Online movement prediction in a robotic application scenario

Abstract

Current movement prediction systems based on electroencephalography were mainly developed and evaluated in highly controlled scenarios, in which subjects concentrate only on the desired task with as few as possible disturbing sources present. However, it has not been addressed sufficiently how the suggested methods perform in more complex and uncontrolled environments. In this work we predict arm movements online in a robotic teleoperation scenario and present a completely online running methodology. The system is evaluated on ten sessions from three subjects. Evaluation criteria are the overall classification performance and the success in predicting an upcoming movement in the application. Our results confirm that it is possible to predict movements in less restricted applications motivating the transfer of these methods to real world applications.