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dc.contributor.authorSaeedinia, SAen_NZ
dc.contributor.authorJahed-Motlagh, MRen_NZ
dc.contributor.authorTafakhori, Aen_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.date.accessioned2022-02-04T02:25:07Z
dc.date.available2022-02-04T02:25:07Z
dc.date.copyright2021en_NZ
dc.identifier.citationScientific Reports 11, 12064 (2021). https://doi.org/10.1038/s41598-021-90029-5
dc.identifier.issn2045-2322en_NZ
dc.identifier.issn2045-2322en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/14876
dc.description.abstractThis 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.en_NZ
dc.languageengen_NZ
dc.publisherNature Publishing Groupen_NZ
dc.relation.urihttps://www.nature.com/articles/s41598-021-90029-5
dc.rights© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.titleDesign of MRI Structured Spiking Neural Networks and Learning Algorithms for Personalized Modelling, Analysis, and Prediction of EEG Signalsen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1038/s41598-021-90029-5en_NZ
aut.relation.issue1en_NZ
aut.relation.volume11en_NZ
pubs.elements-id431579
aut.relation.journalScientific Reportsen_NZ


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