Locking-based frequency measurement and synchronization of chaotic oscillators with complex dynamics

Wavelet analysis of intermittent dynamics in nocturnal electrocardiography and electroencephalography data

This paper presents the results of a study of the characteristics of phase synchronization between electrocardiography(ECG) and electroencephalography (EEG) signals during night sleep. Polysomnographic recordings of eight generally healthy subjects and eight patients with obstructive sleep apnea syndrome were selected as experimental data. A feature of this study was the introduction of an instantaneous phase for EEG and ECG signals using a continuous wavelet transform at the heart rate frequency using the concept of time scale synchronization, which eliminated the emergence of asynchronous areas of behavior associated with the "leaving" of the fundamental frequency of the cardiovascular system. Instantaneous phase differences were examined for various pairs of EEG and ECG signals during night sleep, and it was shown that in all cases the phase difference exhibited intermittency. Laminar areas of behavior are intervals of phase synchronization, i.e., phase capture. Turbulent intervals are phase jumps of 2π. Statistical studies of the observed intermittent behavior were carried out, namely, distributions of the duration of laminar sections of behavior were estimated. For all pairs of channels, the duration of laminar phases obeyed an exponential law. Based on the analysis of the movement of the phase trajectory on a rotating plane at the moment of detection of the turbulent phase, it was established that in this case the eyelet intermittency was observed. There was no connection between the statistical characteristics of laminar phase distributions for intermittent behavior and the characteristics of night breathing disorders (apnea syndrome). It was found that changes in statistical characteristics in the phase synchronization of EEG and ECG signals were correlated with blood pressure at the time of signal recording in the subjects, which is an interesting effect that requires further research.

Real-time estimation of phase and amplitude with application to neural data

Computation of the instantaneous phase and amplitude via the Hilbert Transform is a powerful tool of data analysis. This approach finds many applications in various science and engineering branches but is not proper for causal estimation because it requires knowledge of the signal's past and future. However, several problems require real-time estimation of phase and amplitude; an illustrative example is phase-locked or amplitude-dependent stimulation in neuroscience. In this paper, we discuss and compare three causal algorithms that do not rely on the Hilbert Transform but exploit well-known physical phenomena, the synchronization and the resonance. After testing the algorithms on a synthetic data set, we illustrate their performance computing phase and amplitude for the accelerometer tremor measurements and a Parkinsonian patient's beta-band brain activity.

Microscopic correlations in the finite-size Kuramoto model of coupled oscillators

Supercritical Kuramoto oscillators with distributed frequencies can be separated into two disjoint groups: an ordered one locked to the mean field, and a disordered one consisting of effectively decoupled oscillators-at least so in the thermodynamic limit. In finite ensembles, in contrast, such clear separation fails: The mean field fluctuates due to finite-size effects and thereby induces order in the disordered group. This publication demonstrates this effect, similar to noise-induced synchronization, in a purely deterministic system. We start by modeling the situation as a stationary mean field with additional white noise acting on a pair of unlocked Kuramoto oscillators. An analytical expression shows that the cross-correlation between the two increases with decreasing ratio of natural frequency difference and noise intensity. In a deterministic finite Kuramoto model, the strength of the mean-field fluctuations is inextricably linked to the typical natural frequency difference. Therefore, we let a fluctuating mean field, generated by a finite ensemble of active oscillators, act on pairs of passive oscillators with a microscopic natural frequency difference between which we then measure the cross-correlation, at both super- and subcritical coupling.

Comparing brain connectivity metrics: a didactic tutorial with a toy model and experimental data

OBJECTIVE

The objective of this paper is to didactically compare resting state connectivity networks computed using two different methods called phase locking value (PLV) and convergent cross-mapping (CCM). PLV is a ubiquitous measure of connectivity in electrophysiological research but is less often applied to fMRI BOLD timeseries since this model-based metric assumes that oscillatory coupling is a sufficient condition for connectivity. Alternatively, CCM is a model-free method, which detects potentially nonlinear causal influences based on the ability to estimate one timeseries with another and does not assume an oscillatory structure.

APPROACH

We use a toy dataset to test the PLV and CCM algorithms under different known synchronization conditions. Additionally, experimental resting state EEG and fMRI datasets are used for comparison.

MAIN RESULTS

The results show that the resting state brain networks computed using both algorithms produce similar results for both resting state EEG and fMRI datasets. For both neuroimaging datasets, the network characteristics follow the same trends and the similarity between the computed networks, for both algorithms, is highly significant.

SIGNIFICANCE

CCM is able to identify low or one-way connection strengths better than PLV but takes exponentially longer to compute. Based on these results, PLV provides a good metric for on-line network identification because it is both computationally fast and an excellent approximation of the network computed with CCM.

Experimental phase synchronization detection in non-phase coherent chaotic systems by using the discrete complex wavelet approach

Phase synchronization may emerge from mutually interacting non-linear oscillators, even under weak coupling, when phase differences are bounded, while amplitudes remain uncorrelated. However, the detection of this phenomenon can be a challenging problem to tackle. In this work, we apply the Discrete Complex Wavelet Approach (DCWA) for phase assignment, considering signals from coupled chaotic systems and experimental data. The DCWA is based on the Dual-Tree Complex Wavelet Transform (DT-CWT), which is a discrete transformation. Due to its multi-scale properties in the context of phase characterization, it is possible to obtain very good results from scalar time series, even with non-phase-coherent chaotic systems without state space reconstruction or pre-processing. The method correctly predicts the phase synchronization for a chemical experiment with three locally coupled, non-phase-coherent chaotic processes. The impact of different time-scales is demonstrated on the synchronization process that outlines the advantages of DCWA for analysis of experimental data.

Devil's staircase in an optomechanical cavity

We study self-excited oscillations (SEOs) in an on-fiber optomechanical cavity. While the phase of SEOs randomly diffuses in time when the laser power injected into the cavity is kept constant, phase locking may occur when the laser power is periodically modulated in time. We investigate the dependence of phase locking on the amplitude and frequency of the laser-power modulation. We find that phase locking can be induced with a relatively low modulation amplitude provided that the ratio between the modulation frequency and the frequency of SEOs is tuned close to a rational number of relatively low hierarchy in the Farey tree. To account for the experimental results, a one-dimensional map, which allows evaluating the time evolution of the phase of SEOs, is theoretically derived. By calculating the winding number of the one-dimensional map, the regions of phase locking can be mapped in the plane of modulation amplitude and modulation frequency. Comparison between the theoretical predictions and the experimental findings yields a partial agreement.

Synchronization in an optomechanical cavity

We study self-excited oscillations (SEO) in an on-fiber optomechanical cavity. Synchronization is observed when the optical power that is injected into the cavity is periodically modulated. A theoretical analysis based on the Fokker-Planck equation evaluates the expected phase space distribution (PSD) of the self-oscillating mechanical resonator. A tomography technique is employed for extracting PSD from the measured reflected optical power. Time-resolved state tomography measurements are performed to study phase diffusion and phase locking of the SEO. The detuning region inside which synchronization occurs is experimentally determined and the results are compared with the theoretical prediction.

The discrete complex wavelet approach to phase assignment and a new test bed for related methods

A new approach based on the dual-tree complex wavelet transform is introduced for phase assignment to non-linear oscillators, namely, the Discrete Complex Wavelet Approach-DCWA. This methodology is able to measure phase difference with enough accuracy to track fine variations, even in the presence of Gaussian observational noise and when only a single scalar measure of the oscillator is available. So, it can be an especially interesting tool to deal with experimental data. In order to compare it with other phase detection techniques, a testbed is introduced. This testbed provides time series from dynamics similar to non-linear oscillators, such that a theoretical phase choice is known in advance. Moreover, it allows to tune different types of phase synchronization to test phase detection methods under a variety of scenarios. Through numerical benchmarks, we report that the proposed approach is a reliable alternative and that it is particularly effective compared with other methodologies in the presence of moderate to large noises.

Global analysis of phase locking in gene expression during cell cycle: the potential in network modeling

Background

In nonlinear dynamic systems, synchrony through oscillation and frequency modulation is a general control strategy to coordinate multiple modules in response to external signals. Conversely, the synchrony information can be utilized to infer interaction. Increasing evidence suggests that frequency modulation is also common in transcription regulation.

Results

In this study, we investigate the potential of phase locking analysis, a technique to study the synchrony patterns, in the transcription network modeling of time course gene expression data. Using the yeast cell cycle data, we show that significant phase locking exists between transcription factors and their targets, between gene pairs with prior evidence of physical or genetic interactions, and among cell cycle genes. When compared with simple correlation we found that the phase locking metric can identify gene pairs that interact with each other more efficiently. In addition, it can automatically address issues of arbitrary time lags or different dynamic time scales in different genes, without the need for alignment. Interestingly, many of the phase locked gene pairs exhibit higher order than 1:1 locking, and significant phase lags with respect to each other. Based on these findings we propose a new phase locking metric for network reconstruction using time course gene expression data. We show that it is efficient at identifying network modules of focused biological themes that are important to cell cycle regulation.

Conclusions

Our result demonstrates the potential of phase locking analysis in transcription network modeling. It also suggests the importance of understanding the dynamics underlying the gene expression patterns.

Event-related desynchronization of sensorimotor EEG rhythms in hemiparetic patients with acute stroke

Previous neuroimaging studies based on neurovascular coupling have shown that stroke affects both, strength and spatial extent of brain activation during upper limb movements. Here, we investigated the sub-second amplitude dynamics of a direct neuronal measure, i.e., event-related desynchronization (ERD) of EEG oscillations during finger movements, in patients with acute cortical and subcortical stroke. Acute cortical strokes were found to decrease the ERD of alpha oscillations for the affected pericentral sensorimotor areas compared to a control group. Within the cortical stroke group, the affected hemisphere showed a smaller alpha-ERD compared to the unaffected hemisphere when each was contralateral to the acting hand. Furthermore, when cortical stroke patients moved their paretic hand, the ipsilateral (i.e., contralesional) alpha-ERD was stronger than the contralateral (ipsilesional) ERD. Interestingly, the alpha-ERD amplitude in a hemisphere with a cortical stroke was relatively well preserved for non-paretic hand movements compared to alpha-ERD amplitude for paretic hand movements. This finding provides a new perspective for assessing the rehabilitative potential, which could be utilized through training of the still responsive cortical network, e.g., via enforced use of the paretic hand.

Wavelet phase synchronization and chaoticity

It has been shown that the so-called "wavelet phase" (or "time-scale") synchronization of chaotic signals is actually synchronization of smoothed functions with reduced chaotic fluctuations. This fact is based on the representation of the wavelet transform with the Morlet wavelet as a solution of the Cauchy problem for a simple diffusion equation with initial condition in a form of harmonic function modulated by a given signal. The topological background of the resulting effect is discussed. It is argued that the wavelet phase synchronization provides information about the synchronization of an averaged motion described by bounding tori instead of the fine-level classical chaotic phase synchronization.

Experimental observation of chaotic phase synchronization of a periodically modulated frequency-doubled Nd:YAG laser

We present experimental observations of chaotic phase synchronization of a sinusoidally modulated frequency-doubled Nd:YAG laser. Periodic and chaotic phase synchronization is detected using recurrence analysis and pseudo ensemble averaging applied to the observed intensity fluctuations and the modulated pump current.

Detecting weak phase locking in chaotic system with dual attractors and ill-defined phase structure

A quantitative approach was constructed for detecting phase locking in a chaotic system with complex attractor structure via stroboscopic method. We study the route to weak phase locking by analyzing the stroboscopic points. The onset of weak phase locking detected by using this statistical approach and the critical coupling strength calculated by Lyapunov exponent are matched well. Detailed structure of phase locking intensity is described by the Arnold tongue diagram.

Continuous wavelet transform in the analysis of burst synchronization in a coupled laser system

The transition to synchronization of a pair of coupled chaotic CO2 lasers is investigated numerically in a model system. This system displays episodes of bursting of different predominant frequencies. Due to the multiple time scales present in this system, we use a complex continuous wavelet transform to perform the synchronization analysis. Thus it enables us to resolve the time of occurrence as well as the frequency of an event in a given time series up to an intrinsic uncertainty. Furthermore, due to the complex nature of that wavelet transform, it yields a direct estimate of the system's phase. We show that, as the coupling strength of the laser system is increased, the mutual coherency increases differently for different frequencies. Additionally we test our method with experimental data.

Partial synchronization of chaotic systems with uncertainty

We suggest an approach to partial synchronization of chaotic systems with uncertainty. This method contains two steps: (i) transforming the synchronization system into the canonical form by the well-known feedback linearization theory and (ii) finding a control signal to ensure the asymptotic stability of the canonical system. This partial synchronization approach requires very little system information by applying a finite-time convergence technique to estimate uncertainties caused by unknown states, parameters, or structure. We also argue in detail that this partial synchronization method can be extended to parameter identification, (sub)structure estimation, and even phase detection. Several examples are presented to illustrate the partial synchronization approach suggested.

Detection of synchronization between chaotic signals: An adaptive similarity-based approach

We present an adaptive similarity-based approach to detect generalized synchronization (GS) with n:m phase synchronization (PS), where n and m are integers and one of them is 1. This approach is based on the similarity index (SI) and Gaussian mixture model with the minimum description length criterion. The clustering method, which is shown to be superior to the closeness and connectivity of a continuous function, is employed in this study to detect the existence of GS with n:m PS. We conducted a computer simulation and a finger-lifting experiment to illustrate the effectiveness of the proposed method. In the simulation of a Rössler-Lorenz system, our method outperformed the conventional SI, and GS with 2:1 PS within the coupled system was found. In the experiment of self-paced finger-lifting movement, cortico-muscular GS with 1:2 and 1:3 PS was found between the surface electromyogram signals on the first dorsal interossei muscle and the magnetoencephalographic data in the motor area. The GS with n:m PS ( n or m=1 ) has been simultaneously resolved from both simulation and experiment. The proposed approach thereby provides a promising means for advancing research into both nonlinear dynamics and brain science.

Quasi-movements: a novel motor-cognitive phenomenon

We introduce quasi-movements and define them as volitional movements which are minimized by the subject to such an extent that finally they become undetectable by objective measures. They are intended as overt movements, but the absence of the measurable motor responses and the subjective experience make quasi-movements similar to motor imagery. We used the amplitude dynamics of electroencephalographic alpha oscillations as a marker of the regional involvement of cortical areas in three experimental tasks: movement execution, kinesthetic motor imagery, and quasi-movements. All three conditions were associated with a significant suppression of alpha oscillations over the sensorimotor hand area of the contralateral hemisphere. This suppression was strongest for executed movements, and stronger for quasi-movements than for motor imagery. The topography of alpha suppression was similar in all three conditions. Proprioceptive sensations related to quasi-movements contribute to the assumption that the "sense of movement" can originate from central efferent processes. Quasi-movements are also congruent with the postulated continuity between motor imagery and movement preparation/execution. We also show that in healthy subjects quasi-movements can be effectively used in brain-computer interface research leading to a significantly smaller classification error ( approximately 47% of relative decrease) in comparison to the errors obtained with conventionally used motor imagery strategies.

Control, synchronization, and replicability of aperiodic spike trains

Aperiodic spike sequences, characterized by an almost constant spike amplitude and randomly distributed interspike intervals, are studied experimentally in an electrochemical cell. In the first set of experiments, these aperiodic spike trains are converted to regular spike sequences using periodic forcing. Subsequently, synchronization of two such irregular spike time series is achieved for an appropriate bidirectional coupling. Finally, reproducibility of these irregular spike profiles is evoked by virtue of an externally superimposed stochastic stimulus.

Experimental observation of different types of chaotic synchronization in an electrochemical cell

Chaotic synchronization for a pair of electrochemical oscillators is studied experimentally. The underlying bidirectional coupling between the two oscillators is achieved by immersing the two anodes in a common electrolytic solution. The horizontal distance between these two electrodes determines the strength of the coupling constant. On monotonically decreasing the distance between the two anodes, different domains of chaotic synchronization, namely, no, phase, lag, and complete synchronization, are identified. Furthermore, dynamics from the different transition intervals are also characterized.

Two types of phase synchronization destruction

In this paper we report that there are two different types of destruction of the phase synchronization (PS) regime of chaotic oscillators depending on the parameter mismatch as well as in the case of the classical synchronization of periodic oscillators. When the parameter mismatch of the interacting chaotic oscillators is small enough, the PS breaking takes place without the destruction of the phase coherence of chaotic attractors of oscillators. Alternatively, due to the large frequency detuning, the PS breaking is accomplished by loss of the phase coherence of the chaotic attractors.

Localized short-range correlations in the spectrum of the equal-time correlation matrix

We suggest a procedure to identify those parts of the spectrum of the equal-time correlation matrix C where relevant information about correlations of a multivariate time series is induced. Using an ensemble average over each of the distances between eigenvalues, all nearest-neighbor distributions can be calculated individually. We present numerical examples, where (a) information about cross correlations is found in the so-called "bulk" of eigenvalues (which generally is thought to contain only random correlations) and where (b) the information extracted from the lower edge of the spectrum of C is statistically more significant than that extracted from the upper edge. We apply the analysis to electroencephalographic recordings with epileptic events.

Synchronization phenomena for a pair of locally coupled chaotic electrochemical oscillators: a survey

Chaotic synchronization of two locally coupled electrochemical oscillators is studied numerically. Both bidirectional and unidirectional couplings are considered. For both these coupling scenarios, varying the characteristics of the coupling terms (functional form and/or strength) reveals a wide variety of synchronization phenomena. Standard diagnostic tests are performed to verify and classify the different types of synchronizations observed.

Dynamic causal modeling of evoked responses in EEG and MEG

Neuronally plausible, generative or forward models are essential for understanding how event-related fields (ERFs) and potentials (ERPs) are generated. In this paper, we present a new approach to modeling event-related responses measured with EEG or MEG. This approach uses a biologically informed model to make inferences about the underlying neuronal networks generating responses. The approach can be regarded as a neurobiologically constrained source reconstruction scheme, in which the parameters of the reconstruction have an explicit neuronal interpretation. Specifically, these parameters encode, among other things, the coupling among sources and how that coupling depends upon stimulus attributes or experimental context. The basic idea is to supplement conventional electromagnetic forward models, of how sources are expressed in measurement space, with a model of how source activity is generated by neuronal dynamics. A single inversion of this extended forward model enables inference about both the spatial deployment of sources and the underlying neuronal architecture generating them. Critically, this inference covers long-range connections among well-defined neuronal subpopulations. In a previous paper, we simulated ERPs using a hierarchical neural-mass model that embodied bottom-up, top-down and lateral connections among remote regions. In this paper, we describe a Bayesian procedure to estimate the parameters of this model using empirical data. We demonstrate this procedure by characterizing the role of changes in cortico-cortical coupling, in the genesis of ERPs. In the first experiment, ERPs recorded during the perception of faces and houses were modeled as distinct cortical sources in the ventral visual pathway. Category-selectivity, as indexed by the face-selective N170, could be explained by category-specific differences in forward connections from sensory to higher areas in the ventral stream. We were able to quantify and make inferences about these effects using conditional estimates of connectivity. This allowed us to identify where, in the processing stream, category-selectivity emerged. In the second experiment, we used an auditory oddball paradigm to show that the mismatch negativity can be explained by changes in connectivity. Specifically, using Bayesian model selection, we assessed changes in backward connections, above and beyond changes in forward connections. In accord with theoretical predictions, there was strong evidence for learning-related changes in both forward and backward coupling. These examples show that category- or context-specific coupling among cortical regions can be assessed explicitly, within a mechanistic, biologically motivated inference framework.

Discretization of frequencies in delay coupled oscillators

We study the dynamics of two mutually coupled chaotic oscillators with a time delayed coupling. Due to the delay, the allowed frequencies of the oscillators are shown to be discretized. The phenomenon is observed in the case when the delay is much larger than the characteristic period of the solitary uncoupled oscillator.

Synchronization of spectral components and its regularities in chaotic dynamical systems

The chaotic synchronization regime in coupled dynamical systems is considered. It has been shown that the onset of a synchronous regime is based on the appearance of a phase relation between the interacting chaotic oscillator frequency components of Fourier spectra. The criterion of synchronization of spectral components as well as the measure of synchronization has been discussed. The universal power law has been described. The main results are illustrated by coupled Rössler systems, Van der Pol and Van der Pol-Duffing oscillators.

Detection and characterization of changes of the correlation structure in multivariate time series

We propose a method based on the equal-time correlation matrix as a sensitive detector for phase-shape correlations in multivariate data sets. The key point of the method is that changes of the degree of synchronization between time series provoke level repulsions between eigenstates at both edges of the spectrum of the correlation matrix. Consequently, detailed information about the correlation structure of the multivariate data set is imprinted into the dynamics of the eigenvalues and into the structure of the corresponding eigenvectors. The performance of the technique is demonstrated by application to N(f)-tori, autoregressive models, and coupled chaotic systems. The high sensitivity, the comparatively small computational effort, and the excellent time resolution of the method recommend it for application to the analysis of complex, spatially extended, nonstationary systems.

Synchronization of non-phase-coherent chaotic electrochemical oscillations

Experiments on phase and generalized synchronization of two coupled, nonidentical chaotic electrochemical oscillations are presented. We adapt measures of characterizing synchronization of a non-phase-coherent chaotic behavior and compare its properties and physicochemical mechanism to those of a phase-coherent behavior. Phase synchronization sets in along with the onset of generalized synchronization for the non-phase-coherent oscillations in contrast to phase-coherent oscillations in which the phase synchronization usually occurs at a weaker coupling strength.

An approach to chaotic synchronization

This paper deals with the chaotic oscillator synchronization. An approach to the synchronization of chaotic oscillators has been proposed. This approach is based on the analysis of different time scales in the time series generated by the coupled chaotic oscillators. It has been shown that complete synchronization, phase synchronization, lag synchronization, and generalized synchronization are the particular cases of the synchronized behavior called "time-scale synchronization." The quantitative measure of chaotic oscillator synchronous behavior has been proposed. This approach has been applied for the coupled Rössler systems and two coupled Chua's circuits.

Breaking a secure communication scheme based on the phase synchronization of chaotic systems

A security analysis of a recently proposed secure communication scheme based on the phase synchronization of chaotic systems is presented. It is shown that the system parameters directly determine the cipher text waveform, hence it can be readily broken by system parameter estimation from the cipher text signal.

Frequency entrainment of nonautonomous chaotic oscillators

We give evidence of frequency entrainment of dominant peaks in the chaotic spectra of two coupled chaotic nonautonomous oscillators. At variance with the autonomous case, the phenomenon is here characterized by the vanishing of a previously positive Lyapunov exponent in the spectrum, which takes place for a broad range of the coupling strength parameter. Such a state is studied also for the case of chaotic oscillators with ill-defined phases due to the absence of a unique center of rotation. Different phase synchronization indicators are used to circumvent this difficulty.

Three types of transitions to phase synchronization in coupled chaotic oscillators

We study the effect of noncoherence on the onset of phase synchronization of two coupled chaotic oscillators. Depending on the coherence properties of oscillations characterized by the phase diffusion, three types of transitions to phase synchronization are found. For phase-coherent attractors this transition occurs shortly after one of the zero Lyapunov exponents becomes negative. At rather strong phase diffusion, phase locking manifests a strong degree of generalized synchronization, and occurs only after one positive Lyapunov exponent becomes negative. For intermediate phase diffusion, phase synchronization sets in via an interior crises of the hyperchaotic set.

Oscillatory and rotatory synchronization of chaotic autonomous phase systems

The existence of rotatory, oscillatory, and oscillatory-rotatory synchronization of two coupled chaotic phase systems is demonstrated in the paper. We find four types of transition to phase synchronization depending on coherence properties of motions, characterized by phase variable diffusion. When diffusion is small the onset of phase synchronization is accompanied by a change in the Lyapunov spectrum; one of the zero Lyapunov exponents becomes negative shortly before this onset. If the diffusion of the phase variable is strong then phase synchronization and generalized synchronization, occur simultaneously, i.e., one of the positive Lyapunov exponents becomes negative, or generalized synchronization even sets in before phase synchronization. For intermediate diffusion the phase synchronization appears via interior crisis of the hyperchaotic set. Soft and hard transitions to phase synchronization are discussed.