Adaptive regularization network based neural modeling paradigm for nonlinear adaptive estimation of cerebral evoked potentials.
In this paper we report an adaptive regularization network (ARN) approach to realizing fast blind separation of cerebral evoked potentials (EPs) from background electroencephalogram (EEG) activity with no need to make any explicit assumption on the statistical (or deterministic) signal model. The ARNs are proposed to construct nonlinear EEG and EP signal models. A novel adaptive regularization training (ART) algorithm is proposed to improve the generalization performance of the ARN. Two adaptive neural modeling methods based on the ARN are developed and their implementation and performance analysis are also presented. The computer experiments using simulated and measured visual evoked potential (VEP) data have shown that the proposed ARN modeling paradigm yields computationally efficient and more accurate VEP signal estimation owing to its intrinsic model-free and nonlinear processing characteristics.
Zhang
Jian-Hua
JH
Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield S1 3JD, UK. zhangjh71@yahoo.com
Böhme
Johann F
JF
D000465:Algorithms; D016477:Artifacts; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods; D005071:Evoked Potentials; D005074:Evoked Potentials, Visual; D005246:Feedback; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted