Dynamic Electrical Source Imaging (DESI) of Seizures and Interictal Epileptic Discharges Without Ensemble Averaging

Erem, Burak; Hyde, Daniel C.; Peters, Jurriaan M.; Duffy, Frank H.; Warfield, Simon K.

doi:10.1109/tmi.2016.2595329

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…combined with the recent technologies in computer science such as manifold learning [8], matrix/tensor factorization [7,39,42], time-series model [31], and neural networks [44]. In [39,42], a higher-order extension of delay embedding transform has been proposed, and combined with Tucker decomposition in higher-order delay-embedded space.…”

Section: Introductionmentioning

confidence: 99%

Soft Smoothness for Audio Inpainting Using a Latent Matrix Model in Delay-embedded Space

Yokota¹

2022

Preprint

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Here, we propose a new reconstruction method of smooth time-series signals. A key concept of this study is not considering the model in signal space, but in delay-embedded space. In other words, we indirectly represent a time-series signal as an output of inverse delay-embedding of a matrix, and the matrix is constrained. Based on the model under inverse delay-embedding, we propose to constrain the matrix to be rank-1 with smooth factor vectors. The proposed model is closely related to the convolutional model, and quadratic variation (QV) regularization. Especially, the proposed method can be characterized as a generalization of QV regularization. In addition, we show that the proposed method provides the softer smoothness than QV regularization. Experiments of audio inpainting and declipping are conducted to show its advantages in comparison with several existing interpolation methods and sparse modeling.

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Section: Introductionmentioning

confidence: 99%

Soft Smoothness for Audio Inpainting Using a Latent Matrix Model in Delay-embedded Space

Yokota¹

2022

Preprint

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“…Calculating the voltage distribution throughout a patient-specific head model is a key component of the forward problem of EEG source localization. The forward problem has been solved in previous studies using a preoperative brain model [4,7,[10][11]. However, a more efficient method for computing the voltage terms is required for patient-specific applications and efficient implementation into the clinical workflow.…”

Section: Introductionmentioning

confidence: 99%

A Flux-Conservative Finite Difference Scheme for Anisotropic Bioelectric Problems

Bourantas

Zwick

Warfield

et al. 2020

Computational Biomechanics for Medicine

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We present a flux-conservative finite difference (FCFD) scheme for solving inhomogeneous anisotropic bioelectric problems. The method applies directly on the raw medical image data without the need for sophisticated image analysis algorithms to define the interface between materials with different electrical conductivities. We demonstrate the accuracy of the method by comparison with analytical solution. Results for a patient-specific head model highlight the applicability of the method.

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“…Reconstructing a brain source signal from EEG/MEG measurements is known as EEG/MEG source localization or EEG/MEG source imaging (ESI) [5]. The ESI techniques have been used in several clinical and/or brain research applications such as the study of language mechanisms, cognition process and sensory function with a brain-computer interface [6], the localization of primary sensory cortex in evoked potentials for surgical candidates [7], and the localization of the irritative zone in focal epilepsy [8] [9].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Prior

Liu¹,

Wang²,

Lou³

et al. 2019

Preprint

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Brain source imaging is an important method for noninvasively characterizing brain activity using Electroencephalogram (EEG) or Magnetoencephalography (MEG) recordings. Traditional EEG/MEG Source Imaging (ESI) methods usually assume that either source activity at different time points is unrelated, or that similar spatiotemporal patterns exist across an entire study period. The former assumption makes ESI analyses sensitive to noise, while the latter renders ESI analyses unable to account for timevarying patterns of activity. To effectively deal with noise while maintaining flexibility and continuity among brain activation patterns, we propose a novel probabilistic ESI model based on a hierarchical graph prior. Under our method, a spanning tree constraint ensures that activity patterns have spatiotemporal continuity. An efficient algorithm based on alternating convex search is presented to solve the proposed model and is provably convergent. Comprehensive numerical studies using synthetic data on a real brain model are conducted under different levels of signal-to-noise ratio (SNR) from both sensor and source spaces. We also examine the EEG/MEG data in a real application, in which our ESI reconstructions are neurologically plausible. All the results demonstrate significant improvements of the proposed algorithm over the benchmark methods in terms of source localization performance, especially at high noise levels.

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Dynamic Electrical Source Imaging (DESI) of Seizures and Interictal Epileptic Discharges Without Ensemble Averaging

Cited by 7 publications

References 25 publications

Soft Smoothness for Audio Inpainting Using a Latent Matrix Model in Delay-embedded Space

Soft Smoothness for Audio Inpainting Using a Latent Matrix Model in Delay-embedded Space

A Flux-Conservative Finite Difference Scheme for Anisotropic Bioelectric Problems

Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Prior

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