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...

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Hauptverfasser:	Romo-Vazquez, R., Ranta, R., Louis-Dorr, V., Maquin, D.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Additive noise Aged Algorithms Bioengineering Brain modeling Computer Science Diagnosis, Computer-Assisted - methods Electroencephalography Electroencephalography - methods Engineering Sciences Epilepsy - diagnosis Eye Movements Female Humans Independent component analysis Life Sciences Male Noise reduction Principal Component Analysis Reproducibility of Results Sensitivity and Specificity Signal and Image Processing Signal processing Signal processing algorithms Signal Processing, Computer-Assisted Signal to noise ratio Source separation Testing
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Beschreibung
Zusammenfassung:	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].
ISSN:	1094-687X 1557-170X 1558-4615
DOI:	10.1109/IEMBS.2007.4353577