EEG Verileri Kullanılarak Fiziksel El Hareketleri ve Bu Hareketlerin Hayalinin Yapay Sinir Ağları İle Sınıflandırılması

In recent years, as a result of the technological development, there has been a significance improvement on the computer interface. Electroencephalogram (EEG) signals are widely used in Brain Computer Interface (BCI) methods. By using EEG data, the imagination of movement with physical motion can be classified. In this study, EEG data of a 21-years-old man who used his right hand and who didn't show any disease symptom was used. Part of this EEG data demonstrates the recordings of forward and backward movement of the left and right hand. The other data indicates the records of imagination of motion without any physical movement. Using the Welch method, the power densities of the frequencies of 1-48 Hz of the EEG data were calculated. The obtained data sets were applied to the designed Back Propagation Neural Network (BPNN). At the end of the network training, the Mean Squared Error (MSE) value of 4.6731x10-23 has been reached. When the test data set, which consists of imaginary and motion data is applied to the trained network, imagination and motion data are classified with accuracy of 99.9975%.