Controlling a Rehabilitation Robot with Brain-Machine Interface: An approach based on Independent Component Analysis and Multiple Kernel Learning

Abstract

Patients suffering from severe motor disabilities are usually dependent on assistance from other people to engage in rehabilitation exercises, making the rehabilitation process time-consuming and inconvenient. We propose an automatic feature extraction method for a brain-machine interface that allows patients to control a robot using their own brain waves. A brain–machine interface (BMI) based on the P300 event-related potential (ERP), called the Brain Controlled Rehabilitation System (BCRS), was developed to detect patient intentions. Using the BCRS, patients can communicate with the robot through their brain waves. However, obtaining an automatically extracted, useful EEG signal is a difficult and important problem for BMI research. In this paper, Independent Component Analysis – Multiple Kernel Learning (ICA-MKL) is used to directly extract a useful signal and build the classification mode for BCRS.  The results reveal that this method is useful for automatically extracting the P300 signal and improves on the accuracy of MKL. In addition, the same method can be extended to any motor imagery area. The ICA-MKL approach for brain imagery data also effectively removes eye-blink artifacts.