- Elisa Capecci6,
- Francesco Carlo Morabito7,
- Maurizio Campolo7,
- Nadia Mammone7,
- Domenico Labate7 &
- …
- Nikola Kasabov6
Part of the book series:Smart Innovation, Systems and Technologies ((SIST,volume 37))
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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 NeuCube 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.
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Authors and Affiliations
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand
Elisa Capecci & Nikola Kasabov
DICEAM - Mediterranea University of Reggio Calabria, Reggio Calabria, Italy
Francesco Carlo Morabito, Maurizio Campolo, Nadia Mammone & Domenico Labate
- Elisa Capecci
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- Francesco Carlo Morabito
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- Maurizio Campolo
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- Nadia Mammone
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- Domenico Labate
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Correspondence toElisa Capecci.
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Editors and Affiliations
Computer Science Department, University of Milano, Milano, Italy
Simone Bassis
Dipartimento di Psicologia & Vietri sul Mare (SA), Seconda Universitá di Napoli, International Institute for Advanced Scientific Studies (IIASS), Caserta, Italy
Anna Esposito
Department of Civil, Environmental, Energy, and Material Engineering, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy
Francesco Carlo Morabito
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Capecci, E., Morabito, F.C., Campolo, M., Mammone, N., Labate, D., Kasabov, N. (2015). A Feasibility Study of Using the NeuCube Spiking Neural Network Architecture for Modelling Alzheimer’s Disease EEG Data. In: Bassis, S., Esposito, A., Morabito, F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-18164-6_16
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