Design of Brain-Machine Interface Using Near-Infrared Spectroscopy

Abstract

In recent years, the fields of robotics and medical science have been paying close attention to brainmachine interface (BMI) systems. BMI observes human cerebral activity and use the collected data as the input to various instruments. If such a systemcould be effectively realized, it could be used as a new intuitive input interface for application to human-robot interactions, welfare scenarios, etc. In this paper, we discussed a design problem related to a BMI system using near-infrared spectroscopy (NIRS). We developed a brain state classifier based on the learning vector quantization (LVQ) method. The proposed method classifies the cerebral blood flow patterns and outputs the brain state estimate. The classification experiments showed that the proposed method can successfully classify not only human physical motions and motor imageries, but also human emotions and human mental commands issued to a robot. Especially, in the classification of “the mental commands to a robot,” we successfully realized the imagery classification of five different mental commands. The results point to the potential of NIRS-based brain machine interfaces.