Control of a Smart Electric Wheelchair Based on EEG Signal and Graphical User Interface for Disabled People

Abstract

A Brain-Computer Interface (BCI) can help disabled people to control an electric wheelchair in an indoor environment. However, using the BCI requires a continuously concentrated effort and this can make them tired. This paper proposes a semi-automatic control method for a smart electric wheelchair based on EEG signal and graphical user interface to provide safe and convenient mobility for disabled people. In particular, EEG signals obtained from an Emotiv device through eye movements are digitized with a sampling frequency of 128Hz. Patterns of an EEG signal are identified to use a peak detection algorithm and the width of the signal pattern. In addition, a graphical user interface designed for on-screen display can support disabled people to select the desired destination from a list of predefined locations to reach by an eye movement for controlling the electric wheelchair. For smooth, safety, and prediction, the wheelchair is designed to move on the path towards virtual orbit and the user can stop the wheelchair in case of appearing danger. Experiments in an obstacle-free environment have been demonstrated to illustrate the effectiveness of the proposed approach.