Subject-specific real time motor imaginary detection scheme for robot-aided hand rehabilitation

Abstract

This study is a motor imaginary detection scheme for rehabilitation. Recently, detecting motor imaginary movement based on brain mapping device has been applied to improve robot-aided therapy for rehabilitation. Our goal is to develop a simple method that perform a system in real time to make a natural movement, to build subject-specific real time code to realize a system that help subject-specific rehabilitation therapy and classify ERD and Fake MI and eliminate Fake MI for correct rehabilitation therapy. We opt to EEG for brain imaging modality and using Matlab software for EEG signal processing. Event-related desynchronization (ERD) occurs in specific frequency band in brain wave when human has intention of movement. To detect ERD, in this thesis, we utilize a method called Machine Learning. The machine learning algorithm we applied in this study was Support Vector Machine (SVM). The result of SVM represents low success trial of ERD and low false detection of Fake MI. The algorithm that remove Fake MI also eliminate the ERD and that cause low success trials. We built the real time system that be able to perform voluntary-like movement. During building the system, we have found problems like Fake MI. Fake MI is the factor that interrupt a correct rehabilitation therapy. ⓒ 2016 DGIST