Nonlinear Granger causality and its application in the analysis of epileptic EEG and ECG signal

In this paper, a method based on the nolinear Granger causality is used to analyze epilptic EEG and ECG signal. Polynomial kernel function, Gaussian kernel function and sigmoid kernel function are used to map the linear data in low dimensional input space into high dimensional feature space .In this space linear Granger method can be used to analyse the biomedical signals. The results show that the effect of ECG signals to EEG signals is more significant than that of EEG signals to ECG signals and the result by normal subjects is more significant than that of epileptic subjects. This study is helpful for the analysis of epileptic patient's EEG and ECG signal..