Tonin, LucaPerdikis, SerafeimKuzu, Taylan DenizPardo, JorgeOrset, BastienLee, KyuhwaAach, MirkoSchildhauer, Thomas ArminMartinez-Olivera, RamonMillan, Jose del R.2024-02-162024-02-162024-02-162022-12-2210.1016/j.isci.2022.105418https://infoscience.epfl.ch/handle/20.500.14299/203868WOS:001084010800001Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite 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. We demonstrate 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, significant neuro-plasticity changes and improved BMI command latency, achieved high navigation performance. In addition, 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.Brain-Computer InterfaceMotor-ImageryMachine InterfacesBciAdaptationStimulationMovementPatientCortexSignaltext::journal::journal article::research article