Comparison of different methods to suppress muscle artifacts in EEG signals

Abstract

Independent component analysis (ICA) is an approved method for (e.g., muscle) artifact removal in electroencephalography (EEG). But, as it creates only \(m \le n\) components from n signals, it may fail to clearly separate the artifacts. In order to keep the strengths of ICA and overcome its limitations, we extend ICA by state-space modeling (SSM), thereby enabling \(m > n\). Rather than exploring an optimized choice of the ICA algorithm, the effect of this extension is analyzed. Four methods, low-pass filtering (LPF), ICA, ICA–LPF, and ICA–SSM, are applied, first, to a clean epilepsy EEG segment artificially contaminated by muscle artifacts (MA), thereafter to 7 epilepsy patients’ data. Both by visual assessment by an experienced clinician, and by quantitative measures, ICA–SSM is proven to remove MA better and with less signal distortion than ICA–LPF and much better than pure LPF or ICA.