Brain-computer interfaces (BCIs) represent a revolutionary technology for restoring function to individuals with severe motor disabilities. This study investigates non-invasive EEG-based BCI systems for controlling assistive devices including wheelchairs, robotic arms, and communication systems. We developed machine learning algorithms to decode motor imagery signals from 67 participants, achieving 92% classification accuracy for four-class motor imagery tasks. Real-time control experiments demonstrated successful navigation of powered wheelchairs through complex environments with 98% safety compliance. The research includes user experience evaluation and identifies design principles for accessible BCI systems that can transform independence and quality of life for individuals with disabilities.