Learning to control a BMI-driven wheelchair for people with severe tetraplegia

Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite technical progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the hypothesis that BMI skill acquisition by end-users is fundamental to control a non-invasive brain-actuated intelligent wheelchair in real-world settings. Here, we show that three tetraplegic spinal cord injury users could be trained to operate a non-invasive, self-paced thought-controlled wheelchair and execute complex navigation tasks. However, only the two users exhibiting increasing decoding performance and feature discriminancy, as well as significant neuroplasticity changes and improved BMI command latency, achieved high navigation performance. Additionally, we show that dexterous, continuous control of robots is possible through low-degree of freedom, discrete and uncertain control channels like a motor imagery BMI, by blending human and artificial intelligence through shared-control methodologies. We posit that subject learning and shared-control are the key-components paving the way for translational non-invasive BMI.