title: Gain optimized cosine transform domain LMS algorithm for adaptive filtering of EEG.
abstract: The most common adaptive filtering method is based on the least mean square (LMS) algorithm, which updates the filter coefficients by a gradient based method. The convergence properties of the LMS algorithm can be improved by updating the filter coefficients in the frequency domain. This work presents a new LMS algorithm, which updates the filter coefficients in the cosine transform domain. Instead of a constant gain factor in the coefficient updating the present method uses a time-varying optimized gain factor. This yields a considerably improved convergence performance. The algorithm was applied to the EEG activity analysis of freely behaving rats.
authors.affiliation: Department of Applied Physics, University of Kuopio, Finland. hannu.olkkonen@uku.fi
authors.forename: H
authors.initials: H
authors.lastname: Olkkonen
authors.forename: P
authors.initials: P
authors.lastname: Pesola
authors.forename: A
authors.initials: A
authors.lastname: Valjakka
authors.forename: L
authors.initials: L
authors.lastname: Tuomisto
mesh_terms: D000465:Algorithms; D000818:Animals; D016477:Artifacts; D001522:Behavior, Animal / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D005625:Frontal Lobe / Q000502:physiology; D019579:Neocortex / Q000502:physiology; D051381:Rats; D011919:Rats, Inbred Strains; D017208:Rats, Wistar; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors