0380079 20240111140819.520120911235959.9 000310623800298 5 s. P cav_un_epca*0380077978-1-4673-1068-02076-14651484-1488BucharestEURASIP2012 electroencephalogram brain-computer Interface underdetermined blind source separation cav_un_auth*0108100 Koldovský Zbyněk Stochastická informatika Department of Stochastic Informatics SI SI UTIA-B Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0274170 Phan A. H. JP cav_un_auth*0101212 Tichavský Petr Stochastická informatika Department of Stochastic Informatics SI SI UTIA-B Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0272321 Cichocki A. JP http://library.utia.cas.cz/separaty/2012/SI/tichavsky-a treatment of eeg data by underdetermined blind source separation for motor imagery classification.pdf 283 kB GAP103/11/1947 GA ČR CZ cav_un_auth*0301478 Brain-Computer Interfaces (BCI) controlled through imagined movements cannot work properly without a correct classification of EEG signals. The difficulty of this problem consists in low signal-to-noise ratio, because EEG may contain strong signal components that are not related to motor imagery. In this paper, these artifact components are to be suppressed using a recently proposed underdetermined blind source separation method and a novel MMSE beamformer. We use these tools to remove unwanted components of EEG to increase the classification accuracy of the BCI system. In our experiments with several datasets, the classification is improved by up to 10%. cav_un_auth*0283080 20th European Signal Processing Conference (EUSIPCO 2012) Bukurešť 27.08.2012-31.08.2012 RO 2013 FH 4 PR RVO:67985556 http://hdl.handle.net/11104/0210892 2012 000310623800298 WOS 283 kB cav_un_epca*0380077 20th European Signal Processing Conference (EUSIPCO 2012) 978-1-4673-1068-0 2076-1465 1484 1488 Bucharest EURASIP 2012