Low Cost BCI Enabled Autonomous Wheelchair

The rise in spinal cord injuries (SCI) and motor neuron diseases (MND) has created an urgent need for affordable, effective assistive technologies to enhance mobility and independence for affected individuals. Traditional assistive devices are often expensive and invasive, limiting accessibility and increasing risks. This paper presents a solution: a low-cost, non-invasive Brain-Computer Interface (BCI)-enabled autonomous wheelchair system for individuals with paralysis.Using Electroencephalogram (EEG) technology, the system captures brain signals from eye blinks and facial muscle movements to navigate the wheelchair. Developed through the Incremental Prototyping Methodology, the system includes a main controller for signal acquisition and preprocessing with a Raspberry Pi 2, and a wheelchair controller that determines movement via a trained Machine Learning (ML) model. The EXG pill sensor for EEG signal acquisition enhances costeffectiveness and integration.Key innovations include low-cost BCI technology, customizable wheelchair designs, and high accuracy in interpreting EEG signals for reliable and safe movement. This approach can significantly improve the quality of life for individuals with paralysis by restoring independence and reducing reliance on caregivers.