Technical Brief

P300 and Motor Imagery Based Brain-Computer Interface for Controlling Wheelchairs1

[+] Author and Article Information
Hung-Chyun Chou, Narendra Prataksita, Yi-Tseng Lin, Chung-Hsien Kuo

Department of Electrical Engineering,
National Taiwan University of Science and Technology,
Taipei City 10607, Taiwan

Accepted and presented at the Design of Medical Devices Conference (DMD2014), Minneapolis, MN, April 7–10, 2014. DOI: 10.1115/1.4027100

Manuscript received February 21, 2014; final manuscript received March 3, 2014; published online July 21, 2014. Editor: Arthur G. Erdman.

J. Med. Devices 8(3), 030906 (Jul 21, 2014) (2 pages) Paper No: MED-14-1106; doi: 10.1115/1.4027100 History: Received February 21, 2014; Revised March 03, 2014

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LenhardtA., KaperM., and RitterH. J., 2008, “An Adaptive P300-Based Online Brain–Computer Interface,” IEEE Trans. Neural Syst. Rehab. Eng., 16(2), pp. 121–130. [CrossRef]
Yamawaki, N., Wilke, C., Liu, Z., and HeB., 2006, “An Enhanced Time-Frequency-Spatial Approach for Motor Imagery Classification,” IEEE Trans. Neural Syst. Rehab. Eng., 14(2), pp. 250–254. [CrossRef]
AmiriS., Fazel-RezaiR., and AsadpourV., 2013, “A Review of Hybrid Brain-Computer Interface Systems,” Adv. Human Comp. Interaction, 2013, p. 187024. [CrossRef]
YuanH., DoudA., and HeB., 2008, “Cortical Imaging of Event-Related (de)Synchronization During Online Control of Brain-Computer Interface Using Minimum-Norm Estimates in Frequency Domain,” IEEE Trans. Neural Syst. Rehab. Eng., 16(5), pp. 425–431. [CrossRef]


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Fig. 1

The control scheme and the adapted interface of the wheelchair

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Fig. 2

The FSM of the hybrid BCI-based wheelchair

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Fig. 3

EEG features of P300 and MI

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Fig. 4

The system architecture (LHS) and the setup of experiment and the planned trajectory (RHS)

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Fig. 5

The online experiment trajectories of three subjects



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