Publications

the following three recent papers dealing with the analysis of EEG can be
downloaded from my web-site.

Comments are of course welcome, all the best,
						Arthur.

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Arthur Flexer
arthur@ai.univie.ac.at
http://www.ai.univie.ac.at/~arthur/
Austrian Research Inst. for Artificial Intelligence    +43-1-5336112    (Tel)
Schottengasse 3, A-1010 Vienna, Austria		       +43-1-5336112-77 (Fax)
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Flexer A..: Data mining and electroencephalography,
Statistical Methods in Medical Research, 9: 395-413, 2000.

also available as: TR-2000-12.
ftp://ftp.ai.univie.ac.at/papers/oefai-tr-2000-12.ps.gz

An overview of Data Mining (DM) and its application to the analysis of
EEG is given by (i) presenting a working definition of DM, (ii) motivating
why EEG analysis is a challenging field of application for DM
technology and (iii) by reviewing exemplary work on DM applied to
EEG analysis. The current status of work on DM and EEG is discussed
and some general conclusions are drawn.

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Flexer A., Sykacek P., Rezek I., Dorffner G.: Using Hidden Markov Models to
build an automatic, continuous and probabilistic sleep stager,
in Amari S.-I., et al.(eds.), Proceedings of the IEEE-INNS-ENNS International
Joint Conference on Neural Networks, IJCNN 2000, Como, Italy, IEEE Computer
Society, Vol. III, 627-631, 2000.

also available as: TR-99-21.
ftp://ftp.ai.univie.ac.at/papers/oefai-tr-99-21.ps.gz

We report about an automatic continuous sleep stager which is based on
probabilistic principles employing Hidden Markov Models (HMM). Our
sleep stager offers the advantage of being objective by not relying on
human scorers, having much finer temporal resolution (1 second instead
of 30 second), and being based on solid probabilistic principles rather
than a predefined set of rules (Rechtschaffen & Kales). Results obtained
for nine whole night sleep recordings are reported.

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Flexer A., Bauer H.: Monitoring human information processing via intelligent
data analysis of EEG recordings, Intelligent Data Analysis, 4: 113-128, 2000.

also available as: TR-2000-34.
ftp://ftp.ai.univie.ac.at/papers/oefai-tr-2000-34.ps.gz

Human information processing can be monitored by analysing cognitive
evoked potentials (EP) measurable in the electro encephalogram (EEG)
during cognitive activities. In technical terms, both visualization of high
dimensional sequential data and unsupervised discovery of patterns
within this multivariate set of real valued time series is needed. Our
approach towards visualization is to discretize the sequences via vector
quantization and to perform a Sammon mapping of the codebook.
Instead of having to conduct a time-consuming 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 time series. The methods are described in
detail and results obtained for spatial and verbal information processing
are shown to be statistically valid, to yield an improvement in terms of
noise attenuation and to be well in line with psychophysiological
literature.