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About

Rupert Ortner is a leading researcher in the field of brain-computer interfaces (BCIs) for neurorehabilitation, with a particular focus on restoring motor function after stroke. His work centers on decoding motor imagery (MI)—the mental rehearsal of movement—to control assistive devices, including functional electrical stimulation (FES) systems, exoskeletons, and robots. Ortner’s seminal 2018 study demonstrated that MI-based BCIs can achieve high classification accuracy for stroke rehabilitation training, a finding that has garnered 73 citations and helped establish the clinical viability of these systems. His earlier 2012 paper (66 citations) laid the groundwork by showing how BCIs can transition from assistive tools for communication to active rehabilitation aids. More recently, Ortner has explored the integration of BCIs with lower limb exoskeletons, investigating how robotic gait training modulates neural activity and promotes neuromuscular plasticity—work that has already accumulated 25 citations since 2023. His research on steady-state visual evoked potentials (SSVEP) for robot control (44 citations) further showcases his versatility in BCI applications. Through these contributions, Ortner is advancing a paradigm where human-robot confluence enables intuitive, brain-driven recovery, making him a pivotal figure in the evolution of neurorehabilitation technology.