ITERATIVE CALIBRATION METHOD FOR A DIRECT NEURAL INTERFACE USING A MARKOV MIXTURE OF EXPERTS WITH MULTIVARIATE REGRESSION

摘要

This invention relates to a method of calibrating a direct neural interface with continuous coding. The observation variable is modelled by an HMM model and the control variable is estimated by means of a Markov mixture of experts, each expert being associated with a state of the model.;During each calibration phase, the predictive model of each of the experts is trained on a sub-sequence of observation instants corresponding to the state with which it is associated, using an REW-NPLS (Recursive Exponentially Weighted N-way Partial Least Squares) regression model.;A second predictive model giving the probability of occupancy of each state of the HMM model is also trained during each calibration phase using an REW-NPLS regression method. This second predictive model is used to calculate Markov mixture coefficients during a later operational prediction phase.