Medical Technologies Congress (TIPTEKNO), İzmir, Türkiye, 3 - 05 Ekim 2019, ss.349-352
The aim of this study was to determine the effective frequency subband of electroencephalography (EEG) signals before and during a computer game. In this study, EEG data obtained from 9 volunteers before and during play were separated using wavelet packet transform and power values were calculated and rest and play situations were classified according to these values. K.Nearest Neighbor algorithm and feed-forward artificial neural network were used in the classification stage. In this study, signals were recorded with four channel mobile EEG device. According to the obtained results, it was determined that the most distinctive situation before and after the game was beta band in AF7 electrode region, low gamma and delta bands in TP9 region and delta band in TP10 region.