title: Assessment of artefact suppression by ICA and spatial filtering on reduced sets of EEG signals.
abstract: In recorded EEG signals, the signal components under interest are typically embedded in noise and artefacts. Independent Component Analysis has been demonstrated to be very successful at signal-to-noise ratio enhancement and artefact suppression, but mainly on a large set of EEG channels (20 or more) and typically on signals from healthy young subjects. In this paper, we assess the artefact suppression performance of five different ICA methods (AMUSE, FASTICA, RUNICA, SOBI and THINICA) combined with four different spatial filters on reduced sets of EEG channels from elderly tremor patients. Results demonstrate that a suitable combination of ICA and spatial filtering can effectively suppress artefacts in clinical EEG signals, even on very small sets with only three EEG channels.
authors.affiliation: Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia. matjaz.divjak@ uni-mb.si
authors.forename: Matjaž
authors.initials: M
authors.lastname: Divjak
authors.forename: Damjan
authors.initials: D
authors.lastname: Zazula
authors.forename: Aleš
authors.initials: A
authors.lastname: Holobar
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D016477:Artifacts; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008297:Male; D008875:Middle Aged; D059629:Signal-To-Noise Ratio