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

Doborjeh, Maryam Gholami and Wang, Grace Y. ORCID: https://orcid.org/0000-0003-2063-031X and Kasabov, Nikola K. and 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. ISSN 0018-9294


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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: No Faculty
Faculty/School / Institute/Centre: No Faculty
Date Deposited: 18 May 2022 01:11
Last Modified: 25 May 2022 00:23
Uncontrolled Keywords: EEG 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)
Fields of Research (2020): 52 PSYCHOLOGY > 5202 Biological psychology > 520203 Cognitive neuroscience
Socio-Economic Objectives (2020): 20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions
Identification Number or DOI: https://doi.org/10.1109/TBME.2015.2503400
URI: http://eprints.usq.edu.au/id/eprint/48414

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