Central-tendency estimation and nearest-estimate classification of event related potentials

Abstract

Event related potentials (ERPs) are modeled as random vectors in order to determine multivariate central-tendency (C-T) estimates of ERPs such as the arithmetic mean, geometric mean, harmonic mean, median, tri-mean, trimmed-mean, and the Winsorized mean. Additionally, it is shown that the C-T estimates can be used to implement various forms of minimum-distance classifiers for individual channels and for single-channel heterogeneous, multi-channel homogeneous, and multi-channel heterogeneous-homogenous ERP classification through decision fusion. The study also focuses on answering the following related questions: (a) How do the C-T ERP estimates compare with each other? (b) How do the performances of nearest-estimate classifiers compare with each other? (c) For a given ERP channel, do the heterogeneous nearest-estimate classifiers offer complementary information for improving performance through decision fusion? (d) Do the homogeneous nearest-estimate classifiers of different channels offer complementary information for improving performance through decision fusion? (e) Can the performance be improved by fusing the decisions of all or a selected subset of the entire classifier ensemble? These questions are answered by designing estimation and classification experiments using real 6-channel ERPs. It is shown that although the operations to compute the vector C-T estimates can be quite different, the ERP estimates are similar with respect to their overall waveform shapes and peak latencies. Furthermore, the results of the classification experiments show that by fusing homogeneous nearest-estimate classifier decisions across multiple channels, the classification accuracy can be improved significantly when compared with the accuracies of individual channel classifiers.