A method for calibrating a direct neural interface , the interface being able to acquire electrophysiological signals ( S 1 ... S I 3 ) and to form an input tensor ( X ), on the basis of the electrophysiological signals acquired, so as to establish, with the aid of a predictive model, an output tensor ( Y ), intended to control an effector , the predictive model being obtained by calibration, the calibration comprising the following steps: a) acquisition of so-called input electrophysiological calibration signals to obtain a so-called input calibration tensor ( X ( t ) ), the electrophysiological calibration signals being representative of neural activity during calibration; b) acquisition of so-called output calibration signals to obtain an output calibration tensor, so-called ( Y ( t ) ), each output calibration signal being representative of a control signal for the effector or of a state of the effector during calibration; c) calculation of a covariance tensor ( C XX ( t ) ), representing a covariance of the input calibration tensor ( X ( t ) ); d) calculation of a cross-covariance tensor ( C XY ( t ) ) representing a covariance of the input calibration tensor ( X ( t ) ) and of the output calibration tensor ( Y ( t ) ); e) application of a partial least squares multivariate regression on the basis of the covariance tensor obtained in step c) and of the cross-covariance tensor obtained during step d) so as to obtain a predictive model B ̃ f f = 1 F t Y _ 0 f f = 1 F t , as well as calibration parameters w 1 f , ... w k f f = 1 F t ; steps a) to e) being implemented during a current calibration period (t), the method being such that after a first calibration period ( t = 1), - step c) comprises taking into account of the covariance tensor ( C XX (t-1) ) calculated in the course of an earlier calibration period ( t - 1), and weighted by a forget factor ( λ ); - step d) comprises taking into account of the cross-covariance tensor ( C XY ( t -1) ) calculated in the course of the earlier calibration period ( t - 1), and weighted by the forget factor ( λ ); - step e) comprises taking into account of calibration parameters w 1 f , ... w k f f = 1 F t − 1 resulting from the earlier calibration period. a) acquisition of so-called input electrophysiological calibration signals to obtain a so-called input calibration tensor ( X ( t ) ), the electrophysiological calibration signals being representative of neural activity during calibration; b) acquisition of so-called output calibration signals to obtain an output calibration tensor, so-called ( Y ( t ) ), each output calibration signal being representative of a control signal for the effector or of a state of the effector during calibration; c) calculation of a covariance tensor ( C XX ( t ) ), representing a covariance of the input calibration tensor ( X ( t ) ); d) calculation of a cross-covariance tensor ( C XY ( t ) ) representing a covariance of the input calibration tensor ( X ( t ) ) and of the output calibration tensor ( Y ( t ) ); e) application of a partial least squares multivariate regression on the basis of the covariance tensor obtained in step c) and of the cross-covariance tensor obtained during step d) so as to obtain a predictive model B ̃ f f = 1 F t Y _ 0 f f = 1 F t , as well as calibration parameters w 1 f , ... w k f f = 1 F t ; - step c) comprises taking into account of the covariance tensor ( C XX (t-1) ) calculated in the course of an earlier calibration period ( t - 1), and weighted by a forget factor ( λ ); - step d) comprises taking into account of the cross-covariance tensor ( C XY ( t -1) ) calculated in the course of the earlier calibration period ( t - 1), and weighted by the forget factor ( λ ); - step e) comprises taking into account of calibration parameters w 1 f , ... w k f f = 1 F t − 1 resulting from the earlier calibration period. <EOS>