Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram

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...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2006-12, Vol.53 (12), p.2583-2587
Main Authors: De Clercq, Wim, Vergult, Anneleen, Vanrumste, Bart, Van Paesschen, Wim, Van Huffel, Sabine
Format: Article
Language:English
Subjects:
Action Potentials
Algorithms
Autocorrelation
Blind source separation
Brain
Brain - physiopathology
canonical correlation analysis
Cutoff frequency
Diagnosis, Computer-Assisted - methods
EEG
Electroencephalography
Electroencephalography - methods
Electromyography - methods
Epilepsy - diagnosis
Epilepsy - physiopathology
Humans
Independent component analysis
Low pass filters
muscle artifact removal
Muscle Contraction
Muscle, Skeletal - physiopathology
Muscles
Reproducibility of Results
Sensitivity and Specificity
Signal analysis
Source separation
Statistics as Topic
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Summary: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
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2006.879459