Please wait a minute...
FITEE
Front. Inform. Technol. Electron. Eng.  2010, Vol. 11 Issue (8): 587-597    DOI: 10.1631/jzus.C0910530
    
A P300 based online brain-computer interface system for virtual hand control
Wei-dong Chen1,2, Jian-hui Zhang1,2, Ji-cai Zhang1,2, Yi Li1,2, Yu Qi1,2, Yu Su1,2, Bian Wu1,3, Shao-min Zhang1,3, Jian-hua Dai1,2, Xiao-xiang Zheng*,1,3, Dong-rong Xu1,4,5
1 Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China 2 School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China 3 Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China 4 MRI Unit, Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA 5 New York State Psychiatric Institute, New York, NY 10032, USA
Download:   PDF(0KB)
Export: BibTeX | EndNote (RIS)      

Abstract  Brain-computer interface (BCI) is a communication system that can help lock-in patients to interact with the outside environment by translating brain signals into machine commands. The present work provides a design for a virtual reality (VR) based BCI system that allows human participants to control a virtual hand to make gestures by P300 signals, with a positive peak of potential about 300 ms posterior to the onset of target stimulus. In this virtual environment, the participants can obtain a more immersed experience with the BCI system, such as controlling a virtual hand or walking around in the virtual world. Methods of modeling the virtual hand and analyzing the P300 signals are also described in detail. Template matching and support vector machine were used as the P300 classifier and the experiment results showed that both algorithms perform well in the system. After a short time of practice, most participants could learn to control the virtual hand during the online experiment with greater than 70% accuracy.

Key words： Brain-computer interface (BCI)      Electroencephalography (EEG)      P300      Virtual reality (VR)      Template matching      Support vector machine (SVM)     
Received: 25 August 2009      Published: 02 August 2010
CLC:  TP399  
  R318  
Fund:  Project  supported  by  the  National  Natural  Science  Foundation  of China (No. 60873125), the National Institute of Biomedical Imaging
and   Bioengineering   (No.   1R03EB008235-01A1),   the   Shanghai Commission of Science and Technology (No. 10440710200), and the
Fundamental Research Funds for the Central Universities
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Wei-dong Chen
Jian-hui Zhang
Ji-cai Zhang
Yi Li
Yu Qi
Yu Su
Bian Wu
Shao-min Zhang
Jian-hua Dai
Xiao-xiang Zheng
Dong-rong Xu
Cite this article:

Wei-dong Chen, Jian-hui Zhang, Ji-cai Zhang, Yi Li, Yu Qi, Yu Su, Bian Wu, Shao-min Zhang, Jian-hua Dai, Xiao-xiang Zheng, Dong-rong Xu. A P300 based online brain-computer interface system for virtual hand control. Front. Inform. Technol. Electron. Eng., 2010, 11(8): 587-597.

URL:

http://www.zjujournals.com/xueshu/fitee/10.1631/jzus.C0910530     OR     http://www.zjujournals.com/xueshu/fitee/Y2010/V11/I8/587


A P300 based online brain-computer interface system for virtual hand control

Brain-computer interface (BCI) is a communication system that can help lock-in patients to interact with the outside environment by translating brain signals into machine commands. The present work provides a design for a virtual reality (VR) based BCI system that allows human participants to control a virtual hand to make gestures by P300 signals, with a positive peak of potential about 300 ms posterior to the onset of target stimulus. In this virtual environment, the participants can obtain a more immersed experience with the BCI system, such as controlling a virtual hand or walking around in the virtual world. Methods of modeling the virtual hand and analyzing the P300 signals are also described in detail. Template matching and support vector machine were used as the P300 classifier and the experiment results showed that both algorithms perform well in the system. After a short time of practice, most participants could learn to control the virtual hand during the online experiment with greater than 70% accuracy.

关键词： Brain-computer interface (BCI),  Electroencephalography (EEG),  P300,  Virtual reality (VR),  Template matching,  Support vector machine (SVM)    
[1] Ming-hui SHI, Chang-le ZHOU, Jun XIE, Shao-zi LI, Qing-yang HONG, Min JIANG, Fei CHAO, Wei-feng REN, Xiang-qian LIU, Da-jun ZHOU, Tian-yu YANG. Electroencephalogram-based brain-computer interface for the Chinese spelling system: a survey[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(3): 423-436.
[2] Mohammad Mosleh, Hadi Latifpour, Mohammad Kheyrandish, Mahdi Mosleh, Najmeh Hosseinpour. A robust intelligent audio watermarking scheme using support vector machine[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(12): 1320-1330.
[3] G. R. Brindha, P. Swaminathan, B. Santhi. Performance analysis of new word weighting procedures for opinion mining[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(11): 1186-1198.
[4] Bang-hua Yang, Liang-fei He, Lin Lin, Qian Wang. Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent component analysis based recursive least squares in brain-computer interface[J]. Front. Inform. Technol. Electron. Eng., 2015, 16(6): 486-496.
[5] Qi-rong Mao, Xin-yu Pan, Yong-zhao Zhan, Xiang-jun Shen. Using Kinect for real-time emotion recognition via facial expressions[J]. Front. Inform. Technol. Electron. Eng., 2015, 16(4): 272-282.
[6] Jian Shi, Shu-you Zhang, Le-miao Qiu. Credit scoring by feature-weighted support vector machines[J]. Front. Inform. Technol. Electron. Eng., 2013, 14(3): 197-204.
[7] Jin-he Shi, Ji-zhong Shen, Yu Ji, Feng-lei Du. A submatrix-based P300 brain-computer interface stimulus presentation paradigm[J]. Front. Inform. Technol. Electron. Eng., 2012, 13(6): 452-459.
[8] Zhi-yong Yan, Cong-fu Xu, Yun-he Pan. Improving naive Bayes classifier by dividing its decision regions[J]. Front. Inform. Technol. Electron. Eng., 2011, 12(8): 647-657.
[9] Sun Hee Kim, Hyung Jeong Yang, Kam Swee Ng. Incremental expectation maximization principal component analysis for missing value imputation for coevolving EEG data[J]. Front. Inform. Technol. Electron. Eng., 2011, 12(8): 615-628.
[10] Wen-de Dong, Yue-ting Chen, Zhi-hai Xu, Hua-jun Feng, Qi Li. Image stabilization with support vector machine[J]. Front. Inform. Technol. Electron. Eng., 2011, 12(6): 478-485.
[11] Yu Su, Yu Qi, Jian-xun Luo, Bian Wu, Fan Yang, Yi Li, Yue-ting Zhuang, Xiao-xiang Zheng, Wei-dong Chen. A hybrid brain-computer interface control strategy in a virtual environment[J]. Front. Inform. Technol. Electron. Eng., 2011, 12(5): 351-361.
[12] Hong-xia Pang, Wen-de Dong, Zhi-hai Xu, Hua-jun Feng, Qi Li, Yue-ting Chen. Novel linear search for support vector machine parameter selection[J]. Front. Inform. Technol. Electron. Eng., 2011, 12(11): 885-896.
[13] Kui-kang Cao, Hai-bin Shen, Hua-feng Chen. A parallel and scalable digital architecture for training support vector machines[J]. Front. Inform. Technol. Electron. Eng., 2010, 11(8): 620-628.
[14] Hyeon Chang Lee, Byung Jun Kang, Eui Chul Lee, Kang Ryoung Park. Finger vein recognition using weighted local binary pattern code based on a support vector machine[J]. Front. Inform. Technol. Electron. Eng., 2010, 11(7): 514-524.