EEG Control of a Robotic Wheelchair

Abstract

The Brain-Computer Interface (BCI) technology has been widely used in clinical research; however, its adoption in consumer devices has been hindered by high costs, poor reliability and limited autonomy. In this study, we introduce a low-cost, open-source hardware-based, consumer-grade product that brings BCI technologies closer to the elderly and motor-impaired individuals. Specifically, we developed an autonomous motorized wheelchair with BCI-based input capabilities. The system employs the ROS-backend navigation stack, which integrates RTAB-MAP for mapping, localization, and visual odometry, as well as A* global and DWA local path planning algorithms for seamless indoor autonomous operations. Data acquisition is accomplished using OpenBCI 16-channel EEG sensors, while Ensemble-Subspace KNN machine learning model is utilized for intent prediction, particularly goal selection. The system offers active obstacle avoidance and mapping in all environments, while a hybrid BCI Motor Imagery based control is implemented in a known mapped environment. This prototype offers remarkable autonomy while ensuring user safety and granting unparalleled independent mobility to the motor-impaired and elderly.