An EEMD-IVA Approach to Removing Muscle Artifacts from the Electroencephalogram

Abstract

Electroencephalogram (EEG) is often contaminated by massive muscle artifacts. In this article, a new approach to removing muscle artifacts is proposed, which is based on ensemble empirical mode decomposition (EEMD) and independent vector analysis (IVA). Each channel of EEG is decomposed into intrinsic mode functions (IMFs) with EEMD to achieve an extended data set that contains more channels than the original data set. The potential artifact components are decomposed by IVA for further isolation. Quantitative results are obtained on semi-simulated and real-life EEG data sets. These results show that the proposed EEMD-IVA approach outperforms independent component analysis (ICA), IVA, and EEMD-ICA.