Bringing Motor Imagery BCI systems outside of the laboratory into daily activities

Abstract

Motor Imagery Brain-Computer Interfaces(MI-BCIs) transform imagined movements into actionable control signals, enabling applications such as wheelchair navigation and robotic device operation. By leveraging the brain's ability to generate neural activity similar to actual movement during imagination, MI-BCIs hold great promise for assisting individuals with mobility impairments or neuromuscular disorders, such as spinal cord injuries. Despite decades of research, these systems remain confined to controlled laboratory environments due to technological, usability, and environmental challenges. This research aims to bridge the gap between laboratory and real-world applications by addressing key challenges at every stage, across the MI-BCI pipeline. Enhanced training methods using Virtual Reality(VR) were shown to significantly improve signal quality, as demonstrated in a study involving 21 participants. To simplify system setups, novel channel reduction techniques based on Fisher's ratio and Pearson's correlation identified optimal features, enabling reliable single-channel classification. Furthermore, integrating soft robotics as intuitive control interfaces for performing daily activities, such as pressing spray buttons, exemplifies potential for seamless human-machine interaction. By advancing training protocols, reducing complexity, and enhancing usability, this work brings MI-BCIs closer to real-world applications. These efforts aim to unlock MI-BCIs' transformative potential, empowering individuals with impaired mobility to regain independence and improve quality of life.