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Evolving Systems

, Volume 9, Issue 3, pp 195–211 | Cite as

Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data

  • Maryam Gholami Doborjeh
  • Nikola Kasabov
  • Zohreh Gholami Doborjeh
Original Paper

Abstract

Clustering is a fundamental data processing technique. While clustering of static (vector based) data and of fixed window size time series have been well explored, dynamic clustering of spatiotemporal data has been little researched if at all. Especially when patterns of changes (events) in the data across space and time have to be captured and understood. The paper presents novel methods for clustering of spatiotemporal data using the NeuCube spiking neural network (SNN) architecture. Clusters of spatiotemporal data were created and modified on-line in a continuous, incremental way, where spatiotemporal relationships of changes in variables are incrementally learned in a 3D SNN model and the model connectivity and spiking activity are incrementally clustered. Two clustering methods were proposed for SNN, one performed during unsupervised and one—during supervised learning models. Before submitted to the models, the data is encoded as spike trains, a spike representing a change in the variable value (an event). During the unsupervised learning, the cluster centres were predefined by the spatial locations of the input data variables in a 3D SNN model. Then clusters are evolving during the learning, i.e. they are adapted continuously over time reflecting the dynamics of the changes in the data. In the supervised learning, clusters represent the dynamic sequence of neuron spiking activities in a trained SNN model, specific for a particular class of data or for an individual instance. We illustrate the proposed clustering method on a real case study of spatiotemporal EEG data, recorded from three groups of subjects during a cognitive task. The clusters were referred back to the brain data for a better understanding of the data and the processes that generated it. The cluster analysis allowed to discover and understand differences on temporal sequences and spatial involvement of brain regions in response to a cognitive task.

Keywords

Dynamic spatiotemporal streaming data clustering EEG data NeuCube Spiking neural networks Unsupervised learning Supervised learning Personalised clustering 

Notes

Acknowledgements

The research is supported by the Knowledge Engineering and Discovery Research Institute of the Auckland University of Technology (http://www.kedri.aut.ac.nz). M. GD was also supported by a summer research grant from the faculty of Design and Creative Technology of AUT. The authors would like to acknowledge Professor Robert Kydd and Dr. Bruce Russell from the University of Auckland and Dr. Grace Wang from AUT for providing us with the EEG data. We also acknowledge the assistance of Joyce D’Mello, Dr. Enmei Tu, Dr. Elisa Capecci, Lei Zhou, Israel Espinosa Ramos and Akshay Gollahalli. We are indebted to the reviewers for their detailed, precise and constructive comments and suggestions that helped us tremendously. The NeuCube software along data are available free at http://www.kedri.aut.ac.nz/neucube.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Maryam Gholami Doborjeh
    • 1
  • Nikola Kasabov
    • 1
  • Zohreh Gholami Doborjeh
    • 1
  1. 1.Knowledge Engineering and Discovery Research Institute and School of Engineering, Computer and Mathematical SciencesAuckland University of TechnologyAucklandNew Zealand

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