A novel brain source reconstruction using a multivariate mode decomposition.

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-07-10 DOI:10.1088/1741-2552/acdffe
Hanieh Sotudeh, Sayed Mahmoud Sakhaei, Javad Kazemitabar
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引用次数: 0

Abstract

Objective. Brain source reconstruction through electroencephalogram is a challenging issue in brain research with possible applications in cognitive science as well as brain damage and dysfunction recognition. Its goal is to estimate the location of each source in the brain along with the signal being produced.Approach. In this paper, by assuming a small number of band limited sources, we propose a novel method for the problem by using successive multivariate variational mode decomposition (SMVMD). Our new method can be considered as a blind source estimation method, which means that it is capable of extracting the source signal without the knowledge of the location of the source or its lead field vector. In addition, the source location can be determined through comparing the mixing vector found in SMVMD and the lead filed vectors of the entire brain.Main results. The simulations verify that our method leads to performance improvement in comparison to the well-known localization and source signal estimation techniques such as MUltiple SIgnal Calssification (MUSIC), recursively applied and projected MUSIC, dipole fitting method, MV beamformer, and standardized low-resolution brain electromagnetic tomography.Significance. The proposed method enjoys low computational complexity. Moreover, our investigations on some experimental epileptic data confirm its superiority over the MUSIC method in the aspect of localization accuracy.

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一种基于多元模态分解的脑源重构方法。
目标。通过脑电图重建脑源是脑研究中的一个具有挑战性的问题,在认知科学以及脑损伤和功能障碍识别方面具有潜在的应用前景。它的目标是估计每个信号源在大脑中的位置以及产生的信号。本文提出了一种基于连续多元变分模态分解(SMVMD)的新方法,该方法在假设少量带限源的情况下求解该问题。我们的新方法可以被认为是一种盲源估计方法,这意味着它能够在不知道源的位置或其引线场矢量的情况下提取源信号。此外,可以通过比较SMVMD中发现的混合矢量和整个大脑的引线场矢量来确定源位置。主要的结果。仿真结果表明,与多信号分类(MUSIC)、递归应用和投影MUSIC、偶极子拟合、中压波束形成和标准化低分辨率脑电磁层析成像等知名定位和源信号估计技术相比,该方法的性能有所提高。该方法具有较低的计算复杂度。此外,我们对一些癫痫实验数据的研究证实了它在定位精度方面优于MUSIC方法。
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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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