Research progress and prospect of collaborative brain-computer interface for group brain collaboration.
10.7507/1001-5515.202007059
- Author:
Lixin ZHANG
1
;
Xiaocui CHEN
1
;
Long CHEN
2
;
Bin GU
1
;
Zhongpeng WANG
1
;
Dong MING
1
Author Information
1. Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, P.R.China.
2. Biomedical Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P.R.China.
- Publication Type:Journal Article
- Keywords:
collaborative brain-computer interface;
decision fusion;
feature fusion;
group size;
motor imagery
- MeSH:
Brain;
Brain-Computer Interfaces;
Electroencephalography;
Humans;
Imagery, Psychotherapy;
Imagination
- From:
Journal of Biomedical Engineering
2021;38(3):409-416
- CountryChina
- Language:Chinese
-
Abstract:
As the most common active brain-computer interaction paradigm, motor imagery brain-computer interface (MI-BCI) suffers from the bottleneck problems of small instruction set and low accuracy, and its information transmission rate (ITR) and practical application are severely limited. In this study, we designed 6-class imagination actions, collected electroencephalogram (EEG) signals from 19 subjects, and studied the effect of collaborative brain-computer interface (cBCI) collaboration strategy on MI-BCI classification performance, the effects of changes in different group sizes and fusion strategies on group multi-classification performance are compared. The results showed that the most suitable group size was 4 people, and the best fusion strategy was decision fusion. In this condition, the classification accuracy of the group reached 77%, which was higher than that of the feature fusion strategy under the same group size (77.31%
