Exploiting prior neurophysiological knowledge to improve Brain Computer Interface performance

Abstract

Event Abstract Back to Event Exploiting prior neurophysiological knowledge to improve Brain Computer Interface performance Arne Ewald1*, Andreas Ziehe1, Forooz S. Avarvand1 and Guido Nolte1 1 Fraunhofer Institute FIRST IDA, Germany Most EEG/MEG based Brain Computer Interfaces (BCI) employ machine learning techniques to discriminate and classify the recorded data belonging to different classes. Usually, no neurophysiological knowledge is used within the classification algorithms. Here, a method is proposed that includes prior knowledge about the locations of sources of imagined movement of the left and the right hand by projecting EEG/MEG data onto a subspace defined by modeled sources at the corresponding locations in somatosensory areas. Three different source models are investigated. First, one radial dipole on each side is based on the assumption that both location and orientation are known. Hence, for two sides, a 2-dimensional subspace is selected. Second, three dipoles at each location span a 6-dimensional subspace assuming known locations but uncertain orientations. Third, we modeled the sources as multipoles up to quadrupolar order resulting in a 16-dimensional subspace. The multipole expansion systematically corrects for inaccuracies both in location and exact shape of the source. After the projection onto respective topographies, feature extraction is performed on the reduced data by Common Spatial Filter (CSP) analysis. Finally, Linear Discriminant Analysis (LDA) is applied for classification. The projection of the data leads to a reduction of the dimensionality of the signal focusing on those parts of the signal which are generated or suppressed in the motor cortex during imagined hand movement. Since EEG/MEG data are strongly affected by various types of artifacts hampering the classification the proposed procedure leads to a removal of parts of the signal and therefore a reduction of artifacts. For EEG data it is shown that a projection with respect to source locations prior to CSP analysis leads to a gain of BCI performance when the sources are modeled as multipoles. Conference: Biomag 2010 - 17th International Conference on Biomagnetism , Dubrovnik, Croatia, 28 Mar - 1 Apr, 2010. Presentation Type: Poster Presentation Topic: Brain-computer and neural interfacing Citation: Ewald A, Ziehe A, Avarvand FS and Nolte G (2010). Exploiting prior neurophysiological knowledge to improve Brain Computer Interface performance. Front. Neurosci. Conference Abstract: Biomag 2010 - 17th International Conference on Biomagnetism . doi: 10.3389/conf.fnins.2010.06.00260 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: 01 Apr 2010; Published Online: 01 Apr 2010. * Correspondence: Arne Ewald, Fraunhofer Institute FIRST IDA, Berlin, Germany, mail@aewald.net 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 Arne Ewald Andreas Ziehe Forooz S Avarvand Guido Nolte Google Arne Ewald Andreas Ziehe Forooz S Avarvand Guido Nolte Google Scholar Arne Ewald Andreas Ziehe Forooz S Avarvand Guido Nolte PubMed Arne Ewald Andreas Ziehe Forooz S Avarvand Guido Nolte 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.