title: A novel neural network with Non-Recursive IIR Filters on EEG Artifacts Elimination.
abstract: The artifacts caused by various factors, EOG (electrooculogram), blink and EMG (electromyogram), in EEG (Electroencephalogram) signals increase the difficulty in analyzing them. In addition, EEG signals containing artifacts often cannot be used in analyzing them. So, it is useful and indispensable to eliminate the artifacts from EEG signals. In this paper, a neural network with non-recursive IIR (Infinite Impulse Response) filters are used to eliminate the artifacts from EEG signals. The proposed method is a new approach that is respect to slotting a non-recursive IIR filter into individual neurons of a neural network. First of all, in order to investigate the usefulness of the proposed method in eliminating the artifacts from EEG signals, we apply it to the artificial EEG signals that are weakly stationary process. As the result, the artifacts can be eliminated from EEG signals almost exactly using the proposed method, and it is suggested the proposed method should be useful in eliminating the artifacts from EEG signals.
authors.affiliation: Graduate School of Information, Production and Systems, Waseda University 2-7 Hibikino, Wakamatsu-ku, Kitakyusyu, Fukuoka 808-0135, Japan.
authors.forename: Ryota
authors.initials: R
authors.lastname: Miyazaki
authors.forename: Masakuni
authors.initials: M
authors.lastname: Ohshiro
authors.forename: Toshihiro
authors.initials: T
authors.lastname: Nishimura
authors.forename: Masayoshi
authors.initials: M
authors.lastname: Tsubai