A direct neural control method comprising: - a step of classifying neural signals in accordance with any preceding claim; - a step of generating a signal for controlling an apparatus as a function of the result of said classification step the step of classification comprising the following steps: a) acquiring a plurality of neural signal samples (x 1 (t) - x 16 (t)) over a determined period of time using a plurality of electrodes (EL1 - EL16); b) estimating a covariance matrix (X 1, X 2, X i) of said neural signals; and c) classifying said neural signals, the classification being performed: · either in the Riemann manifold of symmetric positive definite matrices of dimensions equal to the dimensions of said covariance matrix; · or else in a tangent space of said Riemann manifold the method including, prior to said classification step c): - determining a tangent space of said Riemann manifold relative to a symmetric positive matrix corresponding to a geometric mean or a median in said Riemann manifold of a set of covariance matrices of training data associated with classes; - projecting said covariance matrix that needs to be classified together with said covariance matrices of training data into said tangent space; - applying dimensional filtering in said tangent space; and - back-projecting the filtered data into said Riemann manifold in order to perform said classification step c). - a step of classifying neural signals in accordance with any preceding claim; - a step of generating a signal for controlling an apparatus a) acquiring a plurality of neural signal samples (x 1 (t) - x 16 (t)) over a determined period of time using a plurality of electrodes (EL1 - EL16); b) estimating a covariance matrix (X 1, X 2, X i) of said neural signals; and c) classifying said neural signals, the classification being performed: · either in the Riemann manifold of symmetric positive definite matrices of dimensions equal to the dimensions of said covariance matrix; · or else in a tangent space of said Riemann manifold the method including, prior to said classification step c): - determining a tangent space of said Riemann manifold relative to a symmetric positive matrix corresponding to a geometric mean or a median in said Riemann manifold of a set of covariance matrices of training data associated with classes; - projecting said covariance matrix that needs to be classified together with said covariance matrices of training data into said tangent space; - applying dimensional filtering in said tangent space; and - back-projecting the filtered data into said Riemann manifold in order to perform said classification step c). · either in the Riemann manifold of symmetric positive definite matrices of dimensions equal to the dimensions of said covariance matrix; · or else in a tangent space of said Riemann manifold the method including, prior to said classification step c): - determining a tangent space of said Riemann manifold relative to a symmetric positive matrix corresponding to a geometric mean or a median in said Riemann manifold of a set of covariance matrices of training data associated with classes; - projecting said covariance matrix that needs to be classified together with said covariance matrices of training data into said tangent space; - applying dimensional filtering in said tangent space; and - back-projecting the filtered data into said Riemann manifold in order to perform said classification step c). - determining a tangent space of said Riemann manifold relative to a symmetric positive matrix corresponding to a geometric mean or a median in said Riemann manifold of a set of covariance matrices of training data associated with classes; - projecting said covariance matrix that needs to be classified together with said covariance matrices of training data into said tangent space; - applying dimensional filtering in said tangent space; and - back-projecting the filtered data into said Riemann manifold in order to perform said classification step c).