Abstract
In this paper, we present a P300 model for control of Cerebot – a mind-controlled humanoid robot, including a procedure of acquiring P300 signals, topographical distribution analysis of P300 signals, and a classification approach to identifying subjects’ mental activities regarding robot-walking behavior.
We design two groups of image contexts to visually stimulate subjects when acquiring neural signals that are used to control a simulated or real NAO robot. Our study shows that the group of contexts using images of robot behavior delivers better performance.
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Li, M., Li, W., Zhao, J., Meng, Q., Zeng, M., Chen, G. (2014). A P300 Model for Cerebot – A Mind-Controlled Humanoid Robot. In: Kim, JH., Matson, E., Myung, H., Xu, P., Karray, F. (eds) Robot Intelligence Technology and Applications 2. Advances in Intelligent Systems and Computing, vol 274. Springer, Cham. https://doi.org/10.1007/978-3-319-05582-4_43
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DOI: https://doi.org/10.1007/978-3-319-05582-4_43
Publisher Name: Springer, Cham
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