Higher Order Statistics-based Radial Basis Function Network for Evoked Potentials
Date Issued
2006
Date
2006
Author(s)
Lin, Bor-Shyh
DOI
en-US
Abstract
Evoked potentials are responses of the brain to external stimuli, such as sounds, lights and electrical stimuli. They can provide useful information in the diagnosis of nerve systems. However, evoked potentials are typically embedded in the ongoing electroencephalogram with a very poor signal-to-noise ratio. Thus, how to effectively extract evoked potentials is a difficult task. Traditionally, ensemble averaging method is most frequently used for evoked potentials. However, it requires a great number of stimuli to obtain evoked potentials. And it may lose information of trial-to-trial variation. Recently, a number of methods have been investigated for evoked potentials with a minimum of required stimuli repetitions. Adaptive filtering is one of techniques widely developed for evoked potentials. In this thesis, different kinds of schemes of adaptive filtering for evoked potentials were introduced and discussed in detail. Single-trial estimation for evoked potentials by using higher order statistics-based radial basis function network was also proposed. Higher order statistics provides a natural tolerance to symmetrically distributed stochastic signals. By using higher order statistics-based learning algorithm can effectively reduce the influence of additive noises on learning. From simulations and experiments, as our expectation, the performance by using higher order statistics-based learning algorithm is insensitive to the selection of learning rate, and is superior to that by using least mean square algorithm under different noise levels. Thus, higher order statistics-based radial basis function network may be served as a useful tool for evoked potentials.
Subjects
誘發電位
腦電活動
總體平均法
徑向基函數神經網路
Evoked potentials
Electroencephalogram
Ensemble averaging method
Radial basis function network
Higher order statistics
Type
thesis
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