Empowering Mobility: Brain-Computer Interface for Enhancing Wheelchair Control for Individuals with Physical Disabilities

TL;DR

The paper addresses mobility limitations for individuals with physical disabilities by presenting a non-invasive EEG-based brain-computer interface (BCI) for wheelchair control. It details an end-to-end pipeline using a 14-channel Emotiv headset, Emotiv software suites, Cortex SDK, and a Unity-based digital twin to translate cognitive commands into wheelchair navigation in real time. The work reviews related EEG-BCI wheelchair systems, brain rhythm patterns, and machine learning classifiers, and it discusses ethical and accessibility considerations critical to deploying such technology. By incorporating a Unity digital twin and pilot training, the authors demonstrate a feasible framework that enhances autonomy while outlining future work on accuracy, usability, real-world evaluation, and cost scalability to broaden adoption.

Abstract

The integration of brain-computer interfaces (BCIs) into the realm of smart wheelchair (SW) technology signifies a notable leap forward in enhancing the mobility and autonomy of individuals with physical disabilities. BCIs are a technology that enables direct communication between the brain and external devices. While BCIs systems offer remarkable opportunities for enhancing human-computer interaction and providing mobility solutions for individuals with disabilities, they also raise significant concerns regarding security, safety, and privacy that have not been thoroughly addressed by researchers on a large scale. Our research aims to enhance wheelchair control for individuals with physical disabilities by leveraging electroencephalography (EEG) signals for BCIs. We introduce a non-invasive BCI system that utilizes a neuro-signal acquisition headset to capture EEG signals. These signals are obtained from specific brain activities that individuals have been trained to produce, allowing for precise control of the wheelchair. EEG-based BCIs are instrumental in capturing the brain's electrical activity and translating these signals into actionable commands. The primary objective of our study is to demonstrate the system's capability to interpret EEG signals and decode specific thought patterns or mental commands issued by the user. By doing so, it aims to convert these into accurate control commands for the wheelchair. This process includes the recognition of navigational intentions, such as moving forward, backward, or executing turns, specifically tailored for wheelchair operation. Through this innovative approach, we aim to create a seamless interface between the user's cognitive intentions and the wheelchair's movements, enhancing autonomy and mobility for individuals with physical disabilities.

Figures (3)

• Figure 1: EEG electrodes placement on head jayarathne2020person.
• Figure 2: A pipeline of the proposed design system.
• Figure 3: A digital twin of Wheelchair 3D prototype in Unity engine.