Number of Source Signal Estimation by the Mean Squared Eigenvalue Error

Beheshti, Soosan; Sedghizadeh, Saba

doi:10.1109/tsp.2018.2870357

Cited by 23 publications

(27 citation statements)

References 31 publications

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“…To perform such algorithms, pre-knowledge about the number of components involved in the artifact is required, which is often a user-dependent process and difficult to obtain. Here, a method of estimation of the number of artifact components using Mean Square Eigenvalue Error (MSEE) [76] is proposed.…”

Section: Ocular Artifact (Oa) Removal By Subspace Methodsmentioning

confidence: 99%

“…This thesis is heavily based on the proposed denoising approach in [75] and its subspace Singular Value Decomposition (SVD) based approach in [76]. Therefore, this part of the thesis is dedicated to introducing the approach in [75], while the applications of the methods are explained in details in Chapter 3 and Chapter 4.…”

Section: Signal Denoising and Best Basis Selectionmentioning

confidence: 99%

“…The finalized derivation, as well my proposed simplification is presented in Appendix A, while the method is used in Chapter 4. Moreover, the similar approach is utilized in [76] for selection of number of eigenvalues. This method could be considered as a branch of [75] method.…”

Section: Signal Denoising and Best Basis Selectionmentioning

confidence: 99%

“…proposed in [76]. The proposed method in [76] uses the covariance matrix of the noisy signal of interest (in here matrix Ω) as shown in (3.2), to estimate the optimum number of eigenvalues by mean square eigenvalue error (MSEE) estimation.…”

Section: Eye Blink (Eb) Number Of Components (Noc) Estimate As Application Of Number Of Source Eigenvalue Error Noseementioning

confidence: 99%

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Automated Pre-Processing and Automated Post-Processing in EEG/MEG Brain Source Analysis

Sadat-Nejad¹

2021

Preprint

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Analyzing Electroencephalography (EEG)/Magnetoencephalography (MEG) brain source signals allows for a better understanding and diagnosis of various brain-related activities or injuries. Due to the high complexity of the mentioned measurements and their low spatial resolution, different techniques have been employed to enhance the quality of the obtained results. The objective of this work is to employ state-of-the-art approaches and develop algorithms with higher analysis reliability. As a pre-processing method, subspace denoising and artifact removal approaches are taken into consideration, to provide a method that automates and improves the estimation of the Number of Component (NoC) for artifacts such as Eye Blinking (EB). By using synthetic EEG-like simulation and real MEG data, it is shown that the proposed method is more reliable over the conventional manual method in estimating the NoC. For Independent Component Analysis (ICA)-based approaches, the proposed method in this thesis provides an estimation for the number of components with an accuracy of 98.7%. The thesis is also devoted to improving source localization techniques, which aims to estimate the location of the source within the brain, which elicit time-series measurements. In this context, after obtaining a practical insight into the performance of the popular L2-Regularization based approaches, a post-processing thresholding method is introduced. The proposed method improves the spatial resolution of the L2-Regularization inverse solutions, especially for Standard Low-Resolution Electromagnetic Tomography (sLORETA), which is a well-known and widely used inverse solution. As a part of the proposed method, a novel noise variance estimation is introduced, which combines the kurtosis statistical parameter and data (noise) entropy. This new noise variance estimation technique allows for a superior performance of the proposed method compared to the existing ones. The algorithm is validated on the synthetic EEG data using well-established validation metrics. It is shown that the proposed solution improves the resolution of conventional methods in the process of thresholding/denoising automatically and without loss of any critical information.

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Section: Ocular Artifact (Oa) Removal By Subspace Methodsmentioning

confidence: 99%

Section: Signal Denoising and Best Basis Selectionmentioning

confidence: 99%

Section: Signal Denoising and Best Basis Selectionmentioning

confidence: 99%

Section: Eye Blink (Eb) Number Of Components (Noc) Estimate As Application Of Number Of Source Eigenvalue Error Noseementioning

confidence: 99%

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Automated Pre-Processing and Automated Post-Processing in EEG/MEG Brain Source Analysis

Sadat-Nejad¹

2021

Preprint

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“…Many solutions to this problem from various approaches have been proposed in the literature so far, such as the well-known Akaike Information Criterion (AIC) and Minimum Description Length (MDL) [8], Random Matrix Theory (RMT)-based [9], Second ORder sTatistic of the Eigenvalues (SORTE) [10], [11], the recently proposed Bayesian information criterion variant [12], mean squared eigenvalue error [13], and many others [14]- [20]. However, all these solutions are heavily based on an assumption of spatial-whiteness of the additive noise, which essentially leads to a (matrix) rank estimation problem.…”

Section: Introductionmentioning

confidence: 99%

Blind Determination of the Number of Sources Using Distance Correlation

Weiss

Yeredor

2019

IEEE Signal Process. Lett.

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A novel blind estimate of the number of sources from noisy, linear mixtures is proposed. Based on Székely et al.'s distance correlation measure, we define the Sources' Dependency Criterion (SDC), from which our estimate arises. Unlike most previously proposed estimates, the SDC estimate exploits the full independence of the sources and noise, as well as the non-Gaussianity of the sources (as opposed to the Gaussianity of the noise), via implicit use of high-order statistics. This leads to a more robust, resilient and stable estimate w.r.t. the mixing matrix and the noise covariance structure. Empirical simulation results demonstrate these virtues, on top of superior performance in comparison with current state of the art estimates.

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Determining the Number of Sources with Diagonal Unloading in Single-Channel Mixtures

Özbek

2021

Circuits Syst Signal Process

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Number of Source Signal Estimation by the Mean Squared Eigenvalue Error

Cited by 23 publications

References 31 publications

Automated Pre-Processing and Automated Post-Processing in EEG/MEG Brain Source Analysis

Automated Pre-Processing and Automated Post-Processing in EEG/MEG Brain Source Analysis

Blind Determination of the Number of Sources Using Distance Correlation

Determining the Number of Sources with Diagonal Unloading in Single-Channel Mixtures

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