Dynamic decomposition of spatiotemporal neural signals
Publication year
2017Number of pages
37 p.
Source
Plos Computational Biology, 13, 5, (2017), article e1005540ISSN
Publication type
Article / Letter to editor

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Organization
SW OZ DCC SMN
SW OZ DCC AI
Neuroinformatics
Journal title
Plos Computational Biology
Volume
vol. 13
Issue
iss. 5
Languages used
English (eng)
Subject
Action, intention, and motor control; Cognitive artificial intelligence; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication; NeuroinformaticsAbstract
Author summary In neuroscience, researchers are often interested in the modulations of specific signal components (e.g., oscillations in a particular frequency band), that have to be extracted from a background of both rhythmic and non-rhythmic activity. As the interfering background signals often have higher amplitude than the component of interest, it is crucial to develop methods that are able to perform some sort of signal decomposition. In this paper, we introduce a Bayesian decomposition method that exploits a prior dynamical model of the neural temporal dynamics in order to extract signal components with well-defined dynamic features. The method is based on Gaussian process regression with prior distributions determined by the covariance functions of linear stochastic differential equations. Using simulations and analysis of real MEG data, we show that these informed prior distributions allow for the extraction of interpretable dynamic components and the estimation of relevant signal modulations. We generalize the method to the analysis of spatiotemporal cortical activity and show that the framework is intimately related to well-established source-reconstruction techniques.
This item appears in the following Collection(s)
- Academic publications [202923]
- Electronic publications [101091]
- Faculty of Science [31885]
- Faculty of Social Sciences [27123]
- Open Access publications [69755]
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