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International Journal of Biomedical Imaging
Volume 2013 (2013), Article ID 759421, 12 pages
http://dx.doi.org/10.1155/2013/759421
Research Article

A Novel Flexible Model for the Extraction of Features from Brain Signals in the Time-Frequency Domain

Institut für Informatik, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany

Received 20 July 2012; Revised 28 September 2012; Accepted 21 November 2012

Academic Editor: Juan Ruiz-Alzola

Copyright © 2013 R. Heideklang and G. Ivanova. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Electrophysiological signals such as the EEG, MEG, or LFPs have been extensively studied over the last decades, and elaborate signal processing algorithms have been developed for their analysis. Many of these methods are based on time-frequency decomposition to account for the signals’ spectral properties while maintaining their temporal dynamics. However, the data typically exhibit intra- and interindividual variability. Existing algorithms often do not take into account this variability, for instance by using fixed frequency bands. This shortcoming has inspired us to develop a new robust and flexible method for time-frequency analysis and signal feature extraction using the novel smooth natural Gaussian extension (snaGe) model. The model is nonlinear, and its parameters are interpretable. We propose an algorithm to derive initial parameters based on dynamic programming for nonlinear fitting and describe an iterative refinement scheme to robustly fit high-order models. We further present distance functions to be able to compare different instances of our model. The method’s functionality and robustness are demonstrated using simulated as well as real data. The snaGe model is a general tool allowing for a wide range of applications in biomedical data analysis.