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

Maryam Doborjeh, Grace Wang, Nikola Kasabov, Robert Kydd, Bruce Russel

Research output: Contribution to journalArticle

26 Citations (Scopus)

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 modelling 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.
Original languageEnglish
Pages (from-to)1830-1841
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Volume63
Issue number9
DOIs
Publication statusPublished - 1 Sep 2016

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