A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data From Healthy Versus Addiction Treated Versus Addiction Not Treated Subjects

Article

Doborjeh, Maryam Gholami, Wang, Grace Y., Kasabov, Nikola K., Kydd, Robert and Russell, Bruce. 2016. "A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data From Healthy Versus Addiction Treated Versus Addiction Not Treated Subjects." IEEE Transactions on Biomedical Engineering. 63 (9), pp. 1830-1841. https://doi.org/10.1109/TBME.2015.2503400
Article Title

A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data From Healthy Versus Addiction Treated Versus Addiction Not Treated Subjects

ERA Journal ID5043
Article CategoryArticle
AuthorsDoborjeh, Maryam Gholami (Author), Wang, Grace Y. (Author), Kasabov, Nikola K. (Author), Kydd, Robert (Author) and Russell, Bruce (Author)
Journal TitleIEEE Transactions on Biomedical Engineering
Journal Citation63 (9), pp. 1830-1841
Number of Pages12
Year2016
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN0018-9294
1558-2531
Digital Object Identifier (DOI)https://doi.org/10.1109/TBME.2015.2503400
Web Address (URL)https://ieeexplore.ieee.org/document/7336519
Abstract

This paper introduces a method utilizing spiking neural networks (SNN) for learning, classification, and comparative analysis of brain data. As a case study, the method was applied to electroencephalography (EEG) data collected during a GO/NOGO cognitive task performed by untreated opiate addicts, those undergoing methadone maintenance treatment (MMT) for opiate dependence and a healthy control group. Methods: The method is based on an SNN architecture called NeuCube, trained on spatiotemporal EEG data. Objective: NeuCube was used to classify EEG data across subject groups and across GO versus NOGO trials, but also facilitated a deeper comparative analysis of the dynamic brain processes. Results: This analysis results in a better understanding of human brain functioning across subject groups when performing a cognitive task. In terms of the EEG data classification, a NeuCube model obtained better results (the maximum obtained accuracy: 90.91%) when compared with traditional statistical and artificial intelligence methods (the maximum obtained accuracy: 50.55%). Significance: more importantly, new information about the effects of MMT on cognitive brain functions is revealed through the analysis of the SNN model connectivity and its dynamics. Conclusion: This paper presented a new method for EEG data modeling and revealed new knowledge on brain functions associated with mental activity which is different from the brain activity observed in a resting state of the same subjects.

KeywordsEEG data; EEG data classification; Electroencephalography (EEG) comparative analysis; evolving spiking neural networks (eSNNs); GO/NOGO tasks; methadone maintenance treatment (MMT); NeuCube; opiate addicts; spatiotemporal brain data (STBD)
ANZSRC Field of Research 2020520203. Cognitive neuroscience
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Byline AffiliationsAuckland University of Technology, New Zealand
University of Auckland, New Zealand
Institution of OriginUniversity of Southern Queensland
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