DESIGN OF CONTROL SYSTEM FOR MOTOR IMAGERY BASED NEURO-AID APPLICATION

Abstract

The application of Brain Computer Interface (BCI) for rehabilitation purpose has gained wide popularity in recent times. BCI for rehabilitation involves detection of brain signals, when the subject performs some sort of Motor Imagery (MI) task, for example, imagination of movement of limbs. Imagination of such movement causes desynchronization of neurons of one part of the brain gets within other parts synchronized. Band power features are best suited for quantification of the synchronization phenomenon. In the present work, extreme learning machine (ELM) and support vector machine (SVM) based classifiers are used to classify the test data. The classifier output is further used to generate control signals for driving a stepper motor, which may be used to drive some neuro-aid application device. In order to achieve a workable model for pragmatic applications, it is necessary to design a robust in nature stepper motor. Open loop analysis, closed loop analysis and performance analysis of motor with possible disturbances are carried out to evaluate the effectiveness of the proposed work. The maximum accuracy using ELM and SVM classifiers are achieved as 90% and 87.78% with a training time of 0.2496[Formula: see text]s and 3.964[Formula: see text]s, respectively. In the open loop and closed loop analysis, the desired angular movement (task imagined for rehabilitation) is achieved with an accuracy of 54.14% and 93.4%, respectively. These results suggest that a BCI system can be designed with higher efficiency with the help of MI data.