Adaptive tracking of EEG oscillations.

Details

Serval ID
serval:BIB_A6EB3C27BC86
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Adaptive tracking of EEG oscillations.
Journal
Journal of Neuroscience Methods
Author(s)
Van Zaen J., Uldry L., Duchêne C., Prudat Y., Meuli R.A., Murray M.M., Vesin J.M.
ISSN
1872-678X (Electronic)
ISSN-L
0165-0270
Publication state
Published
Issued date
2010
Volume
186
Number
1
Pages
97-106
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
Neuronal oscillations are an important aspect of EEG recordings. These oscillations are supposed to be involved in several cognitive mechanisms. For instance, oscillatory activity is considered a key component for the top-down control of perception. However, measuring this activity and its influence requires precise extraction of frequency components. This processing is not straightforward. Particularly, difficulties with extracting oscillations arise due to their time-varying characteristics. Moreover, when phase information is needed, it is of the utmost importance to extract narrow-band signals. This paper presents a novel method using adaptive filters for tracking and extracting these time-varying oscillations. This scheme is designed to maximize the oscillatory behavior at the output of the adaptive filter. It is then capable of tracking an oscillation and describing its temporal evolution even during low amplitude time segments. Moreover, this method can be extended in order to track several oscillations simultaneously and to use multiple signals. These two extensions are particularly relevant in the framework of EEG data processing, where oscillations are active at the same time in different frequency bands and signals are recorded with multiple sensors. The presented tracking scheme is first tested with synthetic signals in order to highlight its capabilities. Then it is applied to data recorded during a visual shape discrimination experiment for assessing its usefulness during EEG processing and in detecting functionally relevant changes. This method is an interesting additional processing step for providing alternative information compared to classical time-frequency analyses and for improving the detection and analysis of cross-frequency couplings.
Keywords
Adaptation, Physiological/physiology, Algorithms, Biological Clocks/physiology, Brain/physiology, Electroencephalography/methods, Evoked Potentials/physiology, Humans, Mathematical Computing, Pattern Recognition, Visual/physiology, Signal Processing, Computer-Assisted
Pubmed
Web of science
Create date
24/11/2009 11:16
Last modification date
20/08/2019 15:11
Usage data