Nonlinear prediction and complexity of alpha EEG activity

Two prediction techniques were used to investigate the dynamical complexity of the alpha EEG; a nonlinear method using the K-nearest-neighbor local li near (KNNLL) approximation, and one based on global linear autoregressive ( AR) modeling. Generally, KNNLL has more ability to predict nonlinearity in a chaotic time series under moderately noisy conditions as demonstrated by using increasingly noisy realizations of the Henon (a low-dimensional chaot ic) and Mackey-Glass (a high-dimensional chaotic) maps. However, at higher noise levels KNNLL performs no better than AR prediction. For linear stocha stic time series, such as a sine wave with added Gaussian noise, prediction using KNNLL is no better than AR even at very low signal-to-noise ratios. Both prediction techniques were applied to resting EEGs (O2 scalp recording site, 10-20 EEG system) from ten normal adult subjects under eyes-closed a nd eyes-open conditions. In all recordings tested, KNNLL did not yield a lo wer root mean squared error (RMSE) than AR prediction. This result more clo sely resembles that obtained for noisy sine waves as opposed to chaotic tim e series with added noise. This lends further support to the notion that th ese EEG signals are linear-stochastic in nature. However, the possibility t hat some EEG signals, particularly those with high prediction errors produc ed by a noisy nonlinear system cannot be ruled out in this study.