Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI

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aut.relation.articlenumber1341
aut.relation.endpage1341
aut.relation.issue12
aut.relation.journalBioengineering
aut.relation.startpage1341
aut.relation.volume10
dc.contributor.authorKasabov, Nikola K
dc.contributor.authorBahrami, Helena
dc.contributor.authorDoborjeh, Maryam
dc.contributor.authorWang, Alan
dc.date.accessioned2023-12-05T01:42:38Z
dc.date.available2023-12-05T01:42:38Z
dc.date.issued2023-11-21
dc.description.abstractHumans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only part of the information, either as a limited number of variables, limited time to make the decision, or both. The brain functions as a spatio-temporal associative memory. Inspired by the ability of the human brain, a brain-inspired spatio-temporal associative memory was proposed earlier that utilized the NeuCube brain-inspired spiking neural network framework. Here we applied the STAM framework to develop STAM for neuroimaging data, on the cases of EEG and fMRI, resulting in STAM-EEG and STAM-fMRI. This paper showed that once a NeuCube STAM classification model was trained on a complete spatio-temporal EEG or fMRI data, it could be recalled using only part of the time series, or/and only part of the used variables. We evaluated both temporal and spatial association and generalization accuracy accordingly. This was a pilot study that opens the field for the development of classification systems on other neuroimaging data, such as longitudinal MRI data, trained on complete data but recalled on partial data. Future research includes STAM that will work on data, collected across different settings, in different labs and clinics, that may vary in terms of the variables and time of data collection, along with other parameters. The proposed STAM will be further investigated for early diagnosis and prognosis of brain conditions and for diagnostic/prognostic marker discovery.
dc.identifier.citationBioengineering, ISSN: 2306-5354 (Print); 2306-5354 (Online), MDPI AG, 10(12), 1341-1341. doi: 10.3390/bioengineering10121341
dc.identifier.doi10.3390/bioengineering10121341
dc.identifier.issn2306-5354
dc.identifier.issn2306-5354
dc.identifier.urihttp://hdl.handle.net/10292/17023
dc.languageen
dc.publisherMDPI AG
dc.relation.urihttps://www.mdpi.com/2306-5354/10/12/1341
dc.rights© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject40 Engineering
dc.subject4003 Biomedical Engineering
dc.subjectBrain Disorders
dc.subjectBasic Behavioral and Social Science
dc.subjectClinical Research
dc.subjectBehavioral and Social Science
dc.subjectMental Health
dc.subjectNeurosciences
dc.subjectNeurodegenerative
dc.subject1.2 Psychological and socioeconomic processes
dc.subject1 Underpinning research
dc.subject1.1 Normal biological development and functioning
dc.subjectNeurological
dc.subjectMental health
dc.subject4003 Biomedical engineering
dc.titleBrain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI
dc.typeJournal Article
pubs.elements-id531346
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