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Classification Electroencephalography Using Machine Learning

This research examines the feasibility of using the Bagged Trees (BT) and Support Vector Machine (SVM) algorithms for recognition of Electroencephalography (EEG) patterns. We focus on the accuracies of different pattern recognition methods as well as sensitivities of eleven mental states including states related to eyes behavior and thinking signals. We have been introduced and learned relate to four main algorithms: Infinite Impulse Response (IIR) digital filter, Discrete Wavelet Transform, Bagged Trees and Support Vector Machine. By applying IIR and Wavelet to calculate band energies; Bagged Trees and Support Vector Machine to classify states of EEG. Then, we make final conclusions.