A Novel Flexible Model for the Extraction of Features from Brain Signals in the Time-Frequency Domain
Joint Authors
Source
International Journal of Biomedical Imaging
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-01-21
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Heideklang, R.& Ivanova, G.. 2013. A Novel Flexible Model for the Extraction of Features from Brain Signals in the Time-Frequency Domain. International Journal of Biomedical Imaging،Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-496523
Modern Language Association (MLA)
Heideklang, R.& Ivanova, G.. A Novel Flexible Model for the Extraction of Features from Brain Signals in the Time-Frequency Domain. International Journal of Biomedical Imaging No. 2013 (2013), pp.1-12.
https://search.emarefa.net/detail/BIM-496523
American Medical Association (AMA)
Heideklang, R.& Ivanova, G.. A Novel Flexible Model for the Extraction of Features from Brain Signals in the Time-Frequency Domain. International Journal of Biomedical Imaging. 2013. Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-496523
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-496523