IZLOČANJE MOTENJ V VEČKANALNIH ELEKTROENCEFALOGRAMIH

Bratoš, Luka

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Title:	IZLOČANJE MOTENJ V VEČKANALNIH ELEKTROENCEFALOGRAMIH
Authors:	ID Bratoš, Luka (Author) ID Holobar, Aleš (Mentor) More about this mentor...
Files:	UNI_Bratos_Luka_2011.pdf (2,39 MB) MD5: 441270F13B48E8CFCBF35240CB21F92C PID: 20.500.12556/dkum/cf69717f-f832-44cb-937d-b26cff98a254
Language:	Slovenian
Work type:	Bachelor thesis/paper
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	V diplomskem delu smo sistematično preučili in ovrednotili učinkovitost različnih algoritmov slepe ločitve signalov (SOBI, JADER, FASTICA, RUNICA) pri odstranjevanju artefaktov v večkanalnih elektroencefalogramih (EEG). Osredotočili smo se na časovno zahtevnost, na stopnjo dušenja artefaktov in na stabilnost konvergence omenjenih algoritmov. Podrobneje smo preučili učinkovitost odstranjevanja artefaktov zaradi utripanja z očmi, premikanja jezika, žvečenja, lateralnega premikanja oči ter premikanja glave. Ugotovili smo, da vsi testirani algoritmi, razen algoritma RUNICA, učinkovito odstranjujejo artefakte, in potrdili v uvodu zastavljeno tezo diplomskega dela, da je s postopki slepe ločitve signalov mogoče, tudi v primeru manjšega nabora kanalov EEG, učinkovito odstraniti šum in artefakte v signalih EEG.
Keywords:	elektroencefalogram, EEG, slepa ločitev signalov, EEGLAB, sLORETA, anotacija signalov, artefakti, razmerje artefakt-signal
Place of publishing:	Maribor
Publisher:	[L. Bratoš]
Year of publishing:	2011
PID:	20.500.12556/DKUM-20644
UDC:	621.3:[591.185:616.8](043.2)
COBISS.SI-ID:	15658774
NUK URN:	URN:SI:UM:DK:CYJZAQDU
Publication date in DKUM:	28.09.2011
Views:	2399
Downloads:	206
Metadata:
Categories:	KTFMB - FERI
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Secondary language

Language:	English
Title:	ARTEFACT REJECTION IN MULTICHANNEL ELECTROENCEPHALOGRAMS
Abstract:	We have systematically studied and assessed the efficiency of various blind source separation algorithms (SOBI, JADER, FASTICA, RUNICA) in removing the artefacts in multichannel electroencephalograms (EEG). We have mainly focused on computational complexity of tested algorithms, level of their suppression of artefacts and on stability of their convergence. In particular, the efficiency in removing of the artefacts due to eye blinking, tongue movements, chewing, lateral eye movements and head movements has been assessed. All tested algorithms, except RUNICA, have demonstrated significant level of artefact suppression at relatively low computational costs. Out of the algorithms tested, SOBI offered the superior performance. We therefore conclude that blind signal separation is effective in removing of noise and artefacts in multichannel EEG, even in the case of small number of EEG channels.
Keywords:	electroencephalogram, EEG, blind signal separation, EEGLAB, sLORETA, signal annotation, artefact, artefact-signal ratio

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