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dc.contributor.author STRUNGARU, Rodica
dc.contributor.author UNGUREANU, G. Mihaela
dc.date.accessioned 2019-10-27T13:59:23Z
dc.date.available 2019-10-27T13:59:23Z
dc.date.issued 2011
dc.identifier.citation STRUNGARU, Rodica, UNGUREANU, G. Mihaela. Advanced EEG Signal Processing. In: ICNBME-2011. International conference on Nanotechnologies and Biomedical Engineering. German-moldovan workshop on Novel Nanomaterials for Electronic, Photonic and Biomedical Applications: proc. of the intern. conf., July 7-8, 2011. Chişinău, 2011, pp. 373-376. ISBN 978-9975-66-239-0. en_US
dc.identifier.isbn 978-9975-66-239-0
dc.identifier.uri http://repository.utm.md/handle/5014/5420
dc.description.abstract The study investigates the performance of some EEG signal processing methods in detecting the signal variations within the Event-Related Potential (ERP) and in extracting the EEG effective connectivity, and the obtained results are discussed. The advantage of applying the Independent Component Analysis (ICA) in EEG analysis is also considered. The EEG data are recorded in the framework of BCI 2005 competition, during a motor imager task, and includes segments of Event-Related (De)synchronization, revealed be the proposed signal processing methods: Event-Related Spectral Perturbation, , the Inter-Trial Phase Coherence, the Inter-Trial Linear Coherence and the Event Related Cross- Coherence. The effective connectivity is analyzed in time and frequency domain, by applying the Granger Causality Index (GCI) and the Partial-Directed Coherence (PDC) respectively, as time-variant or time-invariant methods. en_US
dc.language.iso en en_US
dc.publisher Technical University of Moldova en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject EEG en_US
dc.subject electroencephalography en_US
dc.title Advanced EEG Signal Processing en_US
dc.type Article en_US


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