A WiSARD Network Approach for a BCI-Based Robotic Prosthetic Control

There are significant research efforts underway in the area of automatic robotic-prosthesis control based on brain-computer interface aiming at understanding how neural signals can be used to control these assistive devices. Although these approaches have made significant progresses in the ability to control robotic manipulators, the realization of portable and easy of use solutions is still an ongoing research endeavor. In this paper, we propose a novel approach relying on the use of (i) a Weightless Neural Network-based classifier, whose design lends itself to an easy hardware implementation; (ii) a robotic hand designed in order to fit with the main requirements of these kind of technologies (such as low cost, high performance, lightness, etc.) and (iii) a non-invasive light-weight and easy-donning EEG-helmet in order to provide a portable controller interface. The developed interface is connected to a robotic hand for controlling open/close actions. The preliminary results for this system are promising in that they demonstrate that the proposed method achieves similar performance with respect to state-of-the-art classifiers by contemporaneously representing a most suitable and practicable solution due to its portability on hardware devices, which will permit its direct implementation on the helmet board.