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
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 |
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ERA Journal ID | 5043 |
Article Category | Article |
Authors | Doborjeh, Maryam Gholami (Author), Wang, Grace Y. (Author), Kasabov, Nikola K. (Author), Kydd, Robert (Author) and Russell, Bruce (Author) |
Journal Title | IEEE Transactions on Biomedical Engineering |
Journal Citation | 63 (9), pp. 1830-1841 |
Number of Pages | 12 |
Year | 2016 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 0018-9294 |
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. |
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) |
ANZSRC Field of Research 2020 | 520203. Cognitive neuroscience |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Auckland University of Technology, New Zealand |
University of Auckland, New Zealand | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q750w/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
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