A paper accepted to ITS2014 (EEG workshop)!

12th International Conference on Intelligent Tutoring Systems
Hosted by the University of Hawaii at Manoa
Honolulu, Hawaii, US. Jun 2014

Abstract

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.

Reference

Lujan-Moreno, G, Atkinson R., Runger G., Gonzalez-Sanchez J. and Chavez-Echeagaray M.E. (2014). Classification of video game players using EEG and logistic regression with ridge estimator. In Workshops Proceedings of the 12th International Conference on Intelligent Tutoring Systems (ITS). Honolulu, HI, US. 5-9 June. Springer. Pp. 21–26.