Marc H. Schieber

Johns Hopkins University Applied Physics Laboratory, Johns Hopkins University, University of Rochester

Papers

Total Citations

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About

Marc H. Schieber is a leading figure in motor neuroscience and brain-machine interfaces (BMIs), whose work has fundamentally advanced our understanding of how cortical signals can be harnessed for dexterous control. His research centers on decoding complex, multi-joint movements from neural activity, with a particular focus on restoring function to individuals with paralysis through prosthetic and robotic systems. Schieber’s major contributions include pioneering the use of recursive Bayesian estimation with dynamic movement primitives to achieve high-precision neural decoding of complex movement trajectories, moving beyond the linear state-space models that had long limited BMI performance. His 2016 paper on this topic (26 citations) and his 2007 study demonstrating real-time decoding of individual finger and wrist movements (20 citations) have been instrumental in pushing the field toward more natural, dexterous control of multi-fingered prosthetic hands. With a career spanning decades, Schieber’s work has consistently challenged the field to move beyond robotic, limited movements, as articulated in his influential 2015 perspective (16 citations). His research continues to shape the next generation of BMIs, bridging the gap between neural signals and fluid, human-like motor output.