Motor decoding from invasive neuromodulations
Motor decoding from invasive neuromodulations
Development of intelligent adaptive deep brain stimulation devices for Parkinson's Disease.
Merk, T., Peterson, V., Lipski, W. J., Blankertz, B., Turner, R. S., Li, N., ... & Neumann, W. J. (2022). Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease. Elife, 11, e75126. doi: https://doi.org/10.7554/eLife.75126
Peterson, V., Merk, T., Bush, A., Nikulin, V., Kühn, A. A., Neumann, W. J., & Richardson, M. (2022). Movement decoding using spatio-spectral features of cortical and subcortical local field potentials. Experimental Neurology, 114261. doi: https://doi.org/10.1016/j.expneurol.2022.114261
Merk, T., Peterson, V., Köhler, R., Haufe, S., Richardson, R. M., & Neumann, W. J. (2022). Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation. Experimental Neurology, 113993. doi: https://doi.org/10.1016/j.expneurol.2022.113993
CRCNS: US-GERMAN RESEARCH PROPOSAL: Deep Neural Network Approaches for Closed-Loop Deep Brain Stimulation Using Cortical and Subcortical Sensing
Principal Investigators: R. Mark Richardson, MD, PhD, Neurosurgery Faculty, Harvard Medical School and Robert S. Turner, PhD, Department of Neurobiology, University of Pittsburgh; Wolf-Julian Neumann, MD, and Andrea A. Kühn, MD, Department of Neurology, Charité-Universitätsmedizin Berlin.
Participation: Postdoc