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dc.contributor.authorDoborjeh, MGen_NZ
dc.contributor.authorCapecci, Een_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.date.accessioned2017-03-17T02:46:12Z
dc.date.available2017-03-17T02:46:12Z
dc.date.copyright2014-01-13en_NZ
dc.identifier.citation2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), Orlando, FL, 2014, pp. 73-80. doi: 10.1109/EALS.2014.7009506en_NZ
dc.identifier.isbn9781479944958en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/10382
dc.description.abstractThe proposed feasibility analysis introduces a new methodology for modelling and understanding functional Magnetic Resonance Image (fMRI) data recorded during human cognitive activity. This constitutes a type of Spatio-Temporal Brain Data (STBD) measured according to neurons spatial location inside the brain and their signals oscillating over the mental activity period [1]; thus, it is challenging to analyse and model dynamically. This paper addresses the problem by means of a novel Spiking Neural Networks (SNN) architecture, called NeuCube [2]. After the NeuCube is trained with the fMRI samples, the 'hidden' spatio-temporal relationship between data is learnt. Different cognitive states of the brain are activated while a subject is reading different sentences in terms of their polarity (affirmative and negative sentences). These are visualised via the SNN cube (SNNc) and then recognized through its classifier. The excellent classification accuracy of 90% proves the NeuCube potential in capturing the fMRI data information and classifying it correctly. The significant improvement in accuracy is demonstrated as compared with some already published results [3] on the same data sets and traditional machine learning methods. Future works is based on the proposed NeuCube model are also discussed in this paper.en_NZ
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_NZ
dc.relation.urihttp://ieeexplore.ieee.org/document/7009506/
dc.rightsCopyright © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectEvolvings Spiking Neural Networksen_NZ
dc.subjectfMRIen_NZ
dc.subjectNeuCubeen_NZ
dc.subjectSpatio-Temporal Brain Dataen_NZ
dc.titleClassification and Segmentation of fMRI Spatio-temporal Brain Data With a Neucube Evolving Spiking Neural Network Modelen_NZ
dc.typeConference Contribution
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1109/EALS.2014.7009506en_NZ
aut.relation.endpage80
aut.relation.startpage73
pubs.elements-id195591


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