Empowering Individuals With Disabilities: A 4-DoF BCI Wheelchair Using MI and EOG Signals

Abstract

The field of Brain-Computer Interface (BCI) has been rapidly expanding in the last few years and it is applicable in several fields. This study introduces a BCI-controlled wheelchair that utilizes Motor Imagery (MI) mental commands for turning left and right and Electrooculogram (EOG) signals, raising the eyebrows, for starting and stopping. The wheelchair offers 4 Degrees of Freedom (DoF), allowing users to move forward, stop, turn left, and turn right. The Emotiv Epoc headset is used to record the raw EEG data, the Common Spatial Patterns (CSP) algorithm is utilized to extract features from the data, and the Support Vector Machine (SVM) is employed to classify the mental commands. A total of 28 subjects, with half of them being individuals with motor and brain disabilities such as brain paralysis, severe brain disability, epilepsy, and spastic tetraplegia, participated in 5 experiments to assess the proposed BCI system. The results show that all participants, including those with disabilities, successfully adapted to and operated the BCI-controlled wheelchair with high accuracy and precision.

Keywords

• Brain-computer interface
• BCI-controlled wheelchair
• SVM
• motor imagery
• CSP
• motor and mental disabilities