Publication: Semi-automatic P300-based brain-controlled wheelchair
Issued Date
2012-10-24
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2-s2.0-84867627523
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Mahidol University
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SCOPUS
Bibliographic Citation
2012 ICME International Conference on Complex Medical Engineering, CME 2012 Proceedings. (2012), 455-460
Suggested Citation
Dilok Puanhvuan, Yodchanan Wongsawat Semi-automatic P300-based brain-controlled wheelchair. 2012 ICME International Conference on Complex Medical Engineering, CME 2012 Proceedings. (2012), 455-460. doi:10.1109/ICCME.2012.6275713 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/14141
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Title
Semi-automatic P300-based brain-controlled wheelchair
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Abstract
The self mobility is a dream of many persons who suffered with disability. To realize their hope, brain-controlled wheelchair (BCW) will be a powerful system for mobility improvement. P300 is the reliably brain phenomenon that can be used for interpreting brain commands as well as eyes-blink artifact signal. This paper aims to propose a prototype of BCW that allows the persons with severe disabilities to practically control the electric wheelchair in their home environment. The combination of EEG signal between P300 phenomenon and eyes-blink artifact were used as a hybrid BCW system. Furthermore, this hybrid BCW can be operated in automatic navigation and normal control mode. These two modes delivered 4 destination commands in automatic navigation control and 4 direction commands (forward, backward, turn left and turn right) in normal control mode. These commands were selected via P300 processing system. The different pattern of eyes blinking was used for fast stop, on/off P300 system, and mode changing command (2 times, 3 times and 4 times of eyes-blinking respectively). The result show that the prototype BCW can be operated both automatic navigation and brain control mode with safe by 2 times confirmation of P300 command. Without assistant, user can operate this system by them self via defined different pattern of eyes blinking command. 100% accuracy can be achieved in eyes-blinking detection algorithm. With our new design of LED based P300 stimulator, 95% averaged accuracy with the transfer rate of 3.52command/minute can be achieved by simple and timesaving P300 detection algorithm. © 2012 IEEE.