A feasibility study of using the neucube spiking neural network architecture for modelling Alzheimer’s disease EEG data

Elisa Capecci, Francesco Carlo Morabito, Maurizio Campolo, Nadia Mammone, Domenico Labate, Nikola Kasabov

Research output: Contribution to journalArticlepeer-review

Abstract

The paper presents a feasibility analysis of a novel Spiking Neural Network (SNN) architecture called NeuCube [10] for classification and analysis of functional changes in brain activity of Electroencephalography (EEG) data collected amongst two groups: control and Alzheimer’s Disease (AD). Excellent classification results of 100% test accuracy have been achieved and these have also been compared with traditional machine learning techniques. Outputs confirmed that the Neu-Cube is better suited to model, classify, interpret and understand EEG data and the brain processes involved. Future applications of a NeuCube model are discussed including its use as an indicator of the early onset of Mild Cognitive Impairment(MCI) to study degeneration of the pathology toward AD.

Original languageEnglish
Pages (from-to)159-172
Number of pages14
JournalSmart Innovation, Systems and Technologies
Volume37
DOIs
Publication statusPublished - 2015

Keywords

  • Alzheimer’s disease
  • EEG data classification
  • NeuCube
  • Spiking neural networks

ASJC Scopus subject areas

  • Computer Science(all)
  • Decision Sciences(all)

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