Nonlinear multivariate analysis of neurophysiological signals.

By Ernesto Pereda, R. Quian Quiroga, Joydeep Bhattacharya. Progress in neurobiology, 2005

1,094 citations recorded by the source corpus.

Multivariate time series analysis is extensively used in neurophysiology with the aim of studying the relationship between simultaneously recorded signals. Recently, advances on information theory and nonlinear dynamical systems theory have allowed the study of various types of synchronization from time series. In this work, we first describe the multivariate linear methods most commonly used in neurophysiology and show that they can be extended to assess the existence of nonlinear interdependence between signals. We then review the concepts of entropy and mutual information followed by a detailed description of nonlinear methods based on the concepts of phase synchronization, generalized synchronization and event synchronization. In all cases, we show how to apply these methods to study different kinds of neurophysiological data. Finally, we illustrate the use of multivariate surrogate data test for the assessment of the strength (strong or weak) and the type (linear or nonlinear) of interdependence between neurophysiological signals.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b217d34b31cedc63bc5b109662b6e15bcae5b0f7

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Citations in the corpus

Diagnosis of Alzheimer's disease from EEG signals: where are we standing? (2010, background): “The (magnitude) coherence function is an extension of the correlation coefficient from time-domain to frequency domain; it measures linear correlations in frequency domain [125].”
Graph theoretical analysis of complex networks in the brain (2007, background): “Recently, the focus in studies of nonlinear brain dynamics has shifted from trying to detect chaotic dynamics to studying nonlinear interactions between brain areas [ 10 ,11].”
Instantaneous and lagged measurements of linear and nonlinear dependence between groups of multivariate time series: frequency decomposition (2007, methodology): “physics definition (see e.g. Rosenblum et al 1996). The basic idea behind this definition has been adapted and used to great advantage in the neurosciences (Tass et al 1998, Quian-Quiroga et al 2002, Pereda et al 2005, Stam et al 2007), as in for example, the analysis of pairs of time series of meas”
Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions (2014, background): “Third, the number of a priori parameters should be limited not only to reduce the cost of determining appropriate values for each signal pair but also to reduce the possibility of erroneous results caused by inappropriate parameter choice (Pereda et al., 2005).”
Broadband Criticality of Human Brain Network Synchronization (2009, neutral): “[22].”
Transfer entropy—a model-free measure of effective connectivity for the neurosciences (2010, background): “Frequency domain variants of GC such as the partial directed coherence or the directed transfer function fall in the same category (Pereda et al. 2005).”
Surrogate data for hypothesis testing of physical systems (2018, background): “[148] review multivariate analysis specifically for neurophysiological time series, and include an in-depth discussion of surrogates in this context.”
Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources (2007, background): “To determine whether PLI is significantly larger than zero, one may introduce surrogate data [see, e.g., appendix A in Pereda et al., 2005].”
Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review (2012, background): “…while over the last decades a wealth of linear and more recently nonlinear methods for quantifying this structure from time series have been devised [60,61], most of them, in addition to making restrictive hypotheses as to the type of underlying dynamics, are vulnerable to even low levels of noise.”
Network modelling methods for FMRI (2011, neutral): “…at www.vis.caltech.edu/~rodri/programs/ synchro.m, which provides three related measures of nonlinear interdependence utilising generalised synchronisation; for detailed descriptions of these measures and the differences between them, see Quian Quiroga et al. (2002) and Pereda et al. (2005).”
HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity (2013, background): “In neurophysiology, different analytical tools stemming from these and related concepts (Pereda et al. 2005) have added to the "traditional" set of linear methods of multivariate time series analysis, such as the cross-correlation or the coherence.”
Mapping Information Flow in Sensorimotor Networks (2006, methodology): “A variety of measures of directed information transfer (and ‘‘causal dependency’’) are available; they rely on the use of univariate and multivariate time-series analysis and embedding techniques [26–29], probabilistic graphical models (e.”
A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG (2010, positive): “A considerable amount of research has recently been devoted to improving the sensitivity of EEG for detecting fluctuations in EEG synchrony; more specifically, a large variety of measures has been proposed to quantify EEG synchrony, stemming from a wide spectrum of disciplines, such as physics, info”
Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution (2012, positive): “Functional interactions between sources of oscillatory activity can be captured by quantifying the phase relationship between their time-series (see Pereda et al. (2005) for a review of coupling measures ).”