Data-driven approach to the estimation of connectivity and time delays in the coupling of interacting neuronal subsystems

Silchenko, A. N.; Adamchic, Ilya; Pawelczyk, Norbert; Hauptmann, Christian; Maarouf, Mohammad; Sturm, Volker; Tass, Peter A.

doi:10.1016/j.jneumeth.2010.06.004

Cited by 30 publications

(31 citation statements)

References 88 publications

(116 reference statements)

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“…The SE analysis was performed for trials of baseline EEG and trials of stimulus-response EEG. To reduce noise, each EEG trial 10 was obtained by time-averaging 20 non-overlapping subsegments of 1 s duration which were filtered to within the standard SSVEP frequency range of interest (13)(14)(15)(16)(17)(18)(19)(20). The recording sampling frequency was 1200 Hz.…”

Section: (B) Intrinsic Measures Of Respiratory Sinus Arrhythmiamentioning

confidence: 99%

“…More recently, phase synchrony (PS)-the temporal locking of phase information [25,26]-has been examined using univariate EMD [2], BEMD [27] and MEMD [28]. Compared with conventional bandpass filters, it was shown in [19] how EMDbased partial directed coherence-a measure for determining directionality in coupled outputsbetter estimates inter-hemispheric features of patients with Parkinson's disease. An MHHS model for characterizing imagined motor actions has shown that asymmetry, a measure which characterizes the lateralization of amplitude information between two components, is more accurately extracted compared with approaches based on the Fourier spectrum [21].…”

Section: Introductionmentioning

confidence: 99%

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Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework

2015

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A novel multi-scale approach for quantifying both inter-and intra-component dependence of a complex system is introduced. This is achieved using empirical mode decomposition (EMD), which, unlike conventional scale-estimation methods, obtains a set of scales reflecting the underlying oscillations at the intrinsic scale level. This enables the datadriven operation of several standard data-association measures (intrinsic correlation, intrinsic sample entropy (SE), intrinsic phase synchrony) and, at the same time, preserves the physical meaning of the analysis. The utility of multi-variate extensions of EMD is highlighted, both in terms of robust scale alignment between system components, a pre-requisite for inter-component measures, and in the estimation of feature relevance. We also illuminate that the properties of EMD scales can be used to decouple amplitude and phase information, a necessary step in order to accurately quantify signal dynamics through correlation and SE analysis which are otherwise not possible. Finally, the proposed multi-scale framework is applied to detect directionality, and higher order features such as coupling and regularity, in both synthetic and biological systems.

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Section: (B) Intrinsic Measures Of Respiratory Sinus Arrhythmiamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework

2015

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“…MIbased BCI systems can be a novel interaction option for those with motor disabilities because they do not require voluntary muscle control [4]. The physiological basis for such an MI paradigm is the mu (8)(9)(10)(11)(12) and beta rhythms (18)(19)(20)(21)(22)(23)(24)(25) in the EEG, which are found in the motor cortex region of the brain when subjects imagine movement of their hands or fingers [5]. Currently, bandpass filters, such as infinite impulse response (IIR) filters, are often used to extract the power features in the frequency bands relevant to MI tasks [1].…”

Section: Introductionmentioning

confidence: 99%

“…The Granger causality, proposed for use in econometric models [7], is the most popular approach for detecting the directed influences between different activities [8]. Partial directed coherence (PDC) [9] is one of the best methods for determining such causal interdependencies, particularly in the frequency domain [8].…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have investigated the functional connectivity that occurs during MI tasks to demonstrate the ability of the connectivity feature to improve the performance of the MI BCI system [4,5]. Furthermore, the EMD algorithm has been used to improve the accuracy of brain connectivity information during mental tasks [8,15]. However, most previous studies have used single-channel EMD algorithms to analyze multichannel EEG connectivity.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Brain Connectivity during Motor Imagery Tasks using Noise-Assisted Multivariate Empirical Mode Decomposition

Lee¹,

Kim²,

Song³

et al. 2016

Journal of Electrical Engineering and Technology

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-The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noiseassisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

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On Complexity and Phase Effects in Reconstructing the Directionality of Coupling in Non-linear Systems

Vakorin

Krakovska

McIntosh

2014

Understanding Complex Systems

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Data-driven approach to the estimation of connectivity and time delays in the coupling of interacting neuronal subsystems

Cited by 30 publications

References 88 publications

Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework

Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework

Estimation of Brain Connectivity during Motor Imagery Tasks using Noise-Assisted Multivariate Empirical Mode Decomposition

On Complexity and Phase Effects in Reconstructing the Directionality of Coupling in Non-linear Systems

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