Papers
8
Total Citations
84
H-Index
5
About
Mads Jochumsen is a leading researcher at the intersection of neural engineering and assistive robotics, whose work focuses on developing intuitive brain-computer interfaces (BCIs) and rehabilitation technologies for individuals with severe motor impairments. His primary research areas span intramuscular electromyography (iEMG) signal processing, electroencephalography (EEG)-based movement intention detection, and hybrid control systems that combine neural signals with alternative input modalities. Jochumsen has made significant contributions to decoding covert speech from single-trial EEG for more natural BCI control, and to optimizing steady-state visually evoked potential (SSVEP) classifiers for low-latency, computationally efficient operation. His work on the "tongue-brain hybrid robot interface" for individuals with amyotrophic lateral sclerosis (ALS) demonstrates an innovative approach to adapting control technology to progressive paralysis. With his most cited paper on windowing techniques for iEMG signals accumulating 24 citations, Jochumsen's research has directly influenced the design of diagnostic, rehabilitative, and assistive devices. His recent development of the "motoBOTTE" rehabilitation robotic device and a hybrid FES-BCI system for lower limb neurorehabilitation showcases his commitment to translating neural engineering principles into practical, life-changing technologies for bedbound and severely impaired patients.
Research Focus
Key Achievements
Top Papers
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- 7Modeling and Control of Rehabilitation Robotic Device: motoBOTTE4 citations · 2018
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