Event Abstract Back to Event Information theoretical analysis of high density electromyographic data for prostheses control David Hofmann1, 2*, Armin Biess1, 2, Janne Hahne1, 3, Bernhard Graimann1, 3 and J. Michael Herrmann1, 2, 4 1 Bernstein Focus Neurotechnology Göttingen, BFNT, Germany 2 MPI for Dynamics and Self-Organization, Department of Nonlinear Dynamics, Germany 3 GmhH, Otto Bock HealthCare, Germany 4 University of Edinburgh, School of Informatics, United Kingdom Statistical properties of high density surface electromyographic (sEMG) signals for control of multifunctional myoprostheses are studied by an information-theoretic approach. We address the question, which electrodes and signal features contribute significantly to hand posture classification. For this purpose, we employ information theoretic and heuristic approaches and compare their performance. Methods: In order to assess the informational content of signals available for the control of a transradial hand prostheses we recorded 126 monopolar sEMG signals, see Fig. 1a. The data were obtained from able-bodied subjects performing repeatedly eight different static contractions (hand open and close, wrist flexion, extension, abduction, adduction, pronation and supination). For each electrode i the mutual information I(C, RMS(Eli ,T)) of the root mean square (RMS) with time window length T of its signal and the classes (hand postures) is calculated. Furthermore, we compute the pairwise mutual information I(RMS(Eli ,T), RMS(Elj ,T)). Using those measures, we construct an electrode selection algorithm and compare its performance in terms of classification accuracy with the heuristic electrode selection method sequential floating forward selection (SFFS). For classification linear discriminant analysis (LDA) is sufficient. Results: While SFFS generally outperforms the mutual information based algorithm, we find regimes for feature parameter T, where the performance of the mutual information algorithm reaches that of heuristic approaches. Figure 1. a) Location of the electrode array on the forearm of the participant. b) Classification performance trade-off between feature parameter T and selected number of electrodes by the mutual information algorithm. Performance is color coded ranging from a classification rate of 50% (blue) to 100% (red). c) Information (in bits) of RMS of signals Eli about hand posture ranges from 32.5 (blue) to 38.5 (red) with red regions indicating Figure 1 Keywords: computational neuroscience Conference: Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010. Presentation Type: Presentation Topic: Bernstein Conference on Computational Neuroscience Citation: Hofmann D, Biess A, Hahne J, Graimann B and Herrmann J (2010). Information theoretical analysis of high density electromyographic data for prostheses control. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.fncom.2010.51.00132 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 24 Sep 2010; Published Online: 24 Sep 2010. * Correspondence: Dr. David Hofmann, Bernstein Focus Neurotechnology Göttingen, BFNT, Göttingen, Germany, david@nld.ds.mpg.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers David Hofmann Armin Biess Janne Hahne Bernhard Graimann J. Michael Herrmann Google David Hofmann Armin Biess Janne Hahne Bernhard Graimann J. Michael Herrmann Google Scholar David Hofmann Armin Biess Janne Hahne Bernhard Graimann J. Michael Herrmann PubMed David Hofmann Armin Biess Janne Hahne Bernhard Graimann J. Michael Herrmann Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.