Sadasivan, PK and Dutt, D Narayana (1997) Development of Newton-type adaptive algorithm for minimization of EOG artefacts from noisy EEG signals. In: Signal Processing, 62 (2). pp. 173-186.
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In this paper, we propose an adaptive algorithm using Newton's method for the enhancement of EEG signals in the presence of EOG artefacts. We consider two models for the noise (i.e., artefacts) estimation: (i) the conventional linear model and (ii) a nonlinear model using second-order Volterra function. Application of Newton's method to the linear case results in the conventional recursive least-squares algorithm. Since the parameters of the nonlinear model are specified in terms of a vector and a matrix, the conventional Newton's method could not be applied. Hence, the underlying cost function has been reformulated whereby all the parameters are represented by a single vector and then Newton's method is applied to this reformulated cost function. While developing the algorithm for the nonlinear case, the Hessian matrix is approximated because of the special property of the reference signal. This ensures the reduced computational complexity and positive definiteness of the approximated Hessian so as to ensure search along the descent directions. Another algorithm making use of the exact Hessian matrix is also derived. These algorithms were used to minimize the EOG artefacts from EEG signals. Simulation results show that the nonlinear scheme with approximated Hessian works well compared to the other two algorithms in minimizing EOG artefacts from contaminated EEG signals.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Elsevier Science.|
|Keywords:||Newton-type algorithm;adaptive minimization;nonlinear modeling;Hessian;electroencephalogram;EOG artefacts;noise reduction.|
|Department/Centre:||Division of Electrical Sciences > Electrical Communication Engineering|
|Date Deposited:||18 Mar 2009 07:29|
|Last Modified:||19 Sep 2010 05:25|
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