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 language | English |
|---|---|
| Pages (from-to) | 159-172 |
| Number of pages | 14 |
| Journal | Smart Innovation, Systems and Technologies |
| Volume | 37 |
| DOIs | |
| Publication status | Published - 2015 |
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Keywords
- Alzheimer’s disease
- EEG data classification
- NeuCube
- Spiking neural networks
ASJC Scopus subject areas
- Computer Science(all)
- Decision Sciences(all)
Cite this
A feasibility study of using the neucube spiking neural network architecture for modelling Alzheimer’s disease EEG data. / Capecci, Elisa; Morabito, Francesco Carlo; Campolo, Maurizio; Mammone, Nadia; Labate, Domenico; Kasabov, Nikola.
In: Smart Innovation, Systems and Technologies, Vol. 37, 2015, p. 159-172.Research output: Contribution to journal › Article
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TY - JOUR
T1 - A feasibility study of using the neucube spiking neural network architecture for modelling Alzheimer’s disease EEG data
AU - Capecci, Elisa
AU - Morabito, Francesco Carlo
AU - Campolo, Maurizio
AU - Mammone, Nadia
AU - Labate, Domenico
AU - Kasabov, Nikola
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Alzheimer’s disease
KW - EEG data classification
KW - NeuCube
KW - Spiking neural networks
UR - http://www.scopus.com/inward/record.url?scp=84930933542&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84930933542&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-18164-6_16
DO - 10.1007/978-3-319-18164-6_16
M3 - Article
AN - SCOPUS:84930933542
VL - 37
SP - 159
EP - 172
JO - 6th International Conference on Research into Design, ICoRD 2017
JF - 6th International Conference on Research into Design, ICoRD 2017
SN - 2190-3018
ER -