Anett Seeland
Papers
5
Total Citations
110
H-Index
5
About
Anett Seeland’s research lies at the intersection of neuroscience, robotics, and human-machine interaction, with a focus on making wearable robotic systems more intuitive and responsive. Her work centers on developing predictive interfaces that use brain signals—particularly electroencephalography (EEG)—to anticipate human intent, enabling seamless control of exoskeletons and robotic devices. A key contribution is her exploration of “brain reading” for predictive human-machine interfaces in robotics, where she demonstrated how neural activity can be used to forecast upcoming movements, a concept detailed in her highly cited 2013 paper (37 citations). She has also advanced the practical application of these systems, addressing challenges like online movement prediction in realistic, less-controlled scenarios (15 citations). Notably, her research extends to stroke rehabilitation, where she has pioneered adaptive multimodal biosignal control for exoskeleton-assisted motor recovery, tackling the critical issue of limited patient training data. With over 100 total citations across her most-cited works, Seeland’s contributions are foundational to the development of brain-controlled assistive technologies, bridging the gap between laboratory-controlled experiments and real-world clinical and robotic applications.
Research Focus
Key Achievements
Top Papers
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- 3Online movement prediction in a robotic application scenario15 citations · 2013
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