0040021 20240103182624.920060815235959.9 3 s. cav_un_epca*007663880-214-3152-0166-168BrnoUniversity of Technology2006JanJ.KozumplíkJ.ProvazníkI. electroencephalogram blind source separation independent component analysis epilepsy ictus cav_un_auth*0101212 Tichavský Petr UTIA-B Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0213020 Nielsen Jan UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0215799 Krajča V. CZ 12B 1M0572 GA MŠk cav_un_auth*0001814 1ET101210512 GA AV ČR CZ cav_un_auth*0215801 CEZ:AV0Z10750506 The presented study aims to evaluate possibility of separation of epileptic activity from the EEG data using two well known and two recently proposed algorithms for independent component analysis (ICA): FastICA, EFICA, SOBI and WASOBI. All these techniques are shown to allow to concentrate an epileptic activityin two epilepsy-related independent components out of 19 channel EEG recordings. Among the techniques, the WASOBI was shown to be a most effective one. V clanku je zkoumana moznost separace epilepticke aktivity v EEG zaznamech pomoci dvou klasickych a dvou nedavno navrzenych metod slepe separace: FastICA, EFICA, SOBI a WASOBI. Tyto metody umoznuji pri zpracovani 19-kanaloveho EEG zaznamu epileptickou aktivitu vice-mene uspesne koncentrovat ve dvou signalovych komponetach. Mezi temito metodami se algoritmus WASOBI jevi jako ten ktery umoznuje nejpresnejsi separaci. cav_un_auth*0215800 Biosignal 2006 Brno 28.06.2006-30.06.2006 CZ 2007 FH http://hdl.handle.net/11104/0133890 2006 cav_un_epca*0076638 Analysis of Biomedical Signals and Images. BIOSIGNAL 2006 80-214-3152-0 166 168 Brno University of Technology 2006 Jan J. 340 Kozumplík J. 340 Provazník I. 340