Inverse Kinematic Based Brain Computer Interface to Control Humanoid Robotic Arm

Abstract

New Inverse Kinematic based Brain Computer Interface (IK-BCI) system was proposed. the system performs aim selection intended by user through acquiring user’s EEG signal, extract the signal’s feature, classify the intention behind the signal, and performs inverse kinematic on the predicted position to make the robotic arm be reached to the desired position. Three types of five-classes EEG mental tasks signals were acquired using EMOTIV EPOC EEG head set in separate sessions and compared in terms of online system’s performance after using each one as input signal. The proposed feature extraction method was hybrid feature extraction that include Multiclass Support Vector Machine (M-CSP) with Autoregressive (AR) coefficients features. Multiclass Support Vector Machine with Radial Basis kernel Function (SVM-RBF) was used for machine learning processing based on LIBSVM MATLAB library. Analytical solution was proposed to perform the Inverse Kinematic (IK) on 5-DOF Humanoid Robotic Arm (HRA) to be controlled in online basis. The practical results showed a successful cooperation between the IK and BCI with highest classification accuracy of 88.75% which leads to successful reach of the desired target.