EEG ocular artefacts and noise removal.
The general framework of this research is the pre-processing of the electroencephalographic (EEG) signals. The goal of this paper is to compare several combinations of wavelet denoising (WD) and independent component analysis (ICA) algorithms for noise and artefacts removal. These methods are tested on simulated EEG, using different evaluation criteria. According to our results, the most effective method consists in source separation by SOBI-RO [1], followed by wavelet denoising by SURE thresholding [2].
Romo-Vazquez
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Centre de Recherche en Automatique de Nancy (CRAN-UMR 7039), Nancy-University, CNRS, ENSEM, 2 Avenue de la Forêt de Haye, Nancy, France. rebeca.romo-vazquez@ensem.inpl-nancy.fr
Ranta
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Louis-Dorr
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Maquin
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D000368:Aged; D000465:Algorithms; D016477:Artifacts; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D005133:Eye Movements; D005260:Female; D006801:Humans; D008297:Male; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted