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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
Published in:
Biomedical Engineering, IEEE Transactions on
(Volume:53
,
Issue:
12
)
Date of Publication: Nov. 2006
- Page(s):
- 2583 - 2587
- ISSN :
- 0018-9294
- INSPEC Accession Number:
- 9193751
- Digital Object Identifier :
- 10.1109/TBME.2006.879459
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 20 November 2006
- Issue Date :
- Nov. 2006
- Sponsored by :
- IEEE Engineering in Medicine and Biology Society
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