Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals

Abstract This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning algorithm integrated with a spike-time-dep...

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Autores principales:	Samaneh Alsadat Saeedinia, Mohammad Reza Jahed-Motlagh, Abbas Tafakhori, Nikola Kasabov
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a2f0ca33db2a424b9a3bc28f530616c9
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spelling	oai:doaj.org-article:a2f0ca33db2a424b9a3bc28f530616c92021-12-02T17:52:12ZDesign of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals10.1038/s41598-021-90029-52045-2322https://doaj.org/article/a2f0ca33db2a424b9a3bc28f530616c92021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90029-5https://doaj.org/toc/2045-2322Abstract This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning algorithm integrated with a spike-time-dependent-plasticity algorithm. The models capture informative personal patterns of interaction between EEG channels, contrary to single EEG signal modeling methods or to spike-based approaches which do not use personal MRI data to pre-structure a model. The proposed models can not only learn and model accurately measured EEG data, but they can also predict signals at 3D model locations that correspond to non-monitored brain areas, e.g. other EEG channels, from where data has not been collected. This is the first study in this respect. As an illustration of the method, personalized MRI-SNN models are created and tested on EEG data from two subjects. The models result in better prediction accuracy and a better understanding of the personalized EEG signals than traditional methods due to the MRI and EEG information integration. The models are interpretable and facilitate a better understanding of related brain processes. This approach can be applied for personalized modeling, analysis, and prediction of EEG signals across brain studies such as the study and prediction of epilepsy, peri-perceptual brain activities, brain-computer interfaces, and others.Samaneh Alsadat SaeediniaMohammad Reza Jahed-MotlaghAbbas TafakhoriNikola KasabovNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Samaneh Alsadat Saeedinia Mohammad Reza Jahed-Motlagh Abbas Tafakhori Nikola Kasabov Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals
description	Abstract This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning algorithm integrated with a spike-time-dependent-plasticity algorithm. The models capture informative personal patterns of interaction between EEG channels, contrary to single EEG signal modeling methods or to spike-based approaches which do not use personal MRI data to pre-structure a model. The proposed models can not only learn and model accurately measured EEG data, but they can also predict signals at 3D model locations that correspond to non-monitored brain areas, e.g. other EEG channels, from where data has not been collected. This is the first study in this respect. As an illustration of the method, personalized MRI-SNN models are created and tested on EEG data from two subjects. The models result in better prediction accuracy and a better understanding of the personalized EEG signals than traditional methods due to the MRI and EEG information integration. The models are interpretable and facilitate a better understanding of related brain processes. This approach can be applied for personalized modeling, analysis, and prediction of EEG signals across brain studies such as the study and prediction of epilepsy, peri-perceptual brain activities, brain-computer interfaces, and others.
format	article
author	Samaneh Alsadat Saeedinia Mohammad Reza Jahed-Motlagh Abbas Tafakhori Nikola Kasabov
author_facet	Samaneh Alsadat Saeedinia Mohammad Reza Jahed-Motlagh Abbas Tafakhori Nikola Kasabov
author_sort	Samaneh Alsadat Saeedinia
title	Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals
title_short	Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals
title_full	Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals
title_fullStr	Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals
title_full_unstemmed	Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals
title_sort	design of mri structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of eeg signals
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a2f0ca33db2a424b9a3bc28f530616c9
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Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals

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