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

Maryam Doborjeh, Nikola Kasabov, Zohreh Doborjeh

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
177 Downloads (Pure)


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 cap- tured 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 spa- tial involvement of brain regions in response to a cognitive task.
Original languageEnglish
Article number9178
Pages (from-to)195-211
Number of pages17
JournalEvolving Systems
Publication statusPublished (in print/issue) - 17 Apr 2017


  • Dynamic spatiotemporal streaming data clustering
  • EEG data
  • NeuCube
  • Spiking neural networks
  • Unsupervised Learning
  • supervised learning
  • Personalaised clustering


Dive into the research topics of 'Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data'. Together they form a unique fingerprint.

Cite this