An adaptive P300 model for controlling a humanoid robot with mind

This paper presents a P300 model for controlling a humanoid robot with mind, including an off-line phase with a fixed trial number for training the model and an on-line phase with an adaptive strategy for generating commands to the humanoid robot. Our control scheme includes 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. Our study shows that the adaptive model is fast and practical to control humanoid robot via brainwaves.

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