Brain-Machine Interface system to differentiate between five mental tasks

Abstract

The large amount of patients suffering from motor disabilities has motivated a lot of studies in order to improve their mobility and quality of life. A Brain-Machine Interface (BMI) can be very useful to control a system that is able to improve the independence of people with motor disabilities. The electroencephalographic (EEG) signals are commonly used to control systems as a robot arm or other devices like rehabilitation systems. Motor imagery is one of the techniques that is usually used to command a BMI. To that end, an accurate method to classify different mental tasks is needed. In this paper, the accuracy of a SVM-based system (Support Vector Machine) is analyzed using four different procedures that include two feature extraction methods: Periodogram and Welch's method. The results show that by using a SVM-based system it is possible to obtain enough accuracy for the suggested purpose. The system defined in this work is able to distinguish between five different mental tasks with a considerably higher accuracy than the random behavior (20% for five tasks). The average success rate for three users is 47,75±4%. Using five different tasks, it is possible to control the movement of a robotic arm in a 2-D plane, assigning a task for each direction (left, right, forward and backward) and another for a rest state.