Higher order statistics-based radial basis function network for evoked potentials.
In this study, higher order statistics-based radial basis function network (RBF) was proposed for evoked potentials (EPs). EPs provide useful information on diagnosis of the nervous system. They are time-varying signals typically buried in ongoing EEG, and have to be extracted by special methods. RBF with least mean square (LMS) algorithm is an effective method to extract EPs. However, using LMS algorithm usually encounters gradient noise amplification problem, i.e., its performance is sensitive to the selection of step sizes and additional noise. Higher order statistics technique, which can effectively suppress Gaussian and symmetrically distributed non-Gaussian noises, was used to reduce gradient noise amplification problem on adaptation in this study. Simulations and human experiments were also carried out in this study.
Lin
Bor-Shyh
BS
Institute of Electrical Engineering, National Taiwan University, Changhua, Taipei 50307, Taiwan, R.O.C. borshyhlin@ntu.edu.tw
Lin
Bor-Shing
BS
Chong
Fok-Ching
FC
Lai
Feipei
F
D000465:Algorithms; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D016018:Least-Squares Analysis; D009424:Nervous System Physiological Phenomena; D016011:Normal Distribution