title: The prediction of EEG signals using a feedback-structured adaptive rational function filter.
abstract: In this article, we present a feedback-structured adaptive rational function filter based on a recursive modified Gram-Schmidt algorithm and apply it to the prediction of an EEG signal that has nonlinear and nonstationary characteristics. For the evaluation of the prediction performance, the proposed filter is compared with other methods, where a single-step prediction and a multi-step prediction are considered for a short-term prediction, and the prediction performance is assessed in normalized mean square error. The experimental results show that the proposed filter shows better performance than other methods considered for the short-term prediction of EEG signals.
authors.affiliation: Department of Electrical and Computer Engineering, Yonsei University, Seoul, Korea. khs@cyber.yonsei.ac.kr
authors.forename: H S
authors.initials: HS
authors.lastname: Kim
authors.forename: T S
authors.initials: TS
authors.lastname: Kim
authors.forename: Y H
authors.initials: YH
authors.lastname: Choi
authors.forename: S H
authors.initials: SH
authors.lastname: Park
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004569:Electroencephalography; D005246:Feedback