304 research outputs found
A Robust Method for Detecting Interdependences: Application to Intracranially Recorded EEG
We present a measure for characterizing statistical relationships between two
time sequences. In contrast to commonly used measures like cross-correlations,
coherence and mutual information, the proposed measure is non-symmetric and
provides information about the direction of interdependence. It is closely
related to recent attempts to detect generalized synchronization. However, we
do not assume a strict functional relationship between the two time sequences
and try to define the measure so as to be robust against noise, and to detect
also weak interdependences. We apply our measure to intracranially recorded
electroencephalograms of patients suffering from severe epilepsies.Comment: 29 pages, 5 figures, paper accepted for publication in Physica
Nonlinear denoising of transient signals with application to event related potentials
We present a new wavelet based method for the denoising of {\it event related
potentials} ERPs), employing techniques recently developed for the paradigm of
deterministic chaotic systems. The denoising scheme has been constructed to be
appropriate for short and transient time sequences using circular state space
embedding. Its effectiveness was successfully tested on simulated signals as
well as on ERPs recorded from within a human brain. The method enables the
study of individual ERPs against strong ongoing brain electrical activity.Comment: 16 pages, Postscript, 6 figures, Physica D in pres
Fluctuation Analysis of Human Electroencephalogram
The scaling behaviors of the human electroencephalogram (EEG) time series are
studied using detrended fluctuation analysis. Two scaling regions are found in
nearly every channel for all subjects examined. The scatter plot of the scaling
exponents for all channels (up to 129) reveals the complicated structure of a
subject's brain activity. Moment analyses are performed to extract the gross
features of all the scaling exponents, and another universal scaling behavior
is identified. A one-parameter description is found to characterize the
fluctuation properties of the nonlinear behaviors of the brain dynamics.Comment: 4 pages in RevTeX + 6 figures in ep
Conedy: a scientific tool to investigate Complex Network Dynamics
We present Conedy, a performant scientific tool to numerically investigate
dynamics on complex networks. Conedy allows to create networks and provides
automatic code generation and compilation to ensure performant treatment of
arbitrary node dynamics. Conedy can be interfaced via an internal script
interpreter or via a Python module
Kullback-Leibler and Renormalized Entropy: Applications to EEGs of Epilepsy Patients
Recently, renormalized entropy was proposed as a novel measure of relative
entropy (P. Saparin et al., Chaos, Solitons & Fractals 4, 1907 (1994)) and
applied to several physiological time sequences, including EEGs of patients
with epilepsy. We show here that this measure is just a modified
Kullback-Leibler (K-L) relative entropy, and it gives similar numerical results
to the standard K-L entropy. The latter better distinguishes frequency contents
of e.g. seizure and background EEGs than renormalized entropy. We thus propose
that renormalized entropy might not be as useful as claimed by its proponents.
In passing we also make some critical remarks about the implementation of these
methods.Comment: 15 pages, 4 Postscript figures. Submitted to Phys. Rev. E, 199
What Models and Tools can Contribute to a Better Understanding of Brain Activity?
This is the final version. Available on open access from Frontiers Media via the DOI in this recordData Availability Statement:
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.Despite impressive scientific advances in understanding the structure and function of the human brain, big challenges remain. A deep understanding of healthy and aberrant brain activity at a wide range of temporal and spatial scales is needed. Here we discuss, from an interdisciplinary network perspective, the advancements in physical and mathematical modeling as well as in data analysis techniques that, in our opinion, have potential to further advance our understanding of brain structure and function.Spanish Ministry of Science and InnovationState Research AgencySpanish Ministerio de Ciencia, Innovacion y UniversidadesCREA ACADEMIA program, Generalitat de Cataluny
Separating neural oscillations from aperiodic 1/f activity: Challenges and recommendations
Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law [Formula: see text] and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent [Formula: see text]. For investigation of either part, however, it is essential to separate the two components. In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them
Identifying phase synchronization clusters in spatially extended dynamical systems
We investigate two recently proposed multivariate time series analysis
techniques that aim at detecting phase synchronization clusters in spatially
extended, nonstationary systems with regard to field applications. The starting
point of both techniques is a matrix whose entries are the mean phase coherence
values measured between pairs of time series. The first method is a mean field
approach which allows to define the strength of participation of a subsystem in
a single synchronization cluster. The second method is based on an eigenvalue
decomposition from which a participation index is derived that characterizes
the degree of involvement of a subsystem within multiple synchronization
clusters. Simulating multiple clusters within a lattice of coupled Lorenz
oscillators we explore the limitations and pitfalls of both methods and
demonstrate (a) that the mean field approach is relatively robust even in
configurations where the single cluster assumption is not entirely fulfilled,
and (b) that the eigenvalue decomposition approach correctly identifies the
simulated clusters even for low coupling strengths. Using the eigenvalue
decomposition approach we studied spatiotemporal synchronization clusters in
long-lasting multichannel EEG recordings from epilepsy patients and obtained
results that fully confirm findings from well established neurophysiological
examination techniques. Multivariate time series analysis methods such as
synchronization cluster analysis that account for nonlinearities in the data
are expected to provide complementary information which allows to gain deeper
insights into the collective dynamics of spatially extended complex systems
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