A method for online calibration of a direct neural interface intended to receive a plurality of electrophysiological signals during a plurality of observation windows associated with observation instants, to form an input tensor and deduce therefrom, by means of a predictive model represented by a prediction tensor, an output tensor providing control signals intended to control one or more effectors, said calibration being carried out during a plurality of calibration phases, each calibration phase u being carried out from input calibration data, represented by an input calibration tensor, X _ u ∈ R I 1 ×... × I N, and output calibration data represented by an output calibration tensor, Y _ u ∈ R J 1 ×... × J M said calibration phase implementing a penalised REW-NPLS regression, a REW-NPLS regression being a Recursive Exponentially Weighted N-way Partial Least Squares method, the penalisation relating to at least one modality of the prediction tensor and involving at least one hyperparameter, characterised in that, during a calibration phase: - a subset, Λ u test, of values of the hyperparameter to be tested is extracted from a set, Λ, of values of the hyperparameter; - a prediction performance indicator ( e u λ, f) is calculated for each predictive model of a plurality of penalised predictive models, each penalised predictive model ( Θ u − 1 λ, f) being associated with a dimension (f) of the space of latent variables and having been obtained, during the previous calibration phase, u-1, by REW-NPLS regression penalised by means of a hyperparameter value (λ) of said subset, the prediction indicator of the penalised predictive model representing the quality of the prediction of the output calibration tensor by this model from the input calibration tensor; - for each hyperparameter value of said subset, a reward ( R u λ) associated with the choice of this value is calculated from the prediction performance indicators ( e u λ, f) of the penalised predictive models ( Θ u − 1 λ, f); - for each hyperparameter value (λ) of said subset, an action value ( Q u λ) corresponding to the choice of this value is updated from the reward obtained ( R u λ) for the choice of this value in the previous step; - a hyperparameter value (A*) is selected according to an action selection strategy among the set Λ of hyperparameter values from the action values thus updated ( Q u λ); - each of the penalised predictive models of said plurality is updated, by means of a REW-NPLS regression penalised by means of a hyperparameter value (λ) of the set Λ; - the predictive model penalised by means of said hyperparameter value selected and updated in the previous step is used to predict a control tensor providing said control signals from said input tensor. - a subset, Λ u test, of values of the hyperparameter to be tested is extracted from a set, Λ, of values of the hyperparameter; - a prediction performance indicator ( e u λ, f) is calculated for each predictive model of a plurality of penalised predictive models, each penalised predictive model ( Θ u − 1 λ, f) being associated with a dimension (f) of the space of latent variables and having been obtained, during the previous calibration phase, u-1, by REW-NPLS regression penalised by means of a hyperparameter value (λ) of said subset, the prediction indicator of the penalised predictive model representing the quality of the prediction of the output calibration tensor by this model from the input calibration tensor; - for each hyperparameter value of said subset, a reward ( R u λ) associated with the choice of this value is calculated from the prediction performance indicators ( e u λ, f) of the penalised predictive models ( Θ u − 1 λ, f); - for each hyperparameter value (λ) of said subset, an action value ( Q u λ) corresponding to the choice of this value is updated from the reward obtained ( R u λ) for the choice of this value in the previous step; - a hyperparameter value (A*) is selected according to an action selection strategy among the set Λ of hyperparameter values from the action values thus updated ( Q u λ); - each of the penalised predictive models of said plurality is updated, by means of a REW-NPLS regression penalised by means of a hyperparameter value (λ) of the set Λ; - the predictive model penalised by means of said hyperparameter value selected and updated in the previous step is used to predict a control tensor providing said control signals from said input tensor.