Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients (original) (raw)
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IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018
Spatiotemporal evolution of synchrony dynamics among neuronal populations plays an important role in decoding complicated brain function in normal cognitive processing as well as during pathological conditions such as epileptic seizures. In this paper, a non-linear analytical methodology is proposed to quantitatively evaluate the phase-synchrony dynamics in epilepsy patients. A set of finite neuronal oscillators was adaptively extracted from a multi-channel electrocorticographic (ECoG) dataset utilizing noise-assisted multivariate empirical mode decomposition (NA-MEMD). Next, the instantaneous phases of the oscillatory functions were extracted using the Hilbert transform in order to be utilized in the mean-phase coherence analysis. The phase-synchrony dynamics were then assessed using eigenvalue decomposition. The extracted neuronal oscillators were grouped with respect to their frequency range into wideband (1-600 Hz), ripple (80-250 Hz), and fast-ripple (250-600 Hz) bands in order to investigate the dynamics of ECoG activity in these frequency ranges as seizures evolve. Drug-refractory patients with frontal and temporal lobe epilepsy demonstrated a reduction in phase-synchrony around seizure onset. However, the network phase-synchrony started to increase towards seizure end and achieved its maximum level at seizure offset for both types of epilepsy. This result suggests that hypersynchronization of the epileptic network may be an essential self-regulatory mechanism by which the brain terminates seizures.
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018
In this paper, an adaptive, non-linear, analytical methodology is proposed in order to quantitatively evaluate the instantaneous phase-synchrony dynamics in epilepsy patients. A group of finite neuronal oscillators is extracted from a multichannel electrocorticographic (ECoG) data, using the empirical mode decomposition (EMD). The instantaneous phases of the extracted oscillators are measured using the Hilbert transform in order to be utilized in the mean-phase coherence analysis. Finally, the dynamical evolution of phase-synchrony among the extracted neuronal oscillators within 1–600 Hz frequency range is assessed using eigenvalue decomposition. A different phasesynchrony dynamics was observed in two patients with frontal vs. temporal lobe epilepsy, as their seizures evolve. However, experimental results demonstrated a hypersynchrony level at seizure offset for both types of epilepsy during the ictal periods. This result suggests that hypersynchronization of the epileptic network may be a crucial, self-regulatory mechanism by which the brain terminate seizures.
Stochastic Behavior of Phase Synchronization Mapped with Seizure-Free 256-Channel Scalp EEG
2011
Abstract. We examined the spatiotemporal stochastic behavior of the phase synchronization index (SI) derived from seizure-free scalp EEG data of an adult male subject for a three minute period. It was found that in the epileptogenic area the stochastic fluctuations in theta (3-7 Hz) and low gamma (30-50 Hz) bands were higher as compared with the surrounding areas and also exhibited complex spatio-temporal patterns. This has a potential to nonivasively localize the epileptogenic areas from the seizure-free scalp EEG data. The EEG data of three minute duration was filtered in theta or low Gamma band. A detrended fluctuation analysis (DFA) was used to find the averaged stochastic fluctuations in the SI. Contour plots were constructed with 20 sec time-frames using a montage of the layout of 256 electrode positions. The phase synchronization was computed after taking Hilbert transform of the EEG data. The SI between a pair of channel was inferred from a statistical tendency to maintain a...
The Journal of neuroscience : the official journal of the Society for Neuroscience, 2005
Synchronization is a fundamental characteristic of complex systems and a basic mechanism of self-organization. A traditional, accepted perspective on epileptiform activity holds that hypersynchrony covering large brain regions is a hallmark of generalized seizures. However, a few recent reports have described substantial fluctuations in synchrony before and during ictal events, thus raising questions as to the widespread synchronization notion. In this study, we used magnetoencephalographic recordings from epileptic patients with generalized seizures and normal control subjects to address the extent of the phase synchronization (phase locking) in local (neighboring) and distant cortical areas and to explore the ongoing temporal dynamics for particular ranges of frequencies at which synchrony occurs, during interictal and ictal activity. Synchronization patterns were found to differ somewhat depending on the epileptic syndrome, with primary generalized absence seizures displaying mor...
Testing for significance of phase synchronisation dynamics in the EEG
Journal of computational neuroscience, 2013
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.
Physical review, 2022
The severe neurological disorder epilepsy affects almost 1% of the world population. For patients who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings are essential for the localization of the brain area where seizures start. Apart from the visual inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for this purpose. Among other features, regularity versus irregularity and phase coherence versus phase independence allowed characterizing brain dynamics from the measured EEG signals. Can phase irregularities also characterize brain dynamics? To address this question, we use the univariate coefficient of phase velocity variation, defined as the ratio of phase velocity standard deviation and the mean phase velocity. Beyond that, as a bivariate measure we use the classical mean phase coherence to quantify the degree of phase locking. All phase-based measures are combined with surrogates to test null hypotheses about the dynamics underlying the signals. In the first part of our analysis, we use the Rössler model system to study our approach under controlled conditions. In the second part, we use the Bern-Barcelona EEG database which consists of focal and nonfocal signals extracted from seizure-free recordings. Focal signals are recorded from brain areas where the first seizure EEG signal changes can be detected, and nonfocal signals are recorded from areas that are not involved in the seizure at its onset. Our results show that focal signals have less phase variability and more phase coherence than nonfocal signals. Once combined with surrogates, the mean phase velocity proved to have the highest discriminative power between focal and nonfocal signals. In conclusion, conceptually simple and easy to compute phase-based measures can help to detect features induced by epilepsy from EEG signals. This holds not only for the classical mean phase coherence but even more so for univariate measures of phase irregularity.
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
IEEE Transactions on Information Technology in Biomedicine, 2009
Epilepsy is one of the most common brain disorders and may result in brain dysfunction and cognitive disturbances. Epileptic seizures usually begin in childhood without being accommodated by brain damage and are tolerated by drugs that produce no brain dysfunction. In this study, cognitive function is evaluated in children with mild epileptic seizures controlled with common antiepileptic drugs. Under this prism, we propose a concise technical framework of combining and validating both linear and nonlinear methods to efficiently evaluate (in terms of synchronization) neurophysiological activity during a visual cognitive task consisting of fractal pattern observation. We investigate six measures of quantifying synchronous oscillatory activity based on different underlying assumptions. These measures include the coherence computed with the traditional formula and an alternative evaluation of it that relies on autoregressive models, an information theoretic measure known as minimum description length, a robust phase coupling measure known as phase-locking value, a reliable way of assessing generalized synchronization in state-space and an unbiased alternative called synchronization likelihood. Assessment is performed in three stages; initially, the nonlinear methods are validated on coupled nonlinear oscillators under increasing noise interference; second, surrogate data testing is performed to assess the possible nonlinear channel interdependencies of the acquired EEGs by comparing the synchronization indexes under the null hypothesis of stationary, linear dynamics; and finally, synchronization on the actual data is measured. The results on the actual data suggest that there is a significant difference between normal controls and epileptics, mostly apparent in occipital-parietal lobes during fractal observation tests.