A FUZZY CLUSTERING APPROACH TO EP ESTIMATION

The problem of extracting a useful signal (a response) buried in relat ively high amplitude noise has been investigated, under the conditions of low signal-to-noise ratio. In particular, we present a method for detecting the ''true'' response of the brain resulting from repeated a uditory stimulation, based on selective averaging of single-trial evok ed potentials. Selective averaging is accomplished in two steps. First , an unsupervised fuzzy-clustering algorithm is employed to identify g roups of trials with similar characteristics, using a performance inde x as an optimization criterion. Then, typical responses are obtained b y ensemble averaging of all trials in the same group. Similarity among the resulting estimates is quantified through a synchronization measu re, which accounts for the percentage of time that the estimates are i n phase, The performance of the classifier is evaluated with synthetic signals of known characteristics, and its usefulness is demonstrated with real electrophysiological data obtained from normal volunteers.