METHOD FOR CALIBRATING ON-LINE AND WITH FORGETTING FACTOR A DIRECT NEURAL INTERFACE WITH PENALISED MULTIVARIATE REGRESSION

摘要

The present invention relates to a method for calibrating on-line a direct neural interface implementing a REW-NPLS regression between an output calibration tensor and an input calibration tensor. The REW-NPLS regression comprises a PARAFAC iterative decomposition of the cross covariance tensor between the input calibration tensor and the output calibration tensor, each PARAFAC iteration comprising a sequence of M elementary steps (2401, 2401, . . . 240M) of minimisation of a metric according to the alternating least squares method, each elementary minimisation step relating to a projector and considering the others as constant, said metric comprising a penalisation term that is a function of the norm of this projector, the elements of this projector not being subjected to a penalisation during a PARAFAC iteration f not being penalisable during following PARAFAC iterations. Said calibration method makes it possible to obtain a predictive model of which the non-zero coefficients are sparse blockwise.