Nonlinear multivariate analysis of neurophysiological signals
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TLDR
This work describes the multivariate linear methods most commonly used in neurophysiology and shows that they can be extended to assess the existence of nonlinear interdependence between signals and describes nonlinear methods based on the concepts of phase synchronization, generalized synchronization and event synchronization.About:
This article is published in Progress in Neurobiology.The article was published on 2005-09-01 and is currently open access. It has received 993 citations till now. The article focuses on the topics: Phase synchronization & Surrogate data.read more
Citations
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Journal ArticleDOI
Network modelling methods for FMRI.
Stephen M. Smith,Karla L. Miller,Gholamreza Salimi-Khorshidi,Matthew T. Webster,Christian F. Beckmann,Christian F. Beckmann,Thomas E. Nichols,Thomas E. Nichols,Joseph D. Ramsey,Mark W. Woolrich +9 more
TL;DR: There are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve.
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Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources.
TL;DR: A novel measure to quantify phase synchronization, the phase lag index (PLI), is proposed and its performance is compared to the well‐known phase coherence (PC), and to the imaginary component of coherency (IC).
Nonlinear Time Series Analysis.
TL;DR: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series and results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages.
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On the interpretation of weight vectors of linear models in multivariate neuroimaging.
Stefan Haufe,Frank C. Meinecke,Kai Görgen,Sven Dähne,John-Dylan Haynes,Benjamin Blankertz,Felix Bießmann,Felix Bießmann +7 more
TL;DR: It is demonstrated that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study, in contrast to the interpretation of backward model parameters.
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Graph theoretical analysis of complex networks in the brain
TL;DR: These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity, and increasing evidence that various types of brain disease may be associated with deviations of the functional network topology from the optimal small- world pattern.
References
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TL;DR: A revised and expanded edition of this classic reference/text, covering the latest techniques for the analysis and measurement of stationary and nonstationary random data passing through physical systems, is presented in this article.