Detection and Removal of Muscle Artifacts from Scalp EEG Recordings in Patients with Epilepsy

Abstract

The Electroencephalogram (EEG) is often contaminated by muscle artifacts. EEG is a widely used recording technique for the study of many brain related diseases such as epilepsy. The detection and removal of muscle artifacts from the EEG signal poses a real challenge and is crucial for the reliable interpretation of EEG-based quantitative measures. In this paper, an automatic method for detection and removal of muscle artifacts from scalp EEG recordings, based on canonical correlation analysis (CCA), is introduced. To this end we exploit the fact that the EEG signal may exhibit altered autocorrelation structure and spectral characteristics during periods when it is contaminated by muscle activity. Therefore, we design classifiers in order to automatically discriminate between contaminated and non-contaminated EEG epochs using features based on the aforementioned quantities and examine their performance on simulated data and in scalp EEG recordings obtained from patients with epilepsy.