A Humanoid Robot Control System with SSVEP-based Asynchronous Brain-Computer Interface

Abstract

A feature extraction method is proposed for steady-state visual evoked potential(SSVEP) idle state detection. By designing a two-level classifier structure,SSVEP-based asynchronous brain-computer interface(BCI) is established. A wireless sensor network(WSN) hardware node embedded with TI CC2430 is implemented for remote transmission of robot control command.The developed humanoid robot control system has multiple control modes,such as mind control, voice interaction,joystick input,machine vision,and obstacle avoidance.The effectiveness of brain-computer interface asynchronous control is validated through experiments on SSVEP idle-state detection.