Noise reduction in brain evoked potentials based on third-order correlations

We use third-order correlations (TOC) in developing a filtering technique for the recovery of brain evoked potentials (EPs). The main idea behind the presented technique is to pass the noisy signal through a finite impulse response filter whose impulse response is matched with the shape of the noise-free signal. It is shown that it is possible to estimate the filter impulse response on basis of a selected third-order correlation slice (TOCS) of the input noisy signal. This is justified by two facts. The first one is that the noise-free EPs can be modeled as a sum of damped sinusoidal signals and the selected TOCS preserve the signal structure. The second fact is that the TOCS is insensitive to both Gaussian noise and other symmetrically distributed non-Gaussian noise, (white or colored). Furthermore, the approach can be applied to either nonaveraged or averaged EP observation data. In the nonaveraged data case, the approach therefore preserves information about amplitude and latency changes. Both fixed and adaptive versions of the proposed filtering technique are described. Extensive simulation results are provided to show the validity and effectiveness of the proposed cumulant-based filtering technique in comparison with the conventional correlation-based counterpart.

[1]  R. Kumaresan,et al.  Estimating the parameters of exponentially damped sinusoids and pole-zero modeling in noise , 1982 .

[2]  Andrzej Cichocki,et al.  Nonlinear interference cancelation using neural networks , 1999 .

[3]  Juha Karhunen,et al.  Neural networks for blind separation with unknown number of sources , 1999, Neurocomputing.

[4]  N. Thakor,et al.  Adaptive Fourier estimation of time-varying evoked potentials , 1989, IEEE Transactions on Biomedical Engineering.

[5]  Brian M. Sadler,et al.  Estimation and detection in non-Gaussian noise using higher order statistics , 1994, IEEE Trans. Signal Process..

[6]  P. Laguna,et al.  Orthonormal (Fourier and Walsh) models of time-varying evoked potentials in neurological injury , 1993, IEEE Transactions on Biomedical Engineering.

[7]  Hosny M. Ibrahim,et al.  A higher-order statistics-based adaptive algorithm for line enhancement , 1999, IEEE Trans. Signal Process..

[8]  M. Hinich Detecting a transient signal by bispectral analysis , 1990 .

[9]  N. Ahmed,et al.  An algorithm for line enhancement , 1982, Proceedings of the IEEE.

[10]  M. Rangoussi,et al.  Detection of human nerve signals using higher-order statistics , 1996, Proceedings of 8th Workshop on Statistical Signal and Array Processing.

[11]  E. Micheli-Tzanakou,et al.  An adaptive approach to spectral analysis of pattern-reversal visual potentials , 1989, IEEE Transactions on Biomedical Engineering.

[12]  C. L. Nikias,et al.  Higher-order spectra analysis : a nonlinear signal processing framework , 1993 .

[13]  N. Thakor,et al.  Higher-order spectral analysis of burst patterns in EEG , 1999, IEEE Transactions on Biomedical Engineering.

[14]  Saeed Vaseghi Advanced Signal Processing and Digital Noise Reduction , 1996 .

[15]  S. Vorobyov APPLICATION OF ICA FOR AUTOMATIC NOISE AND INTERFERENCE CANCELLATION IN MULTISENSORY BIOMEDICAL SIGNALS , 2000 .

[16]  S.S. Rao,et al.  Parametric modeling of somatosensory evoked potentials , 1989, IEEE Transactions on Biomedical Engineering.

[17]  Melvin J. Hinich,et al.  Detecting a transient signal by bispectral analysis , 1990, IEEE Trans. Acoust. Speech Signal Process..

[18]  P. Caminal,et al.  Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques , 1992, IEEE Transactions on Biomedical Engineering.

[19]  P.G. Madhavan Minimal repetition evoked potentials by modified adaptive line enhancement , 1992, IEEE Transactions on Biomedical Engineering.

[20]  D. Liberati,et al.  Topographic mapping of single sweep evoked potentials in the brain , 1992, IEEE Transactions on Biomedical Engineering.

[21]  Reda R. Gharieb New results on employing cumulants for retrieving sinusoids in colored non-Gaussian noise , 2000, IEEE Trans. Signal Process..

[22]  R. Gharieb Higher order statistics based IIR notch filtering scheme for enhancing sinusoids in coloured noise , 2000 .

[23]  Ahmet Ademoglu,et al.  Analysis of event-related potentials (ERP) by damped sinusoids , 1998, Biological Cybernetics.

[24]  E Micheli-Tzanakou,et al.  An adaptive approach to spectral analysis of pattern-reversal visual evoked potentials. , 1989, IEEE transactions on bio-medical engineering.

[25]  H. Shibasaki,et al.  A method for real-time processing to study recovery functions of evoked potentials , 1990, IEEE Transactions on Biomedical Engineering.

[26]  Yuukou Horita,et al.  Retrieving sinusoids in colored Rayleigh noise by a cumulant-based FBLP approach , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[27]  G.B. Giannakis,et al.  Harmonic retrieval using higher order statistics: a deterministic formulation , 1995, IEEE Trans. Signal Process..

[28]  M. S. Mobin,et al.  Weighted averaging of evoked potentials , 1992, IEEE Transactions on Biomedical Engineering.

[29]  C.E. Davila,et al.  Optimal detection of visual evoked potentials , 1998, IEEE Transactions on Biomedical Engineering.

[30]  J. Bronzino,et al.  Bispectral analysis of the rat EEG during various vigilance states , 1989, IEEE Transactions on Biomedical Engineering.

[31]  T.F. Collura Real-time filtering for the estimation of steady-state visual evoked brain potentials , 1990, IEEE Transactions on Biomedical Engineering.

[32]  R. M. Harper,et al.  Laboratory computers in neurophysiology , 1973 .