NDLI: Effective user training for motor imagery based brain computer interface with object-directed 3D visual display

Content Provider	IEEE Xplore Digital Library
Author	Shuang Liang Kup-Sze Choi Jing Qin Wai-Man Pang Pheng-Ann Heng
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., Caritas Inst. of Higher Educ., Hong Kong, China (Wai-Man Pang) \|\| Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Hong Kong, China (Pheng-Ann Heng) \|\| Sch. of Nursing, Hong Kong Polytech. Univ., Hong Kong, China (Kup-Sze Choi) \|\| Shenzhen Inst. of Adv. Technol., Shenzhen, China (Shuang Liang; Jing Qin)
Abstract	Effective user training could help us to improve the discrimination performance of our intention in brain computer interface (BCI). This paper aims to differentiate users left or right hand motor imagery (MI) tasks with different scenarios in 3D virtual environment, as non-object-directed (NOD) scenario, static-object-directed (SOD) scenario and dynamic-object-directed (DOD) scenario respectively. The results have significant differences by applying these three scenarios. Both SOD and DOD scenarios provide better classification accuracy, shorten single-trial period, and need smaller training samples comparing with the NOD case. We conclude that improving visual display may facilitate learning to use a BCI. Further comparing these results between single-subject and multiple-subject paradigm of BCI, we verify better classification performance could also be achieved by the multiple-subject paradigm. We believe these findings have the potential to improve discrimination performance of users intention for EEG-based BCI applications.
Starting Page	297
Ending Page	301
File Size	484331
Page Count	5
File Format	PDF
e-ISBN	9781479958382
DOI	10.1109/BMEI.2014.7002788
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Motor Imagery Multiple-subject Paradigm Electroencephalography Visual Display Brain Computer Interface (BCI) Single-subject Paradigm Training Accuracy Three-dimensional displays Training data User Training US Department of Defense Electroencephalogram (EEG)
Content Type	Text
Resource Type	Article

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