An Optimal Smooth Model based EEG Analysis Method

Aiming at the problem of unsatisfactory noise removal effect during the preprocessing of MI EEG signals, an optimal smooth model based on empirical mode decomposition is introduced. First, use empirical mode decomposition (EMD) to decompose the complex signal into a certain number of intrinsic mode functions (IMFs) and a remainder function (r), and then construct a smooth model function based on the mean square error and smoothing index, using different The function of the intrinsic mode is combined with the optimal smooth model function to reduce noise and obtain the optimal solution. The research results show that compared with several denoising methods commonly used in other literature, this method has improved SNR, RMSE, and PE three evaluation indicators. The proposed method can provide a reference for the preprocessing of motor imagery signals.