Muscle artifact suppression using Independent-Component Analysis and State-Space Modeling

In this paper, we aim at suppressing the muscle artifacts present in electroencephalographic (EEG) signals with a technique based on a combination of Independent Component Analysis (ICA) and State-Space Modeling (SSM). The novel algorithm uses ICA to provide an initial model for SSM which is further optimized by the maximimum-likelihood approach. This model is fitted to artifact-free data. Then it is applied to data with muscle artifacts. The state space is augmented by extracting additional components from the data prediction errors. The muscle artifacts are well separated in the additional components and, hence, a suppression of them can be performed. The proposed algorithm is demonstrated by application to a clinical epilepsy EEG data set.