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 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.
De Clercq
Wim
W
Department of Electrical Engineering ESAT-SCD(SISTA), Katholieke Universiteit Leuven, 3001 Leuven, Belgium. wim.declercq@lrd.kuleuven.be
Vergult
Anneleen
A
Vanrumste
Bart
B
Van Paesschen
Wim
W
Van Huffel
Sabine
S
D000200:Action Potentials; D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004576:Electromyography / Q000379:methods; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D009119:Muscle Contraction; D018482:Muscle, Skeletal / Q000503:physiopathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic