Approximate entropy as an indicator of non-linearity in self paced voluntary finger movement EEG

This study investigates the indications of nonlinear dynamic structures in electroencephalogram signals. The iterative amplitude adjusted surrogate data method along with seven nonlinear test statistics namely the third order autocorrelation, asymmetry due to time reversal, delay vector variance method, correlation dimension, largest Lyapunov exponent, nonlinear prediction error and approximate entropy has been used for analysing the EEG data obtained during self paced voluntary finger-movement. The results have demonstrated that there are clear indications of nonlinearity in the EEG signals. However the rejection of the null hypothesis of nonlinearity rate varied based on different parameter settings demonstrating significance of embedding dimension and time lag parameters for capturing underlying nonlinear dynamics in the signals. Across nonlinear test statistics, the highest degree of nonlinearity was indicated by approximate entropy (APEN) feature regardless of the parameter settings.