지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2013.5
- 수록면
- 1,006 - 1,015 (10page)
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초록· 키워드
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Brain-Computer Interface(BCI) System has been rapidly developed recently in the field of brain engineering. BCI is the system that can read and output user’s thought by analyzing brain waves and manipulate the connected machine. In this paper, I have researched driving simulation using EEG signal by implementing real time BCI system of TORCS and tried to introduce a new interface as well as optimize it.
First, I have determined electrodes that will be used as a control signal by analyzing the brain activation data gained through experimentation according to motor image. Then, I created a client to use as a control interface, and connected TORCS server and BCI2000 each using UDP communication to construct a real-time BCI system. BCI2000 receives the EEG data coming from the EEG measuring equipment and does overall processing of the EEG, then finally generates the control signals. After that, client send the command such as steering, acceleration of the vehicle to TORCS server based on the control signals, and car moves. Then artificial intelligence has been implanted in the client to form the basic model of shared-control system. And I inserted algorithm that can adjust and mediate the commands coming from each of the EEG and artificial intelligence to create improved shared-control model.
As a test result, improved model of shared control was faster than the base model and artificial intelligence in lap times and top speed except for track with severe curves. Also the test participants showed more smooth and easy driving in case of improved model.
First, I have determined electrodes that will be used as a control signal by analyzing the brain activation data gained through experimentation according to motor image. Then, I created a client to use as a control interface, and connected TORCS server and BCI2000 each using UDP communication to construct a real-time BCI system. BCI2000 receives the EEG data coming from the EEG measuring equipment and does overall processing of the EEG, then finally generates the control signals. After that, client send the command such as steering, acceleration of the vehicle to TORCS server based on the control signals, and car moves. Then artificial intelligence has been implanted in the client to form the basic model of shared-control system. And I inserted algorithm that can adjust and mediate the commands coming from each of the EEG and artificial intelligence to create improved shared-control model.
As a test result, improved model of shared control was faster than the base model and artificial intelligence in lap times and top speed except for track with severe curves. Also the test participants showed more smooth and easy driving in case of improved model.
목차
Abstract
1. 서론
2. 관련 연구
3. 제안하는 방법
4. 실험 방법
5. 결과 및 고찰
References
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UCI(KEPA) : I410-ECN-0101-2014-550-002587802