A Graph Fourier Transform Based Bidirectional Long Short-Term Memory Neural Network for Electrophysiological Source Imaging

Electrophysiological source imaging (ESI) refers to the process of reconstructing underlying activated sources on the cortex given the brain signal measured by Electroencephalography (EEG) or Magnetoencephalography (MEG). Due to the ill-posed nature of ESI, solving ESI requires the design of neuroph...
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Autor*in:	Meng Jiao [verfasserIn] Guihong Wan [verfasserIn] Yaxin Guo [verfasserIn] Dongqing Wang [verfasserIn] Hang Liu [verfasserIn] Jing Xiang [verfasserIn] Feng Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	electroencephalography source localization inverse problem graph Fourier transform BiLSTM

Übergeordnetes Werk:	In: Frontiers in Neuroscience - Frontiers Media S.A., 2008, 16(2022)
Übergeordnetes Werk:	volume:16 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fnins.2022.867466

Katalog-ID:	DOAJ031824684

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520			\|a Electrophysiological source imaging (ESI) refers to the process of reconstructing underlying activated sources on the cortex given the brain signal measured by Electroencephalography (EEG) or Magnetoencephalography (MEG). Due to the ill-posed nature of ESI, solving ESI requires the design of neurophysiologically plausible regularization or priors to guarantee a unique solution. Recovering focally extended sources is more challenging, and traditionally uses a total variation regularization to promote spatial continuity of the activated sources. In this paper, we propose to use graph Fourier transform (GFT) based bidirectional long-short term memory (BiLSTM) neural network to solve the ESI problem. The GFT delineates the 3D source space into spatially high, medium and low frequency subspaces spanned by corresponding eigenvectors. The low frequency components can naturally serve as a spatially low-band pass filter to reconstruct extended areas of source activation. The BiLSTM is adopted to learn the mapping relationship between the projection of low-frequency graph space and the recorded EEG. Numerical results show the proposed GFT-BiLSTM outperforms other benchmark algorithms in synthetic data under varied signal-to-noise ratios (SNRs). Real data experiments also demonstrate its capability of localizing the epileptogenic zone of epilepsy patients with good accuracy.
650		4	\|a electroencephalography
650		4	\|a source localization
650		4	\|a inverse problem
650		4	\|a graph Fourier transform
650		4	\|a BiLSTM
653		0	\|a Neurosciences. Biological psychiatry. Neuropsychiatry
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700	0		\|a Hang Liu \|e verfasserin \|4 aut
700	0		\|a Jing Xiang \|e verfasserin \|4 aut
700	0		\|a Feng Liu \|e verfasserin \|4 aut
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topic_facet	electroencephalography source localization inverse problem graph Fourier transform BiLSTM Neurosciences. Biological psychiatry. Neuropsychiatry
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container_title	Frontiers in Neuroscience
authorswithroles_txt_mv	Meng Jiao @@aut@@ Guihong Wan @@aut@@ Yaxin Guo @@aut@@ Dongqing Wang @@aut@@ Hang Liu @@aut@@ Jing Xiang @@aut@@ Feng Liu @@aut@@
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callnumber-first	R - Medicine
author	Meng Jiao
spellingShingle	Meng Jiao misc RC321-571 misc electroencephalography misc source localization misc inverse problem misc graph Fourier transform misc BiLSTM misc Neurosciences. Biological psychiatry. Neuropsychiatry A Graph Fourier Transform Based Bidirectional Long Short-Term Memory Neural Network for Electrophysiological Source Imaging
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topic_title	RC321-571 A Graph Fourier Transform Based Bidirectional Long Short-Term Memory Neural Network for Electrophysiological Source Imaging electroencephalography source localization inverse problem graph Fourier transform BiLSTM
topic	misc RC321-571 misc electroencephalography misc source localization misc inverse problem misc graph Fourier transform misc BiLSTM misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc electroencephalography misc source localization misc inverse problem misc graph Fourier transform misc BiLSTM misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	A Graph Fourier Transform Based Bidirectional Long Short-Term Memory Neural Network for Electrophysiological Source Imaging
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title_full	A Graph Fourier Transform Based Bidirectional Long Short-Term Memory Neural Network for Electrophysiological Source Imaging
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author_browse	Meng Jiao Guihong Wan Yaxin Guo Dongqing Wang Hang Liu Jing Xiang Feng Liu
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title_sort	graph fourier transform based bidirectional long short-term memory neural network for electrophysiological source imaging
callnumber	RC321-571
title_auth	A Graph Fourier Transform Based Bidirectional Long Short-Term Memory Neural Network for Electrophysiological Source Imaging
abstract	Electrophysiological source imaging (ESI) refers to the process of reconstructing underlying activated sources on the cortex given the brain signal measured by Electroencephalography (EEG) or Magnetoencephalography (MEG). Due to the ill-posed nature of ESI, solving ESI requires the design of neurophysiologically plausible regularization or priors to guarantee a unique solution. Recovering focally extended sources is more challenging, and traditionally uses a total variation regularization to promote spatial continuity of the activated sources. In this paper, we propose to use graph Fourier transform (GFT) based bidirectional long-short term memory (BiLSTM) neural network to solve the ESI problem. The GFT delineates the 3D source space into spatially high, medium and low frequency subspaces spanned by corresponding eigenvectors. The low frequency components can naturally serve as a spatially low-band pass filter to reconstruct extended areas of source activation. The BiLSTM is adopted to learn the mapping relationship between the projection of low-frequency graph space and the recorded EEG. Numerical results show the proposed GFT-BiLSTM outperforms other benchmark algorithms in synthetic data under varied signal-to-noise ratios (SNRs). Real data experiments also demonstrate its capability of localizing the epileptogenic zone of epilepsy patients with good accuracy.
abstractGer	Electrophysiological source imaging (ESI) refers to the process of reconstructing underlying activated sources on the cortex given the brain signal measured by Electroencephalography (EEG) or Magnetoencephalography (MEG). Due to the ill-posed nature of ESI, solving ESI requires the design of neurophysiologically plausible regularization or priors to guarantee a unique solution. Recovering focally extended sources is more challenging, and traditionally uses a total variation regularization to promote spatial continuity of the activated sources. In this paper, we propose to use graph Fourier transform (GFT) based bidirectional long-short term memory (BiLSTM) neural network to solve the ESI problem. The GFT delineates the 3D source space into spatially high, medium and low frequency subspaces spanned by corresponding eigenvectors. The low frequency components can naturally serve as a spatially low-band pass filter to reconstruct extended areas of source activation. The BiLSTM is adopted to learn the mapping relationship between the projection of low-frequency graph space and the recorded EEG. Numerical results show the proposed GFT-BiLSTM outperforms other benchmark algorithms in synthetic data under varied signal-to-noise ratios (SNRs). Real data experiments also demonstrate its capability of localizing the epileptogenic zone of epilepsy patients with good accuracy.
abstract_unstemmed	Electrophysiological source imaging (ESI) refers to the process of reconstructing underlying activated sources on the cortex given the brain signal measured by Electroencephalography (EEG) or Magnetoencephalography (MEG). Due to the ill-posed nature of ESI, solving ESI requires the design of neurophysiologically plausible regularization or priors to guarantee a unique solution. Recovering focally extended sources is more challenging, and traditionally uses a total variation regularization to promote spatial continuity of the activated sources. In this paper, we propose to use graph Fourier transform (GFT) based bidirectional long-short term memory (BiLSTM) neural network to solve the ESI problem. The GFT delineates the 3D source space into spatially high, medium and low frequency subspaces spanned by corresponding eigenvectors. The low frequency components can naturally serve as a spatially low-band pass filter to reconstruct extended areas of source activation. The BiLSTM is adopted to learn the mapping relationship between the projection of low-frequency graph space and the recorded EEG. Numerical results show the proposed GFT-BiLSTM outperforms other benchmark algorithms in synthetic data under varied signal-to-noise ratios (SNRs). Real data experiments also demonstrate its capability of localizing the epileptogenic zone of epilepsy patients with good accuracy.
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title_short	A Graph Fourier Transform Based Bidirectional Long Short-Term Memory Neural Network for Electrophysiological Source Imaging
url	https://doi.org/10.3389/fnins.2022.867466 https://doaj.org/article/1c1ee27be22740a2b17eb88f7c984f9b https://www.frontiersin.org/articles/10.3389/fnins.2022.867466/full https://doaj.org/toc/1662-453X
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author2	Meng Jiao Guihong Wan Yaxin Guo Dongqing Wang Hang Liu Jing Xiang Feng Liu
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doi_str	10.3389/fnins.2022.867466
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up_date	2024-07-03T22:38:37.363Z
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A Graph Fourier Transform Based Bidirectional Long Short-Term Memory Neural Network for Electrophysiological Source Imaging

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