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Open Access Research Article

A Novel Semiblind Signal Extraction Approach for the Removal of Eye-Blink Artifact from EEGs

Kianoush Nazarpour1*, Hamid R Mohseni1, Christian W Hesse2, Jonathon A Chambers3 and Saeid Sanei1

Author Affiliations

1 Centre of Digital Signal Processing, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK

2 F. C. Donders Centre for Cognitive Neuroimaging, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands

3 Advanced Signal Processing Group, Department of Electronic and Electrical Engineering, Loughborough University, Loughborough, LE11 3TU, UK

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EURASIP Journal on Advances in Signal Processing 2008, 2008:857459  doi:10.1155/2008/857459

The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2008/1/857459


Received:5 December 2007
Accepted:11 February 2008
Published:21 February 2008

© 2008 The Author(s).

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A novel blind signal extraction (BSE) scheme for the removal of eye-blink artifact from electroencephalogram (EEG) signals is proposed. In this method, in order to remove the artifact, the source extraction algorithm is provided with an estimation of the column of the mixing matrix corresponding to the point source eye-blink artifact. The eye-blink source is first extracted and then cleaned, artifact-removed EEGs are subsequently reconstructed by a deflation method. The a priori knowledge, namely, the vector, corresponding to the spatial distribution of the eye-blink factor, is identified by fitting a space-time-frequency (STF) model to the EEG measurements using the parallel factor (PARAFAC) analysis method. Hence, we call the BSE approach semiblind signal extraction (SBSE). This approach introduces the possibility of incorporating PARAFAC within the blind source extraction framework for single trial EEG processing applications and the respected formulations. Moreover, aiming at extracting the eye-blink artifact, it exploits the spatial as well as temporal prior information during the extraction procedure. Experiments on synthetic data and real EEG measurements confirm that the proposed algorithm effectively identifies and removes the eye-blink artifact from raw EEG measurements.

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