Minimising prediction error for optimal nonlinear modelling of EEG signals using genetic algorithm

摘要

Genetic algorithm (GA) is used for jointly estimating the embedding dimension and time lag parameters in order to achieve an optimal reconstruction of time series in state space. The conventional methods (false nearest neighbours and first minimum of the mutual information for estimating the embedding dimension and time lag, respectively) are also included for comparison purposes. The performance of GA and conventional parameters are tested by a one step ahead prediction modelling and estimation of dynamic invariants (i.e. approximate entropy). The results of this study indicated that the parameters selected by GA provide a better reconstruction (i.e. lower root mean square error) of EEG signals used for a Brain-Computer Interface (BCI) application. Additionally, GA based parameters are found to be computationally less intensive since both parameters are jointly optimised. In order to further illustrate the superiority of the embedding parameters estimated by GA, approximate entropy (ApEn) features using embedding parameters estimated by GA and conventional methods were computed. Next these ApEn features were used to classify the EEG signals into two classes (movement and non-movement) for BCI application. These results show that the embedding parameters estimated by GA are more appropriate than those estimated by the conventional methods for nonlinear modelling of EEG signals in state space.