Move An Artificial Arm by Motor Imagery Data

Some diseases or spinal cord injury completely destruct the sensory, motor and autonomous function for the limb movement. BCI (Brain computer Interface) provides a new communication pathway for those patients. Imagination of limb movements is used to operate a BCI. With analysis of acquired EEG signal due to motor imagery controlling of an artificial limb is possible. For this technique motor imagery EEG signal is classified and the classified part is fed to a controller to execute exactly that movement. State feedback PI controller can be used to control an artificial limb. With help of this controller not only position but also velocity can be controlled. In this paper, a simulated model of EEG driven artificial limb control using state feedback PI controller is presented. For this study, EEG data for motor imagery was taken from five healthy subjects. The wavelet coefficients are calculated from that EEG signals as features and the obtained features are classified by SVM classifier to determine the part of the limb the user wants to move. The initial and target position are fed to the controller and the controller move the artificial limb to reach the target position at the classified direction. The overall control procedure is done using Matlab 7.6.