# Technical Reports - Query Results

Your query term was 'number = 98-07'1 report found

- 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.- 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.