Removing Muscle Artifacts From EEG Data: Multichannel or Single-Channel Techniques?

Review and analysis of continuous EEG recordings may be impeded by physiological artifacts such as blinks, eye movements, or cardiac activity. Spatial filters based on artifact and brain signal topographies can remove artifacts completely without distortion of relevant brain activity. The authors describe the basic principle of artifact correction by spatial filtering and they review different approaches to estimate artifact and brain signal topographies. The main focus is on the preselection approach, which is fast enough to be applied while paging through the segments of a digital EEG recording. Examples of real EEG segments, containing epileptic seizure activity or interictal spikes contaminated by artifacts, show that spatial filtering by preselection can be a useful tool during EEG review. Advantages and disadvantages of the different spatial filter approaches are discussed.

The electroencephalogram (EEG) is often contaminated by muscle artifacts. In this paper, a new method for muscle artifact removal in EEG is presented, based on canonical correlation analysis (CCA) as a blind source separation (BSS) technique. This method is demonstrated on a synthetic data set. The method outperformed a low-pass filter with different cutoff frequencies and an independent component analysis (ICA)-based technique for muscle artifact removal. In addition, the method is applied on a real ictal EEG recording contaminated with muscle artifacts. The proposed method removed successfully the muscle artifact without altering the recorded underlying ictal activity.