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

Maryam Doborjeh, Zohreh Doborjeh, Nikola Kasabov, Molood Barati, Grace Wang

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
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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 spatio-temporal brain data (STBD) in an incremental and on-line mode of operation. This led to the extraction of spatio-temporal 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) spatio-temporal 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.
Original languageEnglish
Article number4900
Pages (from-to)1-21
Number of pages21
JournalSensors (Switzerland)
Issue number14
Early online date19 Jul 2021
Publication statusPublished online - 19 Jul 2021

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • dynamic clustering
  • EEG data
  • spiking neural networks
  • spatio-temporal data
  • explainability
  • interperatability
  • Feature selection
  • Spatiotemporal data
  • Explainable
  • Spiking neural networks
  • Dynamic clustering
  • Interpretable


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