Authors: Mahmoud Boudiaf; Moncef Benkherrat; Khaled Mansouri
Addresses: Laboratoire d'Automatique et Signaux d'Annaba (LASA), Badji Mokhtar-Annaba University, P.O. Box 12, Annaba 23000, Algeria ' ECAM-EPMI, 13 bd de l'Hautil, 95092, Cergy Pontoise Cedex, France; Laboratoire de Neurosciences Cognitives, CNRS UMR 7291, Aix Marseille Universite, Marseille, France ' Laboratoire d'Automatique et Signaux d'Annaba (LASA), Badji Mokhtar-Annaba University, P.O. Box 12, Annaba 23000, Algeria
Abstract: This study presents a method for improving the signal-to-noise ratio of single-trial event-related potentials. The method is based on adaptive linear combiner Hermite model. A variable step size least mean square algorithm is used to estimate and to adjust the parameters of the filter. The performances of the method are applied to simulated data and real event-related potential recordings. The method significantly enhances the observation of single trials and the estimation of amplitude and latency of the event-related potentials.
Keywords: adaptive linear combiner; EEG; event-related potentials; Hermite basis functions; VSS-LMS algorithm.
International Journal of Bioinformatics Research and Applications, 2018 Vol.14 No.3, pp.255 - 268
Received: 16 Jul 2016
Accepted: 19 Oct 2016
Published online: 07 Dec 2017 *