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
| Autor: | Bruce R. Russell, Maryam Gholami Doborjeh, Nikola Kasabov, Grace Y. Wang, Robert R. Kydd |
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| Rok vydání: | 2016 |
| Předmět: |
Adult
Male medicine.medical_specialty Brain activity and meditation Biomedical Engineering 02 engineering and technology Audiology Electroencephalography Sensitivity and Specificity Pattern Recognition Automated Data modeling Machine Learning 03 medical and health sciences Cognition 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering medicine Humans Diagnosis Computer-Assisted Spiking neural network Resting state fMRI medicine.diagnostic_test Artificial neural network Brain Reproducibility of Results Human brain Opioid-Related Disorders medicine.anatomical_structure Female 020201 artificial intelligence & image processing Neural Networks Computer Psychology Neuroscience Methadone 030217 neurology & neurosurgery |
| Zdroj: | IEEE Transactions on Biomedical Engineering. 63:1830-1841 |
| ISSN: | 1558-2531 0018-9294 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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