A Novel Hybrid Brain-Computer Interface for Robot Arm manipulation using Visual Evoked Potential

This paper attempts to solve an major problem of rigorous subject training in BCI based robotics. A hybrid Brain computer interface has been established here to mentally guide a robot arm without any motor commands generated in mind. Spontaneous N200 response of human brain as a part of motion onset visual evoked potential (mVEP) is used here to detect the desired object in the environment and use of Steady state visual evoked potential (SSVEP) provides the guidance to a bedside robotic arm to reach that target object. Electroencephalographic response of ten such subjects has been used here to evaluate the efficacy of the proposed system. Recorded EEG response goes through a sequential operation of preprocessing, feature extraction and classification. For detecting N200 response of the brain, a novel EKF-Particle filter induced Neural Network classifier is also proposed which essentially outperforms the other existing classifier for N200 detection. Performance analysis of other classifiers has been given here for comparison.