Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
11023862 | Signal Processing | 2019 | 6 Pages |
Abstract
Independent Component Analyzers Mixture Models (ICAMM) are versatile and general models for a large variety of probability density functions. In this paper we assume ICAMM to derive new MAP and LMSE estimators. The first one (MAP-ICAMM) is obtained by an iterative gradient algorithm, while the second (LMSE-ICAMM) admits a closed-form solution. Both estimators can be combined by using LMSE-ICAMM to initialize the iterative computation of MAP-ICAMM .The new estimators are applied to the reconstruction of missed channels in EEG multichannel analysis. The experiments demonstrate the superiority of the new estimators with respect to: Spherical Splines, Hermite, Partial Least Squares, Support Vector Regression, and Random Forest Regression.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Gonzalo Safont, Addisson Salazar, Luis Vergara, Alberto RodrÃguez,