Phase Synchronization Analysis of EEG Signals: An Evaluation Based on Surrogate Tests (original) (raw)
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We compared parametric and nonparametric methods for assessing phase synchronization in the presence of EMG artifacts. The parametric methods used an autoregressive approach to model the data before deriving the time-frequency transform. We used two simulations: simple sinusoidal model and more complex neural mass model, to which were added simulated EMG.
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A number of tests exist to check for statistical significance of phase synchronisation within the Electroencephalogram (EEG); however, the majority suffer from a lack of generality and applicability. They also may fail to account for temporal dynamics in the phase synchronisation, regarding synchronisation as a constant state instead of a dynamical process. Therefore, a novel test is developed for identifying the statistical significance of phase synchronisation based upon a combination of work characterising temporal dynamics of multivariate time-series and Markov modelling.
Quantifying cognitive state from EEG using phase synchrony
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2013
Phase synchrony is a powerful amplitudeindependent measure that quantifies linear and nonlinear dynamics between non-stationary signals. It has been widely used in a variety of disciplines including neural science and cognitive psychology. Current time-varying phase estimation uses either the Hilbert transform or the complex wavelet transform of the signals. This paper exploits the concept of phase synchrony as a mean to discriminate face processing from the processing of a simple control stimulus. Dependencies between channel locations were assessed for two separate conditions elicited by distinct pictures (representing a human face and a Gabor patch), both flickering at a rate of 17.5 Hz. Statistical analysis is performed using the Kolmogorov-Smirnov test. Moreover, the phase synchrony measure used is compared with a measure of association that has been previously applied in the same context: the generalized measure of association (GMA). Results show that although phase synchrony ...
Neuroinformatics, 2005
Phase synchrony analysis is a relatively new concept that is being increasingly used on neurophysiological data obtained through different methodologies. It is currently believed that phase synchrony is an important signature of information binding between distant sites of the brain, especially during cognitive tasks. Electroencephalographic (EEG) recordings are the most widely used recording technique for recording brain signals and assessing phase synchrony patterns. In this study, we address the suitability of phase synchrony analysis in EEG recordings. Using geometrical
bioRxiv (Cold Spring Harbor Laboratory), 2023
The estimation of functional connectivity (FC) from electro-(EEG) or magnetoencephalographic (MEG) recordings suffers from low spatial resolution, being one of the reasons for the reduced number of sensors compared to the number of reconstructed sources of activity. This problem can be avoided by estimating FC between larger regions instead of individual sources. However, combining all the sources in each area to produce a single time series per region is far from trivial. We have used simultaneous EEG/MEG data from 11 participants and compared the FC estimates from both techniques by using different multivariate approaches. Since the underlying generators are identical for EEG and MEG, the more similar the FC estimation from both techniques is, the more accurate it is likely to be. The results show that using either the average or the root-mean-square of the bivariate source-to-source FC estimates consistently outperforms the use of a representative time series from each area. We concluded that the reconstructed activity in each brain region is too complex to be reduced to a single representative time series and that full multivariate approaches must be used to describe between-area FC from electrophysiological recordings accurately. Moreover, the high correlation between the FC values estimated from EEG and MEG suggests that the results found in the highsensitivity, low-noise MEG can be transferable to the more affordable EEG, at least when high-quality source reconstruction is used.
2009
The phase locking index (PLI) was introduced to quantify in a statistical sense the phase synchronization of two signals. It has been commonly used to process biosignals. In this article, we investigate the PLI for measuring the interdependency of cortical source signals (CSSs) recorded in the Electroencephalogram (EEG). To this end, we consider simple analytical models for the mapping of simulated CSSs into the EEG. For these models, the PLI is investigated analytically and through numerical simulations. An evaluation is made of the sensitivity of the PLI to the amount of crosstalk between the sources through biological tissues of the head. It is found that the PLI is a useful interdependency measure for CSSs, especially when the amount of crosstalk is small. Another common interdependency measure is the coherence. A direct comparison of both measures has not been made in the literature so far. We assess the performance of the PLI and coherence for estimation and detection purposes based on, respectively, a normalized variance and a novel statistical measure termed contrast. Based on these performance
Detection of Phase Synchronization in Multivariate Single Brain Signals by a Clustering Approach
Coordinated Activity in the Brain, 2009
Analog signals of the cerebral cortex in behaving subjects frequently express strong oscillatory components. To investigate functional interactions among different areas of the cortex, it is biologically plausible to determine dependencies of oscillatory signals such as their phase relation both within and across areas. The chapter introduces a cluster approach algorithm to detect phase synchronization in single brain signals. The introduced synchronization index allows for the extraction of time windows, which exhibit strong phase synchronization in all examined time series. This kind of phase synchronization is highly non-stationary and is called mutual phase synchronization. Further the assessment of single trials with respect to the trial average revealed that a number of features in time-frequency space are common to different trials.
MSC and Phase Synchronization Analysis for Classification of Eeg
International Journal of Advance Research and Innovative Ideas in Education, 2018
EEG signals are often used for disease diagnosis and behavioral analyses. These signals are highly non-stationary. Therefore a common practice of EEG analysis is, breaking it into many band limited components and then extrapolating the underlying features of these temporal components. This paper is about analysis of mean spectral coherence and phase synchronization of EEG components, across various sub-bands. Coherence is studied for different pair of EEG signals, acquired from corresponding locations of the two hemisphere of brains. Coherence feature is unrestricted to amplitude distortions which are caused by various signal processing steps. This work is centered on MSC computation by using wavelet transform. By using suitable frequency resolutions, or in other ward by using different number of samples while computing FFT for different wavelet sub-band components, wavelet based computation results into time-frequency resolved MSC coefficients. This helps in preserving the underlyi...
Computational Intelligence and Neuroscience, 2018
Most EEG phase synchrony measures are of bivariate nature. Those that are multivariate focus on producing global indices of the synchronization state of the system. Thus, better descriptions of spatial and temporal local interactions are still in demand. A framework for characterization of phase synchrony relationships between multivariate neural time series is presented, applied either in a single epoch or over an intertrial assessment, relying on a proposed clustering algorithm, termed Multivariate Time Series Clustering by Phase Synchrony, which generates fuzzy clusters for each multivalued time sample and thereupon obtains hard clusters according to a circular variance threshold; such cluster modes are then depicted in Time-Frequency-Topography representations of synchrony state beyond mere global indices. EEG signals from P300 Speller sessions of four subjects were analyzed, obtaining useful insights of synchrony patterns related to the ERP and even revealing steady-state artif...
Physical Review E, 2002
We study the synchronization between left and right hemisphere rat EEG channels by using various synchronization measures, namely non-linear interdependences, phase-synchronizations, mutual information, cross-correlation and the coherence function. In passing we show a close relation between two recently proposed phase synchronization measures and we extend the definition of one of them. In three typical examples we observe that except mutual information, all these measures give a useful quantification that is hard to be guessed beforehand from the raw data. Despite their differences, results are qualitatively the same. Therefore, we claim that the applied measures are valuable for the study of synchronization in real data. Moreover, in the particular case of EEG signals their use as complementary variables could be of clinical relevance.