Random matrix analysis of human EEG data.

We use random matrix theory to demonstrate the existence of generic and subject-independent features of the ensemble of correlation matrices extracted from human EEG data. In particular, the spectral density as well as the level spacings was analyzed and shown to be generic and subject independent. We also investigate number variance distributions. In this case we show that when the measured subject is visually stimulated the number variance displays deviations from the random matrix prediction.