Search arXiv⌕ Search

arXiv · 0706.1776

Coherence and phase synchronization: generalization to pairs of multivariate time series, and removal of zero-lag contributions

Abstract

Coherence and phase synchronization between time series corresponding to different spatial locations are usually interpreted as indicators of the connectivity between locations. In neurophysiology, time series of electric neuronal activity are essential for studying brain interconnectivity. Such signals can either be invasively measured from depth electrodes, or computed from very high time resolution, non-invasive, extracranial recordings of scalp electric potential differences (EEG: electroencephalogram) and magnetic fields (MEG: magnetoencephalogram) by means of a tomography such as sLORETA (standardized low resolution brain electromagnetic tomography). There are two problems in this case. First, in the usual situation of unknown cortical geometry, the estimated signal at each brain location is a vector with three components (i.e. a current density vector), which means that coherence and phase synchronization must be generalized to pairs of multivariate time series. Second, the inherent low spatial resolution of the EEG/MEG tomography introduces artificially high zero-lag coherence and phase synchronization. In this report, solutions to both problems are presented. Two additional generalizations are briefly mentioned: (1) conditional coherence and phase synchronization; and (2) non-stationary time-frequency analysis. Finally, a non-parametric randomization method for connectivity significance testing is outlined. The new connectivity measures proposed here can be applied to pairs of univariate EEG/MEG signals, as is traditional in the published literature. However, these calculations cannot be interpreted as connectivity, since it is in general incorrect to associate an extracranial electrode or sensor to the underlying cortex.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Roberto D. Pascual-Marqui. 2007-07-12. Coherence and phase synchronization: generalization to pairs of multivariate time series, and removal of zero-lag contributions. https://arxiv.org/abs/0706.1776

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Estimation of Treatment Effects Under Nonstationarity via the Truncated Policy Gradient Estimator

Randomized experiments (or A/B tests) are widely used to evaluate interventions in dynamic systems such as recommendation platforms, marketplaces, and digital health. In these settings, interventions affect both current and future system states, so estimating the global average treatment effect (GATE) requires accounting for temporal dynamics, which is especially challenging in the presence of nonstationarity; existing approaches suffer from high bias, high variance, or both. In this paper, we address this challenge via the novel Truncated Policy Gradient (TPG) estimator, which replaces instantaneous outcomes with short-horizon outcome trajectories. The estimator admits a policy gradient interpretation: it is a truncation of the first-order approximation to the GATE, yielding explicit bias and variance bounds in nonstationary Markovian settings. For a fixed truncation size, we establish a central limit theorem and, under an additional mean-stability condition, the consistency of a variance estimator for nonstationary single-trajectory data. We validate our theory with two real-world case studies. The results show that relative to existing approaches, a well-calibrated TPG estimator can achieve a favorable balance between bias and variance in nonstationary settings, highlighting the value of the policy-gradient perspective for designing effective estimators under complex dynamics.

stat.ME↗

Weak Factor Analysis via Temporally Weighted PCA

Principal component analysis (PCA) is arguably the most widely used approach for large-dimensional factor analysis. While it is effective when the factors are sufficiently strong, it can be inconsistent when the factors are weak and/or the noise has complex dependence structure. We argue that the inconsistency often stems from bias and introduce a general approach to restore consistency. Specifically, we propose a general weighting scheme for PCA and show that suitable weights can restore consistency for much weaker factors under the dependent-noise structures allowed by our model. Under the stronger signal-to-noise conditions needed for inference, we also establish asymptotic normality of the loading and factor estimators. While an oracle weighting matrix may require knowledge about the factors and covariance of the idiosyncratic noise that are not known a priori, we develop an agnostic approach to adaptively choose from a large class of weighting matrices that can be viewed as PCA for weighted linear combinations of auto-covariances among the observations. Theoretical and numerical results demonstrate the merits of our methodology over the usual PCA and other recently developed techniques for large-dimensional approximate factor models.

stat.ME↗

RAPSEM: Identifying Latent Mediators Without Sequential Ignorability via a Rank-Preserving Structural Equation Model

Standard structural equation models (SEMs) are often used to identify latent mediators. However, valid inference typically relies on the strong, frequently violated Sequential Ignorability assumption. We introduce the Rank-Preserving Structural Equation Model (RAPSEM), which increases robustness through G-estimation while maintaining the measurement model's integrity through a two-stage method of moments (2SMM) for factor score corrections. RAPSEM replaces the no unmeasured mediator-outcome confounding with the weaker no unobserved effect modification assumption. By leveraging treatment randomization, RAPSEM achieves identification in a manner equivalent to instrumental variable estimation through structurally emerging instruments. Specifically, identification relies on treatment-covariate interactions that influence the mediator but have no direct effect on the outcome, allowing researchers to utilize natural heterogeneity in treatment response as a testable source of identification. We provide a robustness assessment for the core identifying assumption and establish the consistency and asymptotic normality of the resulting estimator. Simulation studies demonstrate that RAPSEM remains unbiased under unobserved confounding, whereas standard SEM yields biased results. RAPSEM achieves reasonable power for sample sizes above 500, depending on the strength of the structural instruments. The method is implemented in the accompanying rapsem R package, and its practical utility is illustrated through an empirical example from educational research. The code is available at https://github.com/PsychometricsMZ/RAPSEM.

stat.ME↗