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.

A feasibility study of using the neucube spiking neural network architecture for modelling Alzheimer’s disease EEG data / Capecci, E., Morabito, F.C., Campolo, M., Mammone, N., Labate, D., Kasabov, N.. - 37:(2015), pp. 159-172. [10.1007/978-3-319-18164-6_16]

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

Morabito F. C.;Campolo M.;Mammone N.
Membro del Collaboration Group
;
2015-01-01

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.
2015
Inglese
37
Smart Innovation, Systems and Technologies
159
172
14
978-3-319-18163-9
978-3-319-18164-6
https://link.springer.com/chapter/10.1007/978-3-319-18164-6_16
Springer Science and Business Media Deutschland GmbH
Esperti anonimi
Alzheimer’s disease
EEG data classification
NeuCube
Spiking neural networks
Internazionale
info:eu-repo/semantics/bookPart
Capecci, E.; Morabito, F. C.; Campolo, M.; Mammone, N.; Labate, D.; Kasabov, N.
2 Contributo in Volume::2.1 Contributo in volume (Capitolo o Saggio)
6
268
A feasibility study of using the neucube spiking neural network architecture for modelling Alzheimer’s disease EEG data / Capecci, E., Morabito, F.C., Campolo, M., Mammone, N., Labate, D., Kasabov, N.. - 37:(2015), pp. 159-172. [10.1007/978-3-319-18164-6_16]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/137410
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