Next Article in Journal
A Smart Archive Box for Museum Artifact Monitoring Using Battery-Less Temperature and Humidity Sensing
Next Article in Special Issue
A Clinically Interpretable Computer-Vision Based Method for Quantifying Gait in Parkinson’s Disease
Previous Article in Journal
Study and Analysis of Interference Signals of the LTE System of the GNSS Receiver
Previous Article in Special Issue
Machine Learning Methods for Fear Classification Based on Physiological Features
Article

Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network

1
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
2
Department of Audiology, Faculty of Medical and Health Sciences, School of Population Health, The University of Auckland, Auckland 1023, New Zealand
3
George Moore Chair of Data Analytics, School of Computing, Engineering and Intelligent Systems, Ulster University, Derry/Londonderry BT48 7JL, UK
4
Department of Psychology and Neuroscience, Auckland University of Technology, Auckland 0627, New Zealand
*
Author to whom correspondence should be addressed.
Academic Editor: Tianming Liu
Sensors 2021, 21(14), 4900; https://doi.org/10.3390/s21144900
Received: 21 June 2021 / Revised: 14 July 2021 / Accepted: 14 July 2021 / Published: 19 July 2021
The paper proposes a new method for deep learning and knowledge discovery in a brain-inspired Spiking Neural Networks (SNN) architecture that enhances the model’s explainability while learning from streaming spatiotemporal brain data (STBD) in an incremental and on-line mode of operation. This led to the extraction of spatiotemporal rules from SNN models that explain why a certain decision (output prediction) was made by the model. During the learning process, the SNN created dynamic neural clusters, captured as polygons, which evolved in time and continuously changed their size and shape. The dynamic patterns of the clusters were quantitatively analyzed to identify the important STBD features that correspond to the most activated brain regions. We studied the trend of dynamically created clusters and their spike-driven events that occur together in specific space and time. The research contributes to: (1) enhanced interpretability of SNN learning behavior through dynamic neural clustering; (2) feature selection and enhanced accuracy of classification; (3) spatiotemporal rules to support model explainability; and (4) a better understanding of the dynamics in STBD in terms of feature interaction. The clustering method was applied to a case study of Electroencephalogram (EEG) data, recorded from a healthy control group (n = 21) and opiate use (n = 18) subjects while they were performing a cognitive task. The SNN models of EEG demonstrated different trends of dynamic clusters across the groups. This suggested to select a group of marker EEG features and resulted in an improved accuracy of EEG classification to 92%, when compared with all-feature classification. During learning of EEG data, the areas of neurons in the SNN model that form adjacent clusters (corresponding to neighboring EEG channels) were detected as fuzzy boundaries that explain overlapping activity of brain regions for each group of subjects. View Full-Text
Keywords: interpretable; explainable; dynamic clustering; feature selection; spiking neural networks; spatiotemporal data; EEG data interpretable; explainable; dynamic clustering; feature selection; spiking neural networks; spatiotemporal data; EEG data
Show Figures

Figure 1

MDPI and ACS Style

Doborjeh, M.; Doborjeh, Z.; Kasabov, N.; Barati, M.; Wang, G.Y. Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network. Sensors 2021, 21, 4900. https://doi.org/10.3390/s21144900

AMA Style

Doborjeh M, Doborjeh Z, Kasabov N, Barati M, Wang GY. Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network. Sensors. 2021; 21(14):4900. https://doi.org/10.3390/s21144900

Chicago/Turabian Style

Doborjeh, Maryam, Zohreh Doborjeh, Nikola Kasabov, Molood Barati, and Grace Y. Wang 2021. "Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network" Sensors 21, no. 14: 4900. https://doi.org/10.3390/s21144900

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop