Recognition and removal of EMG artifacts in single-channel EEG signals based on variational mode decomposition and second-order blind identification

Abstract

In the process of EEG signal acquisition, it is inevitable to be affected by some artifacts, for the traditional multi-channel EEG signal, researchers have proposed a variety of methods to remove artifacts. However, with the development of wearable EEG acquisition systems, only single-channel EEG signals are collected in many cases. In order to solve the problem that the artifact of single-channel EEG signal is not easy to deal with, this paper proposes an artifact identification and removal method that combines variational mode decomposition (VMD) and second-order blind identification (SOBI). First, the original signal is decomposed into a series of intrinsic modal function (IMF) by the optimized variational modal decomposition. The idea of blind source separation is introduced, and the IMF is used as the input signal of SOBI. Then, the fuzzy entropy and correlation coefficient comprehensive indicators are used to identify extract the components containing artifacts and remove them. Finally, the pure EEG signal is reconstructed by inverse transformation. The semi-simulation experiments show that the proposed method has a better effect of removing EMG artifacts than other existing methods, and can retain useful information to the greatest extent.