Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients

By Florian Mormann, Klaus Lehnertz, P. David, Christian Erich Elger. 2000

1,279 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/d7f8349d181d3705d1a74c0c1bbbd1181ebc76a5

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Citations in the corpus

Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources (2007, methodology): “Epoch length 4,096 samples (13.083 s). r 1188 r et al., 2005; Mormann et al., 2000].”
Differential entropy feature for EEG-based vigilance estimation (2013, background): “Various EEG features can be classified into four categories: time series feature [9], spectral feature [5], spatial synchronization feature [10], and complexity measure feature [11].”
Nonlinear multivariate analysis of neurophysiological signals. (2005, background): “The second one is termed as mean phase coherence (Hoke et al., 1989; Mormann et al., 2000), although it has been also called the phase locking value (Lachaux et al., 1999) or the synchrony factor (Tallon-Baudry et al., 2001) in neurophysiological applications.”
Coupling functions:universal insights into dynamical interaction mechanisms (2017, methodology): “…methods have been designed (Bahraminasab et al., 2008; Chicharro and Andrzejak, 2009; Faes et al., 2011; Jamšek et al., 2010; Marwan et al., 2007; Mormann et al., 2000; Paluš and Stefanovska, 2003; Rosenblum and Pikovsky, 2001; Smirnov and Bezruchko, 2009; Staniek and Lehnertz, 2008; Sun et…”
A Note on the Phase Locking Value and its Properties (2013, methodology): “The most commonly used phase interaction measure is the Phase Locking Value (PLV), the absolute value of the mean phase difference between the two signals expressed as a complex unit-length vector (Lachaux et al., 1999; Mormann et al., 2000).”
Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution (2012, positive): “The Phase Coherence quantifies the phase coupling between two signals as follows (Mardia, 1972; Mormann et al., 2000):”
HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity (2013, background): “PLV is also referred to in the literature as Mean Phase Coherence (Mormann 2000) when dealing with continuous data, instead of evoked responses.”
Generalising the Kuramoto Model for the study of Neuronal Synchronisation in the Brain (2007, background): “Neuronal synchronisation also plays a role in vision [1], movement [2], memory [3] and epilepsy [ 4-10 ].”
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review (2020, methodology): “r preictal pattern [22]. 1999: Change in dynamical similarity before seizure onset was observed by Le Van Quyen [23]. 2002: Drop in similarity index before seizure onset was observed by Navarro et al [25]. 2003:De Clercqand Winterhalderchallenged the performance of similarity index measure for seizu”
Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony. (2001, methodology): “Furthermore, several neurological diseases such as epilepsy (Mormann et al., 2000) and Parkinson’s disease (Tass et al., 1998) manifest as a pathological form of the synchronization process.”
Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals (2015, background): “Mean phase coherence, which is a statistical measure of phase synchronization, is studied using EEG signals recorded from patients with temporal lobe epilepsy [21].”
Seizure prediction: the long and winding road. (2007, background): “It could be caused by a sudden and abrupt transition, in which case it would not be preceded by detectable dynamical changes in the EEG.”
Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. (2005, methodology): “Phase synchronization can be computed using the Hilbert transform (Mormann et al., 2000; Tass et al., 1998) or by means of wavelets analysis (Lachaux et al., 1999).”