Classification of EEG signals caused by imagining hand movements

In this study; Electroencephalogram signals which have caused in the brain of imagining right and left hand movements of human are classified with k-Nearest Neighbor Algorithm, Multi Layer Perceptron and Support Vector Machines algorithms. By acquiring different features (mean value, standard deviation, variance, maximum, minimum, absolute value and Fourier transform) from this electroencephalogram data, the best representing features of this data have been investigated. When the results of the used features and classification algorithms are examined, the features obtained from Fourier Transform and k-Nearest Neighbor Algorithm have shown the most successful results in classifying electroencephalogram data.