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A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual

Xiyu Song¹, Ying Zeng^1,2, Li Tong¹

¹Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China.

Computational Intelligence and Neuroscience

|January 28, 2022

Summary

This summary is machine-generated.

This study introduces a new collaborative brain-computer interface (cBCI) framework using mutual learning domain adaptation networks (MLDANet) to improve dynamic visual target detection. The MLDANet-cBCI significantly enhances group detection performance and individual network capabilities.

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Area of Science:

Neuroscience
Computer Science
Human-Computer Interaction

Background:

Collaborative brain-computer interfaces (cBCIs) offer performance advantages for dynamic visual target detection.
Existing cBCIs lack dynamic information interaction and learning guidance among multiple agents.
Current multi-mind fusion modes are static and unidirectional, limiting collaborative potential.

Purpose of the Study:

To propose a novel cBCI framework enhancing group detection performance for dynamic visual targets.
To develop a mutual learning domain adaptation network (MLDANet) for improved information interaction and learning.
To overcome the limitations of static, unidirectional multi-mind fusion in existing cBCIs.

Main Methods:

Developed a Mutual Learning Domain Adaptation Network (MLDANet) as the core of the cBCI framework.
Integrated information interaction, dynamic learning, and individual transferring abilities within MLDANet.
Established a dynamic interactive learning mechanism between individual networks and collaborative decision-making at the neural decision level, using P3-sSDA as the base network.

Main Results:

The MLDANet-cBCI framework achieved superior group detection performance for dynamic visual targets.
The mutual learning strategy within MLDANet enhanced the detection abilities of individual networks.
With three collaborators, MLDANet-cBCI showed F1 score improvements of 0.12 for collaborative detection and 0.19 for individual networks compared to multi-classifier cBCIs.

Conclusions:

The proposed MLDANet-cBCI framework surpasses traditional multi-mind collaborative modes.
This novel framework demonstrates superior group detection performance for dynamic visual targets.
The findings hold significant implications for the practical applications of multi-mind collaboration in BCI technology.