Enhancing training performance for brain–computer interface with object-directed 3D visual guidance

Purpose 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 in...

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Vydáno v:International journal for computer assisted radiology and surgery Ročník 11; číslo 11; s. 2129 - 2137
Hlavní autoři: Liang, Shuang, Choi, Kup-Sze, Qin, Jing, Pang, Wai-Man, Heng, Pheng-Ann
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2016
Springer Nature B.V
Témata:
Adult
Brain-Computer Interfaces
Classification
Computer Imaging
Computer Science
Electroencephalography - methods
Female
Health Informatics
Human-computer interface
Humans
Imaging
Male
Medicine
Medicine & Public Health
Original Article
Patient Education as Topic
Pattern Recognition and Graphics
Radiology
Reproducibility of Results
Surgery
Virtual environments
Vision
Visual effects
Electroencephpalogram (EEG)
Visual guidance
User training
Single-subject paradigm
Motor imagery
Multi-subject paradigm
Brain–computer interface (BCI)
ISSN:1861-6410, 1861-6429, 1861-6429
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Shrnutí:Purpose 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. Conclusion We believe that our findings would have the potential for improving classification performance of MI-based BCI and being applied in the practical applications.
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ISSN:1861-6410
1861-6429
1861-6429
DOI:10.1007/s11548-015-1336-5