[en] EEG signals are often contaminated by ocular artifacts (OAs), in particular when they are recorded for a subject that is, in principle, awake, such as in a study of drowsiness. It is generally desirable to detect and/or correct these OAs before interpreting the EEG signals. We have identified 11 existing methods for dealing with OAs. Their study allowed us to create 16 new methods. We performed a comparative performance evaluation of the resulting 27 distinct methods using a common set of data and a common set of metrics. The data was recorded during a driving task of about two hours in a driving simulator. This led to a ranking of all methods, with five emerging clear winners, comprising two existing methods and three new ones.
Disciplines :
Neurology
Author, co-author :
Kirkove, Murielle ; Université de Liège - ULiège > CSL (Centre Spatial de Liège)
François, Clémentine ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Exploitation des signaux et images
Verly, Jacques ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Exploitation des signaux et images
Language :
English
Title :
Comparative evaluation of existing and new methods for correcting ocular artifacts in electroencephalographic recordings
R.J. Croft, and R.J. Barry Removal of ocular artifact from the EEG: a review Clin. Physiol. 30 1 2000 5 19 (Pubitemid 30148764)
A. Kandaswamy, V. Krishnaveni, S. Jayaraman, N. Malmurugan, and K. Ramadoss Removal of ocular artifacts from EEG: a survey IETE J. Res. 51 2 2005 121 130 (Pubitemid 40680693)
M.T. Akhtar, W. Mitsuhashi, and C.J. James Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data Signal Process 92 2 2012 401 416
S. Mallat. A Wavelet Tour of Signal Processing second ed. 1999 Academic Press
G.P. Nason, and B.W. Silverman. The stationary wavelet transform and some statistical applications Lecture Notes in Statistics 103 1995 281 300
V. Krishnaveni, S. Jayaraman, S. Aravind, V. Hariharasudhan, and K. Ramadoss Automatic identification and removal of ocular artifacts from EEG using wavelet transform Meas. Sci. Rev 6 2 2006 45 57 (no. 4)
V. Krishnaveni, S. Jayaraman, L. Anitha, and K. Ramadoss Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients J. Neural Eng 3 4 2006 338 346
D.L. Donoho, and I.M. Johnstone Adapting to unknown smoothness via wavelet shrinkage J. Am. Statist 90 432 1995 1200 1224 Assoc 90 432 1995 1200 1224 (Pubitemid 126093135)
Z. Xiao-Ping, and M.D. Desai Adaptive denoising based on SURE risk IEEE Signal Process. Lett 5 10 1998 265 267 (Pubitemid 128556770)
C.M. Stein Estimation of the mean of a multivariate normal distribution Ann. Stat. 9 6 1981 1135 1151
A.V. Oppenheim, and R.W. Schafer Discrete-time Signal Processing second ed. 1999 Prentice-Hall
S. Puthusserypady, and T. Ratnarajah Robust adaptive techniques for minimization of EOG artifacts from EEG signals Signal Process 86 9 2006 2351 2363 (Pubitemid 44015901)
M.A. Klados, C. Papadelis, C. Lythari, P.D. Bamidis. The removal of ocular artifacts from EEG signals: a comparison of performances for different methods, in: Fourth European Conf. of the International Federation for Medical and Biological Engineering. J. Sloten, P. Verdonck, M. Nyssen and J. Haueisen, Springer Berlin Heidelberg, 22 (2008) 1259-1263.
M.A.G. Correa, E.L. Leber. Noise removal from EEG signals in polysomnographic records applying adaptive filters in cascade. Adaptive Filtering Applications, L.G. (Ed.), 2011.
P.S. Kumar, R. Arumuganathan, K. Sivakumar, C. Vimal. Removal of artifacts from EEG signals using adaptive filter through wavelet transform signal processing. In: ninth IEEE International Conference on Signal Process., 2008.
P. Tichavský, and Z. Koldovský. Fast and accurate methods of independent component analysis: a survey Kybernetika 47 2011 426 438
T.W. Lee Independent Component Analysis: Theory and Applications 1998 Kluwer Academic Publishers
A. Hyvärinen, and E. Oja Independent component analysis: algorithms and applications Neural Networks 13 2000 411 430 (Pubitemid 30447427)
A. Bell, and T. Sejnowski. An information-maximization approach to blind separation and blind deconvolution Neural Comput. 7 6 1995 1129 1159
T.-W. Lee, M. Girolami, and T.-J. Sejnowski Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources Neural Comput. 11 1999 417 441
Z. Koldovský, P. Tichavský, and E. Oja Efficient variant of algorithm FastICA for independent component analysis attaining the Cramér-Rao lower bound IEEE Trans. Neural Networks 17 2006 1265 1277 (Pubitemid 46445183)
D.-T. Pham, and J.F. Cardoso Blind separation of instantaneous mixtures of non stationary sources IEEE Trans. Signal Process 49 2001 1837 1848 (Pubitemid 32866193)
D.T. Pham Joint approximate diagonalization of positive definite Hermitian matrices SIAM J. Matrix Anal. Appl 22 2001 1136 1152
P. Tichavský, and A. Yeredor Fast approximate joint diagonalization incorporating weight matrices IEEE Trans. Signal Process 57 2009 878 891
A. Belouchrani, K. Abed-Meraim, and J.F. Cardoso. A blind source separation technique using second-order statistics IEEE Signal Process 45 2 1997 434 444 (Pubitemid 127765906)
D.T. Pham Blind separation of instantaneous mixture of sources via the Gaussian mutual information criterion Signal Process 81 2001 855 870
S. Dégerine, and A. Zaïdi Separation of an instantaneous mixture of Gaussian autoregressive sources by the exact maximum likelihood approach IEEE Trans. Signal Process 52 2004 1492 1512
H. Ghandeharion, and A. Erfanian. A fully automatic ocular artifact suppression from EEG data using higher order statistics: improved performance by wavelet analysis Med. Eng. Phys 32 7 2010 720 729
M. Zima, P. Tichavský, K. Paul, and V. Krajča Robust removal of short-duration artifacts in long neonatal EEG recordings using wavelet-enhanced ICA and adaptive combining of tentative reconstructions Phys. Meas. 33 8 2012 39 49
N. Mammone, F. La Foresta, and F.C. Morabito Automatic artifact rejection from multichannel scalp EEG by wavelet ICA IEEE Sens. J. 12 3 2012 533 542
N.P. Castellanos, and V.A. Makarov Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis J. Neurosci. Methods 158 2006 300 312 (Pubitemid 44738074)
R. Oostenveld, P. Fries, E. Maris, J.M. Schoffelen. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. (2011).
