Single trial event-related potentials with automatic wavelet denoising

Abstract

Event Abstract Back to Event Single trial event-related potentials with automatic wavelet denoising Maryam Ahmadi Shapourabadi1* and Rodrigo Quian Quiroga1 1 University of Leicester, United Kingdom Event related potentials (ERPs) are the electroencephalographic (EEG) responses to sensory, cognitive or motor events. Besides their clinical applications, ERPs are widely used in cognitive psychology. In most cases ERPs have small amplitudes compared to the ongoing EEG in which they are embedded. By far the most popular technique to extract the ERPs is by averaging several trials, but when averaging, the information related to the systematic and unsystematic variation between the single trials is lost. Single trial ERPs have been previously detected using a denoising implementation based on discrete wavelet decomposition. The denoising was obtained by manually selecting coefficients correlated with the ERPs in each scale of the decomposition, then setting to zero the uncorrelated coefficients and finally reconstructing the signal from the remaining coefficients. However the manual selection of coefficients is a very subjective and time consuming task. In the proposed study the wavelet decomposition was combined with the several detection theories to develop new automatic denoising methods. A level-dependent hard threshold of event related potentials was done by estimating the variability or by approximating the energy of the wavelet coefficients of the average ERPs in each scale of decomposition. The inverse wavelet transform provided the denoised average ERPs. Consequently the single trial ERPs were extracted by reconstruction of the wavelet coefficients of the decomposed single trials with the same range of the selected coefficients of the average ERPs. Performances of the proposed methods were evaluated on visual and auditory ERPs and provided a successful detection of major components of average and single trials ERPs. Keywords: Brain Signals, ERP Conference: XI International Conference on Cognitive Neuroscience (ICON XI), Palma, Mallorca, Spain, 25 Sep - 29 Sep, 2011. Presentation Type: Poster Presentation Topic: Poster Sessions: Modeling and Analysis of Brain Signals Citation: Shapourabadi M and Quian Quiroga R (2011). Single trial event-related potentials with automatic wavelet denoising. Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI). doi: 10.3389/conf.fnhum.2011.207.00076 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: 16 Nov 2011; Published Online: 25 Nov 2011. * Correspondence: Dr. Maryam Ahmadi Shapourabadi, University of Leicester, Leicester, United Kingdom, ma447@le.ac.uk 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 Maryam Ahmadi Shapourabadi Rodrigo Quian Quiroga Google Maryam Ahmadi Shapourabadi Rodrigo Quian Quiroga Google Scholar Maryam Ahmadi Shapourabadi Rodrigo Quian Quiroga PubMed Maryam Ahmadi Shapourabadi Rodrigo Quian Quiroga 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.