Regularized Kernel-Based Wiener Filtering. Application to Magnetoencephalographic Signals Denoising.

Ibtissam Constantin (1) , Cédric Richard (2) , Régis Lengellé (2) , Laurent Soufflet (3)

1 Université Libanaise
2 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
3 FORENAP FRP

We take a new approach in nonlinear Wiener filtering. This approach is based on the theory of reproducing kernel Hilbert spaces (RKHS). By means of the well-known "kernel trick", the arithmetic operations are carried out in the initial space. We show that the solution is given by solving a linear system which may be ill-conditioned. To find a solution for such a problem, we resorted to a kernel principal component analysis (KPCA) method to perform dimensionality reduction in RKHS. A new reduced-rank Wiener filter based on KPCA is thus elaborated. It is applied on magnetoencephalographic (MEG) data for cardiac artifacts extraction.

Domains: Computer Science [cs]

Ibtissam Constantin, Cédric Richard, Régis Lengellé, Laurent Soufflet. Regularized Kernel-Based Wiener Filtering. Application to Magnetoencephalographic Signals Denoising.. (ICASSP '05) IEEE International Conference on Acoustics, Speech, and Signal Processing 2005, Mar 2005, Philadelphia, United States. pp.289-292, ⟨10.1109/ICASSP.2005.1416002⟩. ⟨hal-02861473⟩