Discovery of common subsequences in cognitive evoked potentials

Abstract

This work is about developing a new method for the analysis of evoked potentials of cognitive activities that combines methods from statistics and sequence afignment to tackle the following two problems: the visualization of high dimensional sequential data and the unsupervised discovery of patterns within this multivariate set of real valued time series data. The sequences of the original high dimensional vectors are transformed to discrete sequences by vector quantization plus Sammon mapping of the codebook. Instead of having to conduct a timeconsuming search for common subsequences in the set of multivariate sequential data a multiple sequence alignment procedure can be applied to the set of one-dimensional discrete symbolic time series. The methods are described in detail and the results are shown to be significantly better than those obtained for two sets of randomized artificial data.