Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network

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aut.relation.endpage4900
aut.relation.issue14en_NZ
aut.relation.journalSensorsen_NZ
aut.relation.startpage4900
aut.relation.volume21en_NZ
aut.researcherWang, Ying
dc.contributor.authorDoborjeh, Men_NZ
dc.contributor.authorDoborjeh, Zen_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.contributor.authorBarati, Men_NZ
dc.contributor.authorWang, GYen_NZ
dc.date.accessioned2021-07-26T04:02:31Z
dc.date.available2021-07-26T04:02:31Z
dc.description.abstractThe paper proposes a new method for deep learning and knowledge discovery in a brain-inspired Spiking Neural Networks (SNN) architecture that enhances the model’s explainability while learning from streaming spatiotemporal brain data (STBD) in an incremental and on-line mode of operation. This led to the extraction of spatiotemporal rules from SNN models that explain why a certain decision (output prediction) was made by the model. During the learning process, the SNN created dynamic neural clusters, captured as polygons, which evolved in time and continuously changed their size and shape. The dynamic patterns of the clusters were quantitatively analyzed to identify the important STBD features that correspond to the most activated brain regions. We studied the trend of dynamically created clusters and their spike-driven events that occur together in specific space and time. The research contributes to: (1) enhanced interpretability of SNN learning behavior through dynamic neural clustering; (2) feature selection and enhanced accuracy of classification; (3) spatiotemporal rules to support model explainability; and (4) a better understanding of the dynamics in STBD in terms of feature interaction. The clustering method was applied to a case study of Electroencephalogram (EEG) data, recorded from a healthy control group (n = 21) and opiate use (n = 18) subjects while they were performing a cognitive task. The SNN models of EEG demonstrated different trends of dynamic clusters across the groups. This suggested to select a group of marker EEG features and resulted in an improved accuracy of EEG classification to 92%, when compared with all-feature classification. During learning of EEG data, the areas of neurons in the SNN model that form adjacent clusters (corresponding to neighboring EEG channels) were detected as fuzzy boundaries that explain overlapping activity of brain regions for each group of subjects.en_NZ
dc.identifier.citationSensors, 21(14), 4900. doi:10.3390/s21144900
dc.identifier.doi10.3390/s21144900en_NZ
dc.identifier.issn1424-8220en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14378
dc.languageenen_NZ
dc.publisherMDPI AGen_NZ
dc.relation.urihttps://www.mdpi.com/1424-8220/21/14/4900
dc.rights© 2021 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.accessrightsOpenAccessen_NZ
dc.subjectInterpretable; Explainable; Dynamic clustering; Feature selection; Spiking neural networks; Spatiotemporal data; EEG data
dc.titleDeep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Networken_NZ
dc.typeJournal Article
pubs.elements-id435320
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Health & Environmental Science
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Clinical Sciences
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Clinical Sciences/Psychology & Neuroscience Department
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences/HY Public Health & Psychosocial Studies 2018 PBRF
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