title: Enhancing training performance for brain-computer interface with object-directed 3D visual guidance.
abstract: OBJECTIVE
  The accuracy of the classification of user intentions is essential for motor imagery (MI)-based brain-computer interface (BCI). Effective and appropriate training for users could help us produce the high reliability of mind decision making related with MI tasks. In this study, we aimed to investigate the effects of visual guidance on the classification performance of MI-based BCI.
  
  
  METHODS
  In this study, leveraging both the single-subject and the multi-subject BCI paradigms, we train and classify MI tasks with three different scenarios in a 3D virtual environment, including non-object-directed scenario, static-object-directed scenario, and dynamic object-directed scenario. Subjects are required to imagine left-hand or right-hand movement with the visual guidance.
  
  
  RESULTS
  We demonstrate that the classification performances of left-hand and right-hand MI task have differences on these three scenarios, and confirm that both static-object-directed and dynamic object-directed scenarios could provide better classification accuracy than the non-object-directed case. We further indicate that both static-object-directed and dynamic object-directed scenarios could shorten the response time as well as be suitable applied in the case of small training data. In addition, experiment results demonstrate that the multi-subject BCI paradigm could improve the classification performance comparing with the single-subject paradigm. These results suggest that it is possible to improve the classification performance with the appropriate visual guidance and better BCI paradigm.
  
  
  CONCLUSIONS
  We believe that our findings would have the potential for improving classification performance of MI-based BCI and being applied in the practical applications.
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. shuang.liang.jsj@gmail.com.
authors.forename: Shuang
authors.initials: S
authors.lastname: Liang
authors.affiliation: School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
authors.forename: Kup-Sze
authors.initials: KS
authors.lastname: Choi
authors.affiliation: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Medicine, Shenzhen University, Shenzhen, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Qin
authors.affiliation: Department of Computer Science, Caritas Institute of Higher Education, Tseung Kwan O, Hong Kong.
authors.forename: Wai-Man
authors.initials: WM
authors.lastname: Pang
authors.affiliation: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong.
authors.forename: Pheng-Ann
authors.initials: PA
authors.lastname: Heng
mesh_terms: D000328:Adult; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D010353:Patient Education as Topic; D015203:Reproducibility of Results
keywords: Brain–computer interface (BCI); Electroencephpalogram (EEG); Motor imagery; Multi-subject paradigm; Single-subject paradigm; User training; Visual guidance