EEG Signal Classification using LDA and MLP Classifier

Abstract

Electroencephalography (EEG) is the recording of electrical activities along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain. Diagnostic applications generally focus on the spectral content of EEG, which is the type of neural oscillations that can be observed in EEG signal. EEG is most often used to diagnose epilepsy, which causes obvious abnormalities in EEG readings. This powerful property confirms the rich potential for EEG analysis and motivates the need for advanced signal processing techniques to aid clinicians in their interpretations. This paper describes the application of Wavelet Transform (WT) for the processing of Electroencephalogram (EEG) signals. Furthermore, the linear discriminant analysis (LDA) is applied for feature selection and dimensionality reduction where the informative and discriminative two-dimension features are used as a benchmark for classification purposes through a Multi-Layers Perceptron (MLP) neural network. For five classification problems, the proposed model achieves a high sensitivity, specificity and accuracy of 100%.Finally, the comparison of the results obtained with the proposed methods and those obtained with previous literature methods shows the superiority of our approach for EEG signals classification and automated diagnosis.