Parameter estimation and multichannel fusion for classifying averaged ERPs

Abstract

A parameter estimation and classification fusion approach is developed to classify averaged event-related potentials (ERPs) recorded from multiple channels. It is shown that the parameters of the averaged ERP ensemble can be estimated directly from the parameters of the single-trial ensemble. The parameter estimation methods are applied to independently design a Gaussian likelihood ratio classifier for each channel. A fusion rule is formulated to classify an ERP using the classification results from all the channels. Very importantly, it is shown that parametric classifiers can be designed and evaluated without having to collect a prohibitively large number of single-trial ERPs. It is also shown that the performance of a majority rule fusion classifier is consistently superior to the rule that selects a single best channel.