EEG Classification based on Machine Learning Techniques

Abstract

The main issue to build applicable Brain-Computer Interfaces is the capability to classify the electroencephalograms (EEG). During the last decade, researchers developed lots of interests in this field. The purpose behind this research is to improve a model for EEG signals analysis. Filtration of EEG Signals is essential to remove artifacts. Otherwise, wavelet transform was used to extract features. Mean, Maximum, Minimum and Standard Deviations values of wavelet coefficients for the EEG signals were chosen as a feature vector. This paper compares the classification results by the use of Neural Network, KNearest Neighbor and Support Vector Machine classifiers. It has been illustrated from results that the K-Nearest Neighbor classifier outperforms a better performance than Neural Network and Support Vector Machine.