Classification of video game players using EEG and logistic regression with ridge estimator

The objective is to classify a group of subjects playing a video game as experts and novices using electroencephalogram (EEG) signals as inputs. Analytical methods applied to multi-channel EEG recording are described. A fast Fourier transform (FFT) is used to calculate the power spectral density for a number of bandwidths (delta, theta, alpha and beta) and ratios (e.g., theta/beta). A regularized logistic regression learning algorithm (L2 penalty) was applied to the extracted features. We successfully classified 80% of the instances using a 10 fold cross-validation.