A new General Weighted Least-Squares Algorithm for Approximate Joint Diagonalization

Marco Congedo (1) , Christian Jutten (1) , Reza Sameni (1) , Cedric Gouy-Pailler (1)

1 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique

Independent component analysis (ICA) and other blind source separation (BSS) methods are important processing tools for multi-channel processing of electroencephalographic data and have found numerous applications for brain-computer interfaces. A number of solutions to the BSS problem are achieved by approximate joint diagonalization (AJD) algorithms, thus the goodness of the solution depends on them. We present a new least-squares AJD algorithm with adaptive weighting on the separating vectors. We show that it has good properties while keeping the greatest generality among AJD algorithms; no constraint is imposed either on the input matrices or on the joint diagonalizer to be estimated. The new cost function allows interesting extensions that are now under consideration.

Mots clés: Blind Source Separation; Approximate Joint Diagonalization

Domaines: Optimisation et contrôle [math.OC]

Marco Congedo, Christian Jutten, Reza Sameni, Cedric Gouy-Pailler. A new General Weighted Least-Squares Algorithm for Approximate Joint Diagonalization. BCI 2008 - 4th International Brain-Computer Interface Workshop and Training Course, Sep 2008, Graz, Austria. pp.98-103. ⟨hal-00324215⟩