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OFAI-TR-98-07 ( 86kB g-zipped PostScript file)

Discovery of common subsequences in cognitive evoked potentials

Arthur Flexer, Herbert Bauer

This work is about developing a new method for the analysis of evoked potentials (EP) of cognitive activities that combines methods from statistics and sequence alignment 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 sequence of the original high dimensional vectors is replaced by a sequence of prototypical codebook vectors obtained from a clustering procedure. A dimensionality reduction technique is applied to obtain an ordered one-dimensional representation of codebook vectors which allows for the depiction of the original sequences as one-dimensional time series. As a result, instead of having to 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. This result is further corroborated by a one-way analysis of variance.

Keywords: Data Mining, Sequence Analysis, Clustering, Visualization, Application

Citation: Flexer A., Bauer H.: Discovery of common subsequences in cognitive evoked potentials, in Zytkow J.M. & Quafafou M.(eds.), Principles of Data Mining and Knowledge Discovery, Second European Symposium, PKDD '98, Proceedings, Lecture Notes in Artificial Intelligence 1510, p.309-317, 1998.