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A Feasibility Study of Using the NeuCube Spiking Neural Network Architecture for Modelling Alzheimer's Disease EEG Data.

, , , , , and . Advances in Neural Networks, volume 37 of Smart Innovation, Systems and Technologies, Springer, (2015)

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Hierarchical clustering of the electroencephalogram spectral coherence to study the changes in brain connectivity in Alzheimer's disease., , , , , , , , and . CEC, page 1241-1248. IEEE, (2016)Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings., , , , , , , , and . RTSI, page 1-6. IEEE, (2016)Environmental Time Series Prediction by Improved Classical Feed-Forward Neural Networks., , and . WIRN/NAIS, volume 3931 of Lecture Notes in Computer Science, page 171-178. Springer, (2005)A Kernel Based Learning by Sample Technique for Defect Identification Through the Inversion of a Typical Electric Problem., , , , and . KES (3), volume 4694 of Lecture Notes in Computer Science, page 243-250. Springer, (2007)Learning Vector Quantization and Permutation Entropy to analyse epileptic electroencephalography., , , , , and . IJCNN, page 1-6. IEEE, (2015)A Feasibility Study of Using the NeuCube Spiking Neural Network Architecture for Modelling Alzheimer's Disease EEG Data., , , , , and . Advances in Neural Networks, volume 37 of Smart Innovation, Systems and Technologies, Springer, (2015)Modelling Absence Epilepsy seizure data in the NeuCube evolving spiking neural network architecture., , , , , , , , and . IJCNN, page 1-8. IEEE, (2015)Visual Explanations of Deep Convolutional Neural Network for eye blinks detection in EEG-based BCI applications., , , , , , and . IJCNN, page 1-8. IEEE, (2022)