Analyzing the resting-state EEG of action video game players using Wavelet Transform

Action video game is a video game genre that emphasizes on enhancing the mental ability of information processing. The present study considered the neural activation pattern of action video game players under the resting condition for assessment. EEG data are collected from 16-male action video-game players (AVGPs) and 19-male non-video-game players (NVGPs) using 19-electrodes during eyes open (EO) resting states. Spontaneous fluctuations in brain activity of participants are assessed by two consciousness index (CI) features, beta/theta and beta/alpha, computed using wavelet energies of theta, alpha, and beta frequency bands. Results show a higher level of resting state consciousness in AVGPs compared to NVGPs. When performances are evaluated using linear and nonlinear Support Vector Machines (SVMs), 93.97% classification accuracy is seen with beta/theta ratio whereas beta/alpha ratio produced 91.47% accuracy. The results of the analysis demonstrate that the effect of long-term gaming practice can be effectively analyzed by the wavelet transform based feature extraction technique described here.