Deep Learning of EEG Data in the NeuCube Brain-Inspired Spiking Neural Network Architecture for a Better Understanding of Depression

Paper

Shah, Dhvani, Wang, Grace Y., Doborjeh, Maryam, Doborjeh, Zohreh and Kasabov, Nikola. 2019. "Deep Learning of EEG Data in the NeuCube Brain-Inspired Spiking Neural Network Architecture for a Better Understanding of Depression." Gedeon, Tom, Wong, Kok Wai and Lee, Minho (ed.) 26th International Conference on Neural Information Processing, Part III (ICONIP 2019). Sydney, Australia 12 - 15 Dec 2019 Switzerland. Springer. https://doi.org/10.1007/978-3-030-36718-3_17
Paper/Presentation Title

Deep Learning of EEG Data in the NeuCube Brain-Inspired Spiking Neural Network Architecture for a Better Understanding of Depression

Presentation TypePaper
AuthorsShah, Dhvani (Author), Wang, Grace Y. (Author), Doborjeh, Maryam (Author), Doborjeh, Zohreh (Author) and Kasabov, Nikola (Author)
EditorsGedeon, Tom, Wong, Kok Wai and Lee, Minho
Journal or Proceedings TitleLecture Notes in Computer Science (Book series)
ERA Conference ID43474
Journal Citation11955, pp. 195-206
Number of Pages12
Year2019
PublisherSpringer
Place of PublicationSwitzerland
ISSN1611-3349
0302-9743
ISBN9783030367176
9783030367183
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-36718-3_17
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/978-3-030-36718-3_17
Conference/Event26th International Conference on Neural Information Processing, Part III (ICONIP 2019)
International Conference on Neural Information Processing
Event Details
International Conference on Neural Information Processing
ICONIP
Neural Information Processing
Rank
A
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A
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Event Details
26th International Conference on Neural Information Processing, Part III (ICONIP 2019)
Event Date
12 to end of 15 Dec 2019
Event Location
Sydney, Australia
Abstract

In the recent years, machine learning and deep learning techniques are being applied on brain data to study mental health. The activation of neurons in these models is static and continuous-valued. However, a biological neuron processes the information in the form of discrete spikes based on the spike time and the firing rate. Understanding brain activities is vital to understand the mechanisms underlying mental health. Spiking Neural Networks are offering a computational modelling solution to understand complex dynamic brain processes related to mental disorders, including depression. The objective of this research is modeling and visualizing brain activity of people experiencing symptoms of depression using the SNN NeuCube architecture. Resting EEG data was collected from 22 participants and further divided into groups as healthy and mild-depressed. NeuCube models have been developed along with the connections across different brain regions using Synaptic Time Dependent plasticity (STDP) learning rule for healthy and depressed individuals. This unsupervised learning revealed some distinguishable patterns in the models related to the frontal, central and parietal areas of the depressed versus the control subjects that suggests potential markers for early depression prediction. Traditional machine learning techniques, including MLP methods have been also employed for classification and prediction tasks on the same data, but with lower accuracy and fewer new information gained.

KeywordsDepression; Electroencephalogram (EEG); NeuCube; Spiking Neural Networks (SNN)
ANZSRC Field of Research 2020520206. Psychophysiology
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Byline AffiliationsAuckland University of Technology, New Zealand
Institution of OriginUniversity of Southern Queensland
Book TitleNeural Information Processing26th International Conference, ICONIP 2019Sydney, NSW, Australia, December 12–15, 2019Proceedings, Part III
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