Improving the discrimination of hand motor imagery via virtual reality based visual guidance.
While research on the brain-computer interface (BCI) has been active in recent years, how to get high-quality electrical brain signals to accurately recognize human intentions for reliable communication and interaction is still a challenging task. The evidence has shown that visually guided motor imagery (MI) can modulate sensorimotor electroencephalographic (EEG) rhythms in humans, but how to design and implement efficient visual guidance during MI in order to produce better event-related desynchronization (ERD) patterns is still unclear. The aim of this paper is to investigate the effect of using object-oriented movements in a virtual environment as visual guidance on the modulation of sensorimotor EEG rhythms generated by hand MI. To improve the classification accuracy on MI, we further propose an algorithm to automatically extract subject-specific optimal frequency and time bands for the discrimination of ERD patterns produced by left and right hand MI. The experimental results show that the average classification accuracy of object-directed scenarios is much better than that of non-object-directed scenarios (76.87% vs. 69.66%). The result of the t-test measuring the difference between them is statistically significant (p = 0.0207). When compared to algorithms based on fixed frequency and time bands, contralateral dominant ERD patterns can be enhanced by using the subject-specific optimal frequency and the time bands obtained by our proposed algorithm. These findings have the potential to improve the efficacy and robustness of MI-based BCI applications.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
Shuang
S
Liang
School of Nursing, The Hong Kong Polytechnic University, Hong Kong.
Kup-Sze
KS
Choi
School of Nursing, The Hong Kong Polytechnic University, Hong Kong; School of Medicine, Shenzhen University, Shenzhen, Guangdong, China. Electronic address: jing.qin@szu.edu.cn.
Jing
J
Qin
The Department of Computer Science, Caritas Institute of Higher Education, Hong Kong.
Wai-Man
WM
Pang
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
Qiong
Q
Wang
The Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong.
Pheng-Ann
PA
Heng
D000328:Adult; D005260:Female; D006225:Hand / Q000502:physiology*; D006801:Humans; D008297:Male; D011597:Psychomotor Performance; D014584:User-Computer Interface
Brain–computer interface; Event-related desynchronization; Hand motor imagery; Subject-specific frequency and time bands; Virtual reality; Visual guidance