Developing a 3- to 6-state EEG-based brain-computer interface for a robotic manipulator control

Recent developments in BCI techniques have demonstrated high-performance control of robotic prosthetic systems primarily via invasive methods. In this work we develop an electroencephalography (EEG) based noninvasive BCI system that can be used for a similar, albeit lower-speed, robotic manipulator control and a signal processing system for detecting user’s mental intent based on motor-imagery BCI communication paradigm. We examine the performance of that system on experimental data collected from 12 different healthy participants and analyzed offline. Our EEG BCI system can correctly identify different motor imageries in EEG data with high accuracy: 3 out of 12 participants achieved accuracy in 8090% range while 2 participants could not achieve a satisfactory accuracy. We further implement an online BCI system for live control of a virtual 3 degree-of-freedom prosthetic arm manipulator and test it in live experiments with the 3 best participants who aim to reach a set of specified targets using the virtual robotic arm. The participants’ ability to control the BCI was quantified using the percentage of the tasks in which the required target was successfully reached, the time required to complete a task, and the error rate. All 3 subjects were able to successfully complete 100% of the test tasks demonstrating on average the error rate of 80% and requiring 5-10 seconds to implement given manipulator move. Our results lay a foundation for further development of EEG BCI-based robotic assistive systems and demonstrate that EEG-based BCI may be feasible for robotic control by paralyzed or immobilized individuals. Index Terms Brain Machine interfaces, electroencephalography,