Deep Learning of EEG Data in the NeuCube Brain-Inspired Spiking Neural Network Architecture for a Better Understanding of Depression
Paper
Paper/Presentation Title | Deep Learning of EEG Data in the NeuCube Brain-Inspired Spiking Neural Network Architecture for a Better Understanding of Depression |
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Presentation Type | Paper |
Authors | Shah, Dhvani (Author), Wang, Grace Y. (Author), Doborjeh, Maryam (Author), Doborjeh, Zohreh (Author) and Kasabov, Nikola (Author) |
Editors | Gedeon, Tom, Wong, Kok Wai and Lee, Minho |
Journal or Proceedings Title | Lecture Notes in Computer Science (Book series) |
ERA Conference ID | 43474 |
Journal Citation | 11955, pp. 195-206 |
Number of Pages | 12 |
Year | 2019 |
Publisher | Springer |
Place of Publication | Switzerland |
ISSN | 1611-3349 |
0302-9743 | |
ISBN | 9783030367176 |
9783030367183 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-36718-3_17 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-030-36718-3_17 |
Conference/Event | 26th 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 A A A A A A A A A A A A A A A |
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. |
Keywords | Depression; Electroencephalogram (EEG); NeuCube; Spiking Neural Networks (SNN) |
ANZSRC Field of Research 2020 | 520206. Psychophysiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Auckland University of Technology, New Zealand |
Institution of Origin | University of Southern Queensland |
Book Title | Neural Information Processing26th International Conference, ICONIP 2019Sydney, NSW, Australia, December 12–15, 2019Proceedings, Part III |
https://research.usq.edu.au/item/q74zx/deep-learning-of-eeg-data-in-the-neucube-brain-inspired-spiking-neural-network-architecture-for-a-better-understanding-of-depression
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