A novel semiblind signal extraction approach for the removal of eye-blink artifact from EEGs

Authors:
Kianoush Nazarpour;Hamid R. Mohseni;Christian W. Hesse;Jonathon A. Chambers;Saeid Sanei
Affiliations:
Centre of Digital Signal Processing, School of Engineering, Cardiff University, Cardiff, UK;Centre of Digital Signal Processing, School of Engineering, Cardiff University, Cardiff, UK;F. C. Donders Centre for Cognitive Neuroimaging, Kapittelweg, EN Nijmegen, The Netherlands;Advanced Signal Processing Group, Department of Electronic and Electrical Engineering, Loughborough University, Loughborough, UK;Centre of Digital Signal Processing, School of Engineering, Cardiff University, Cardiff, UK
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2008

Citing 7
Cited 3

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Blind spatial signature estimation via time-varying user power loading and parallel factor analysis

IEEE Transactions on Signal Processing
Blind PARAFAC receivers for DS-CDMA systems

IEEE Transactions on Signal Processing
Blind MIMO System Estimation Based on PARAFAC Decomposition of Higher Order Output Tensors

IEEE Transactions on Signal Processing
Parallel factor analysis in sensor array processing

IEEE Transactions on Signal Processing
Robust iterative fitting of multilinear models

IEEE Transactions on Signal Processing - Part I
Approach and applications of constrained ICA

IEEE Transactions on Neural Networks

Removal of ballistocardiogram artifact from electroencephalograms exploiting heart rate variability

DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Automatic removal of eye-blink artifacts based on ICA and peak detection algorithm

CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data

Signal Processing

Quantified Score

Hi-index	0.00

Visualization

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 eyeblink 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 eyeblink artifact from raw EEG measurements.