency and effectiveness of collaborative brain-computer interfaces.