Document Type



This item is available under a Creative Commons License for non-commercial use only


Computer Sciences

Publication Details

A dissertation submitted in partial fulfilment of the requirements of Technological University Dublin for the degree of M.Sc. in Computer Science (Data Analytics)


Modeling non-stationary time series data is a difficult problem area in AI, due to the fact that the statistical properties of the data change as the time series progresses. This complicates the classification of non-stationary time series, which is a method used in the detection of brain diseases from EEGs. Various techniques have been developed in the field of deep learning for tackling this problem, with recurrent neural networks (RNN) approaches utilising Long short-term memory (LSTM) architectures achieving a high degree of success. This study implements a new, spiking neural network-based approach to time series classification for the purpose of detecting three brain diseases from EEG datasets - epilepsy, alcoholism, and schizophrenia. The performance and training time of the spiking neural network classifier is compared to those of both a baseline RNN-LSTM EEG classifier and the current state-of-the art RNN-LSTM EEG classifier architecture from the relevant literature. The SNN EEG classifier model developed in this study outperforms both the baseline and state of-the-art RNN models in terms of accuracy, and is able to detect all three brain diseases with an accuracy of 100%, while requiring a far smaller number of training data samples than recurrent neural network approaches. This represents the best performance present in the literature for the task of EEG classification