Niclas Nilsson
Papers
1
Total Citations
9
H-Index
1
About
Niclas Nilsson is a researcher whose work sits at the intersection of biomedical engineering and human-machine interaction, with a primary focus on myoelectric pattern recognition (MPR) for prosthetic control and rehabilitation. His most-cited paper, "Estimates of Classification Complexity for Myoelectric Pattern Recognition" (2016), tackles a fundamental challenge in the field: how to optimize the balance between classifier complexity and real-time performance when decoding electromyographic (EMG) signals. By systematically analyzing the trade-offs between feature extraction, classifier architecture, and classification accuracy, Nilsson provided a practical framework for designing more robust and efficient prosthetic control systems. This work has garnered 9 citations and serves as a methodological cornerstone for researchers seeking to improve the responsiveness of myoelectric interfaces. Beyond this paper, Nilsson's contributions extend to addressing the clinical translation of MPR, including its use in treating phantom limb pain through intuitive virtual and robotic effector control. His research is particularly valuable for students and engineers working at the nexus of signal processing, machine learning, and assistive technology, offering clear guidelines for reducing computational overhead without sacrificing classification fidelity.
Research Focus
Key Achievements
Top Papers
- 1