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.

Journal: IEEE transactions on bio-medical engineering

PMID: 26625401

Abstract

UNLABELLED: 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.

Authors

Maryam Gholami Doborjeh

Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand.
Grace Y Wang

Department of Psychology, Faculty of Health and Environmental Science, Auckland University of Technology, Auckland 1142, New Zealand.
Nikola K Kasabov
Robert Kydd
Bruce Russell

Keywords

Adult Brain Cognition Diagnosis, Computer-Assisted Electroencephalography Female Humans Machine Learning Male Methadone Neural Networks, Computer Opioid-Related Disorders Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (26625401)

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.

Abstract

Authors

Keywords

External Resources

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