EEG discrimination with artificial neural networks

Neurodegenerative disorders associated with aging as Alzheimer’s disease (AD) have been increasing significantly in the last decades. AD affects the cerebral cortex and causes specific changes in brain electrical activity. Therefore, the analysis of signals from the electroencephalogram (EEG) may reveal structural and functional deficiencies typically associated with AD. This study aimed to develop an Artificial Neural Network (ANN) to classify EEG signals between cognitively normal control subjects and patients with probable AD . The results showed that the EEG can be a very useful tool to obtain an accurate diagnosis of AD. The best results were performed using the Power Spectral Density (PSD) determined by Short Time Fourier Transform (STFT) with a ANN developed using Levenberg - Marquardt training algorithm, Logarithmic Sigmoid activation function and 9 nodes in the hidden layer (correlation coefficient training: 0.99964, test: 0.95758 and validation: 0.9653 and with a total of: 0 .99245).