Adaptive tracking of EEG oscillations
| Autor: | Micah M. Murray, Yann Prudat, Cédric Duchêne, Jérôme Van Zaen, Laurent Uldry, Jean-Marc Vesin, Reto Meuli |
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| Rok vydání: | 2010 |
| Předmět: |
Cross-frequency couplings
Adaptive tracking 0206 medical engineering Phase (waves) 02 engineering and technology Electroencephalography Tracking (particle physics) Radio spectrum 03 medical and health sciences 0302 clinical medicine Biological Clocks Control theory Component (UML) medicine Humans EEG Evoked Potentials Mathematical Computing Mathematics medicine.diagnostic_test business.industry Oscillation General Neuroscience Brain Signal Processing Computer-Assisted Pattern recognition Adaptation Physiological 020601 biomedical engineering Neuronal oscillations Adaptive filter Amplitude Pattern Recognition Visual Artificial intelligence business Algorithms 030217 neurology & neurosurgery |
| Zdroj: | JOURNAL OF NEUROSCIENCE METHODS |
| DOI: | 10.1016/j.jneumeth.2009.10.018 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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