Papers

12

Total Citations

607

H-Index

9

About

Matthew S. Johannes is a pioneering robotics and neural engineering researcher whose work sits at the intersection of brain-machine interfaces (BMIs), advanced prosthetics, and autonomous robotic systems. Best known for his contributions to the HARMONIE (Hybrid Augmented Reality Multimodal Operation Neural Integration Environment) framework, Johannes has helped redefine what is possible in human-machine interaction, particularly for individuals with limb loss or paralysis. His landmark 2014 paper demonstrating semi-autonomous hybrid BMI control of the Johns Hopkins Modular Prosthetic Limb (MPL) using intracranial EEG, eye tracking, and computer vision has garnered nearly 200 citations, reflecting its transformative influence on prosthetics research. Alongside collaborators, he demonstrated simultaneous neural control of reaching and grasping — a critical milestone for restoring natural arm function — and contributed to translational BCI work moving from animal models to human clinical application. Beyond neuroprosthetics, Johannes has expanded into disaster robotics, aerial grasping systems, and marsupial robotic platforms, showcasing the remarkable breadth of his expertise. His 2021 study on extended home use of an osseointegrated prosthetic arm further underscores his commitment to real-world patient outcomes, bridging laboratory innovation with meaningful everyday impact.

Research Focus

Key Achievements

9
H-Index
12
Papers
607
Total Citations
51
Avg Citations/Paper
🏆 Most Cited Paper
Demonstration of a Semi-Autonomous Hybrid Brain–Machine Interface Using Human Intracranial EEG, Eye Tracking, and Computer Vision to Control a Robotic Upper Limb Prosthetic
190 citations · 2014
📈 Most Prolific Year: 2014 (3 Papers)
🤝 Key Collaborators: 89
🏛 Institutions: Johns Hopkins University, Johns Hopkins University Applied Physics Laboratory

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago