Time-varying dimension analysis of EEG using adaptive principal component analysis and model selection

P. Celka; M. Mesbah; M. Keir; B. Boashash; P. Colditz

doi:10.1109/IEMBS.2000.898003

Time-varying dimension analysis of EEG using adaptive principal component analysis and model selection

P. Celka, M. Mesbah, M. Keir, B. Boashash, P. Colditz

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

This paper present a new approach to the analysis of non-stationary possibly nonlinear time series. It is based on an adaptive autocorrelation eigenspectrum computation known as APEX together with a model selection rule. New concepts of stochastic instantaneous embedding dimension and time averaged instantaneous embedding dimension are introduced. The motivation for this new approach is the analysis of newborn electroencephalogram for which non-stationarity is a crutial property. Experimental data are analyzed using the proposed scheme.

Original language	English
Pages (from-to)	1404-1407
Number of pages	4
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	2
DOIs	https://doi.org/10.1109/IEMBS.2000.898003
Publication status	Published - 2000
Externally published	Yes

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

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10.1109/IEMBS.2000.898003

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AU - Colditz, P.

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Time-varying dimension analysis of EEG using adaptive principal component analysis and model selection

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