Information Theoretic Measures for Quantifying the Integration of Neural Activity

Aviyente, Selin

doi:10.1109/ita.2007.4357556

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…The information-theoretic measures analyse information flow between two systems or between constituent subsystems of a complex system. These methods do not explicitly model the underlying interaction, and hence do not make any assumption about the underlying system [28,29].…”

Section: Information-theoretic Measuresmentioning

confidence: 99%

“…With an appropriate choice of kernel function, the TFD can have the desirable properties of preserving energy and the marginal distributions. If a spectrogram is used, then the TFD is also non-negative, which is necessary for calculating information theoretic measures [29,36].…”

Section: Information-theoretic Measuresmentioning

confidence: 99%

“…Jensen-Shannon divergence is a way of deriving distance measures from entropy. The Jensen-Shannon divergence of two timefrequency distributions , and , is given by [29]:…”

Section: Jensen-shannon Divergencementioning

confidence: 99%

“…Jensen-Rényi divergence is modification of the Jensen-Shannon divergence from an arithmetic to a geometric mean [29]:…”

Section: Jensen-rényi Divergencementioning

confidence: 99%

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Detecting synchrony in EEG: A comparative study of functional connectivity measures

Bakhshayesh

Fitzgibbon

Janani

et al. 2019

Computers in Biology and Medicine

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In neuroscience, there is considerable current interest in investigating the connections between different parts of the brain. EEG is one modality for examining brain function, with advantages such as high temporal resolution and low cost. Many measures of connectivity have been proposed, but which is the best measure to use? In this paper, we address part of this question: which measure is best able to detect connections that do exist, in the challenging situation of non-stationary and noisy data from nonlinear systems, like EEG. This requires knowledge of the true relationship between signals, hence we compare 26 measures of functional connectivity on simulated data (unidirectionally coupled Hénon maps, and simulated EEG). To determine whether synchrony is detected, surrogate data were generated and analysed, and a threshold determined from the surrogate ensemble. No measure performed best in all tested situations. The correlation and coherence measures performed best on stationary data with many samples. S-estimator, correntropy, mean-phase coherence (Hilbert), mutual information (kernel), nonlinear interdependence (S) and nonlinear interdependence (N) performed most reliably on non-stationary data with small to medium window sizes. Of these, correlation and S-estimator have execution times that scale slower with the number of channels and the number of samples. Keywords-Connectivity, EEG, biomedical signal processing, nonstationarity. Highlights:• Extensive comparative study of 26 functional connectivity measures for EEG • Measures compared on simulated noisy and nonstationary data • Surrogates used to determine threshold for significant connectivity • 8 measures performed well, choice of best depends on the particular situation • Correlation coefficient and S-estimator measures performed best overall M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 2 synchronisation means adjustment of frequencies of periodic self-sustained oscillators due to weak interaction" [2]. The concept of synchronisation has been generalised to the case of chaotic oscillatory systems with irregular behaviour. The study of synchronisation between signals from such systems has been a topic of increasing interest, and has found applications in areas such as laser dynamics, solid state physics, electronics, biology, medicine, communication and even economics. There has been wide-ranging research aimed at detecting underlying relationships (which may be nonlinear and/or nonstationary) in multi-output dynamic systems, to give useful insight into their spatio-temporal organisation [3]. Synchronisation can manifest itself in different ways, hence a large variety of measures have been proposed to quantify synchronisation between signals. Synchronisation can occur due to one source driving another, and in such situations there is a direction to the relationship. Functional connectivity measures are symmetric and so cannot detect a direction in a relationship.Effective connectivity measures are not symmetric, and do detect a direction. T...

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Section: Information-theoretic Measuresmentioning

confidence: 99%

Section: Information-theoretic Measuresmentioning

confidence: 99%

“…Jensen-Shannon divergence is a way of deriving distance measures from entropy. The Jensen-Shannon divergence of two timefrequency distributions , and , is given by [29]:…”

Section: Jensen-shannon Divergencementioning

confidence: 99%

“…Jensen-Rényi divergence is modification of the Jensen-Shannon divergence from an arithmetic to a geometric mean [29]:…”

Section: Jensen-rényi Divergencementioning

confidence: 99%

See 2 more Smart Citations

Detecting synchrony in EEG: A comparative study of functional connectivity measures

Bakhshayesh

Fitzgibbon

Janani

et al. 2019

Computers in Biology and Medicine

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“…Rényi entropy [74] based measures have recently been applied to different areas of signal processing and information theory. It has been proven that the Rényi entropy is a very robust measure [81]. The Rényi α-entropy is a generalization of the Shannon entropy and is defined as follows [74]:…”

Section: Rényi Entropymentioning

confidence: 99%

Time-Frequency Based Methods for Non-Stationary Signal Analysis with Application To EEG Signals

Feltane¹

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The analysis of electroencephalogram or EEG plays an important role in diagnosis and detection of brain related disorders like seizures. In this dissertation, we propose three new seizure detection algorithms that can classify seizure from nonseizure data with high accuracy. The first algorithm is based on time-domain features which are the approximate entropy (ApEn), the maximum singular value (MSV) and the median absolute deviation (MAD). These features were fed into the AdaBoost and the Support Vector Machine (SVM) algorithms, which were used to classify the signal as either seizure or non-seizure. The accuracy of these classifications was summarized and compared to different algorithms in the literature. In the second algorithm, the Rényi entropy was extracted from different spectral components after the EEG signal was decomposed using either Empirical Mode Decomposition (EMD) or the Discrete Dyadic Wavelet Transform (DWT). The knearest neighbor (k-NN) classifier was use to classify the seizure segments based on the extracted features. In the third algorithm, we decompose the EEG signal into subcomponents occupying different spectral sub-bands using the EMD. A decomposition energy measure was used to discard those sub-components estimated to contain mostly noise. Different time-frequency representations (TFRs) were computed of the remaining sub-components. Local energy measures were estimated and fed into a linear classifier to determine whether or not the EEG signal contained a seizure. The three algorithms were tested on noisy EEG signals from roaming rats as well as the relatively noise free human seizure from a well-known public dataset provided on-line I would like to thank my parents, my brother and my sisters; they are always supporting me and encouraging me with their love and best wishes. Finally, I would like to thank my husband, Yacine Boudria, for his love, support, understanding and patience throughout the completion of this research. v

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An Improved Minimum-Distance Texture Estimator for Speckled Data Under the $$\mathscr {G}^0$$ Model

Cassetti

Frery

2022

J Math Imaging Vis

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The G 0 distribution is an apt model for speckled data, such as SAR imagery, because it possesses the ability to characterize areas with different degrees of texture. In the monopolarized case, this distribution depends on three parameters: the texture, the scale, and the number of looks. The first one is related to the roughness of the image, so its estimation deserves special attention. This paper proposes and compares estimation methods of the texture parameter in intensity format. We treat the multilook case. The proposal is to estimate this parameter by minimizing the triangular distance between the G 0 density and an estimate of the underlying density function using asymmetric kernels. We assess the properties of these estimators with analytic results and simulation. We use actual images to evaluate the performance of our proposal.

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Information Theoretic Measures for Quantifying the Integration of Neural Activity

Cited by 7 publications

References 23 publications

Detecting synchrony in EEG: A comparative study of functional connectivity measures

Detecting synchrony in EEG: A comparative study of functional connectivity measures

Time-Frequency Based Methods for Non-Stationary Signal Analysis with Application To EEG Signals

An Improved Minimum-Distance Texture Estimator for Speckled Data Under the $$\mathscr {G}^0$$ Model

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