Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials

Fontaine, Charles; Frostig, Ron D.; Ombao, Hernando

doi:10.1016/j.ecosta.2019.06.003

Cited by 5 publications

(3 citation statements)

References 42 publications

(41 reference statements)

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“…In Fontaine et al [ 27 ], a univariate LFP microelectrode-wise change point analysis was performed on the same dataset. In their work, for various frequency bands, changes in the non-linear spectral dependence of the LFP signal is modeled using parametric copulas.…”

Section: Analysis Of Complexity Of Rat Local Field Potentials In Amentioning

confidence: 99%

Modeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals

Sundararajan

Frostig

Ombao

2020

Entropy

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In some applications, it is important to compare the stochastic properties of two multivariate time series that have unequal dimensions. A new method is proposed to compare the spread of spectral information in two multivariate stationary processes with different dimensions. To measure discrepancies, a frequency specific spectral ratio (FS-ratio) statistic is proposed and its asymptotic properties are derived. The FS-ratio is blind to the dimension of the stationary process and captures the proportion of spectral power in various frequency bands. Here we develop a technique to automatically identify frequency bands that carry significant spectral power. We apply our method to track changes in the complexity of a 32-channel local field potential (LFP) signal from a rat following an experimentally induced stroke. At every epoch (a distinct time segment from the duration of the experiment), the nonstationary LFP signal is decomposed into stationary and nonstationary latent sources and the complexity is analyzed through these latent stationary sources and their dimensions that can change across epochs. The analysis indicates that spectral information in the Beta frequency band (12–30 Hertz) demonstrated the greatest change in structure and complexity due to the stroke.

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Section: Analysis Of Complexity Of Rat Local Field Potentials In Amentioning

confidence: 99%

Modeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals

Sundararajan

Frostig

Ombao

2020

Entropy

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“…In Fontaine et al (2019), a univariate LFP microelectrode-wise change point analysis was performed on the same dataset. In their work, for various frequency bands, changes in the nonlinear spectral dependence of the LFP signal is modeled using parametric copulas.…”

Section: Analysis Of Complexity Of Rat Local Field Potentials In a St...mentioning

confidence: 99%

Modeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals

Sundararajan¹,

Frostig²,

Ombao³

2019

Preprint

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A common class of methods for analyzing of multivariate time series, stationary and nonstationary, decomposes the observed series into latent sources. Methods such as principal compoment analysis (PCA), independent component analysis (ICA) and Stationary Subspace Analysis (SSA) assume the observed multivariate process is generated by latent sources that are stationary or nonstationary. We develop a method that tracks changes in the complexity of a 32-channel local field potential (LFP) signal from a rat following an experimentally induced stroke. We study complexity through the latent sources and their dimensions that can change across epochs due to an induced shock to the cortical system. Our method compares the spread of spectral information in several multivariate stationary processes with different dimensions. A frequency specific spectral ratio (FS-ratio) statistic is proposed and its asymptotic properties are derived. The FS-ratio is blind to the dimension of the stationary process and captures the proportion of spectral information in various (user-specified) frequency bands. We apply our method to study differences in complexity and structure of the LFP before and after system shock. The analysis indicates that spectral information in the beta frequency band (12-30 Hertz) demonstrated the greatest change in structure and complexity due to the stroke.

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“…The accuracy of the proposed classifier for drowsiness detection was 94.3%, which performed best among the list models. Fontaine et al [22] adopted the EEG-based copula model to probe the changepoints of EEG signals for analysing the impact of stroke on the local field potential of rats, based on that the dependence of pre-changepoints would be different from the post-changepoints. The BIC was introduced to estimate marginal density functions.…”

Section: Introductionmentioning

confidence: 99%

Mixture Kernel Density Estimation and Remedied Correlation Matrix on the EEG-Based Copula Model for the Assessment of Visual Discomfort

Zhao

Yao

2020

Cogn Comput

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Since electroencephalogram (EEG) signals can directly provide information on changes in brain activity due to behaviour changes, how to assess visual discomfort through EEG signals attracts researchers’ attention. However, previous assessments based on time-domain EEG features lack sufficient consideration of the dependence among EEG signals, which may affect the discrimination to visual discomfort. Although the copula model can explore the dependence among variables, the EEG-based copula models still have the following deficiencies: (1) the methods ignoring the fine-grained information hidden in EEG signals could make the estimated marginal density function improper, and (2) the approaches neglecting the pseudo-correlation among data may inappropriately estimate the correlation matrix parameter of the copula density function. The mixture kernel density estimation (MKDE) and remedied correlation matrix (RCM) on the EEG-based copula model are proposed to mitigate the mentioned shortcomings. The simulation experiments show that MKDE can not only better estimate the marginal density function but also explore fine-grained information. The RCM can be closer to the real correlation matrix parameter. With the favourable quality of the proposed EEG-based model, it is used to extract time-domain EEG features to assess visual discomfort further. To our best knowledge, the extracted features present better discrimination to visual discomfort compared with the features extracted by the state-of-the-art method.

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Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials

Cited by 5 publications

References 42 publications

Modeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals

Modeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals

Modeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals

Mixture Kernel Density Estimation and Remedied Correlation Matrix on the EEG-Based Copula Model for the Assessment of Visual Discomfort

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