Mobile Robot Control by Neural Networks EOG Gesture Recognition
This paper describes the development of a neural networks gesture recognition system whereby one can control a mobile robot by using the components of his brain wave bio-potentials. Such a system may be used as a control device through human eye-movements, facial muscle, and brain wave bio-potentials. Neural networks are trained to classify EOG data into one of two classes corresponding to two cognitive tasks performed by eight training segments. The operators forehead bio-potentials can be acquired and processed in Cyberlink?as mobile robot control source signals. The computer analyzes an operators the EEG(eleciroencephatographic) and EOG (electrooculographic) signals in real time. Neural networks analyze users EOG signal in order to discern for the presense of a signal and then decide whether it corresponds to a valid command. In the course of EOG analysis, the neural network checks for example, turning the robot. A trained neural network can effectively recognize user intention, left or right based only on the EOG signal. The experimental results suggest that a mobile robot can be operated by human brain wave bio-potentials with neural networks.
Brain-Wave Mind-Body Operated Devices Human Computer Interfacing Mobile Robot Brain Computer Interface EOG EMG
Choi Kyoung ho Minoru SASAKI
Gifu University, 1-1 Yanagido, Gifu Gifu University, 1-1 Yanagido Gifu
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
207-213
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）