Paper Info
Title
Improving the discrimination of hand motor imagery via virtual reality based visual guidance.
Abstract
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. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
Year
DOI
Venue
2016
10.1016/j.cmpb.2016.04.023
Computer Methods and Programs in Biomedicine
Keywords
Field
DocType
Brain–computer interface,Event-related desynchronization,Hand motor imagery,Subject-specific frequency and time bands,Virtual reality,Visual guidance
Visual guidance,Computer vision,Virtual machine,Virtual reality,Computer science,Brain–computer interface,Robustness (computer science),Artificial intelligence,Rhythm,Electroencephalography,Motor imagery
Journal
Volume
Issue
ISSN
132
C
0169-2607
Citations 
PageRank 
References 
10
1.06
10
Authors
6
Name
Order
Citations
PageRank
Shuang Liang1436.12
Kup-Sze Choi252647.41
Jing Qin3110995.43
Wai-Man Pang425022.20
Qiong Wang53015.18
Pheng-Ann Heng63565280.98