Nonlinear dynamical analysis of EEG and MEG: review of an emerging field.

By Cornelis J. Stam. Clinical Neurophysiology, 2005

1,411 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Networks and Communications, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6e3dccf88388818bbc28e609461abf08fc89eb7a

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Citations in the corpus

Diagnosis of Alzheimer's disease from EEG signals: where are we standing? (2010, background): “An interesting alternative family of synchrony measures, referred to as state space based synchrony or generalized synchrony, stems from physics [123, 133].”
The application of graph theoretical analysis to complex networks in the brain. (2007, positive): “Based on these conflicting results, it was suggested that complexity of brain dynamics in itself might not be the most relevant parameter to understand cognitive processing (Houlihan et al., 1996; Stam, 2000, 2005).”
Visual and Auditory Brain–Computer Interfaces (2014, background): “activities such as EEG signals can be better characterized by nonlinear dynamic methods than linear methods [109, 110].”
Classification of patterns of EEG synchronization for seizure prediction. (2009, neutral): “Nonlinear, chaotic, dynamical systems have become a plausible model for many complex biological observations, including EEG waveforms (Stam, 2005).”
Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review (2012, background): “Second, 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”
EEG Based Emotion Recognition: A Tutorial and Review (2022, background): “In addition to time-frequency-domain analysis, the researchers pay more and more attention to the chaotic characteristics in the neural system activities [82].”
Graph theoretical analysis of complex networks in the brain (2007, result): “Nonlinear dynamics has been applied to the study of the brain since 1985, and has become a very active research field in itself [5,6].”
Detrended Fluctuation Analysis: A Scale-Free View on Neuronal Oscillations (2012, background): “…is growing evidence that physiological processes can exhibit fluctuations without characteristic scales and that this scale-free dynamics is important for their function (Bassingthwaighte et al., 1994; Bak, 1996; Goldberger et al., 2002; Stam, 2005; Ghosh et al., 2008; He et al., 2010; West, 2010).”
Seizure prediction: the long and winding road. (2007, methodology): “In extension to previous studies on seizure dynamics (e.g. Pijn et al., 1991; Franaszczuk et al., 1994, 1998; Bartolomei et al., 2004; Stam, 2005 and references therein) Schiff et al. (2005) successfully used canonical discrimination analysis to search for dynamically distinct stages of epileptic…”
Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources (2007, background): “Higher brain functions depend upon a delicate balance between local specialization and global integration of brain processes [Friston, 2001; Le van Quyen, 2003; Stam, 2005; Tononi et al., 1998].”
Classifying depression patients and normal subjects using machine learning techniques (2011, background): “The nonlinearity of brain limits the ability of linear analysis to provide full description of underlying dynamics [6].”
Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. (2009, neutral): “Associations between cognitive status (MMSE) and PLI or network-derived measures were assessed with Spearman’s bivariate correlation test.”
Nonlinear multivariate analysis of neurophysiological signals. (2005, methodology): “…time series analysis methods started to be applied to neurophysiological data about two decades ago (Babloyantz et al., 1985); see, e.g., (Elbert et al., 1994; Faure and Korn, 2001; Galka, 2000; Jansen, 1991; Korn and Faure, 2003; Segundo, 2001; Segundo et al., 1998; Stam, 2005) for surveys.”
Exploring EEG Features in Cross-Subject Emotion Recognition (2018, background): “Researchers have found that human brain manifests many characteristics specifically belonging to non-linear and chaotic dynamical systems; thus, the EEG signal is inherently complex, non-linear, non-stationary, and random in nature (Stam, 2005; Sanei and Chambers, 2013).”
Dynamic models of large-scale brain activity (2017, methodology): “The application of NMMs and NFMs to clinical neurophysiology data represents a fertile area, with emerging applications to many other neurological disorders, from Parkinson’s disease to dementia 126 .”