1. Process for the computerized prevision of intended movements (X) from neuronal signals (Y) of a proband's brain, wherein the neuronal signals (Y) are associated each time with intended movements (X) in the brain, wherein the most likely movements (X) are determined based on detected neuronal signals (Y), namely using at least one model, wherein the at least one model describes a mapping (H) of neuronal signals (Y) on the possible intended movements (X), wherein the process comprises: a step in which the at least one model is updated, wherein the update takes place using the detected neuronal signals (Y) and without knowledge of the intended movement, and a step of the calculation of the currently most likely movement X with the at least one updated model wherein the at least one model (H) is determined each time by at least one characteristic and the at least one characteristic of the at least one model is updated in the updating step, wherein the at least one model (H) is an adaptive model (p(Y|X)) of Gauss distributed probabilities for a predetermined number, K, of classes of movements (X), wherein the at least one model comprises the average (mu_k) with k={1,. .. , K} as well as the covariance matrix (C_k) of the Gauss distribution as at least one characteristic, and in wherein the average (mu_k) of the predetermined classes of movement (X_ 1 ,. .. , X_k) is updated each time in the second step using a predetermined number (N) of detected signals (Y), and the covariance matrix (C) is updated using a predetermined number (N) of detected sianals (Y) and of the updated average, wherein the following applies for each of the probabilities p(X k |Y)): p ⁡ ( X k ❘ Y ) = p ⁡ ( X k ) ⁢ p ⁡ ( Y ❘ X k ) ∑ k = 1 K ⁢ ⁢ p ⁡ ( Y ❘ X k ) ⁢ p ⁡ ( X k ) wherein k indicates the index of the class, for the averages the following applies: μ k = 1 p ⁡ ( X k ) ⁢ N ⁢ ∑ i = 1 N ⁢ ⁢ p ⁡ ( X k ❘ Y i ) ⁢ Y i and the following applies for the covariance: C = 1 N - 1 ⁢ ∑ k = 1 K ⁢ ∑ i = 1 N ⁢ p ⁡ ( X k ❘ Y i ) ⁢ ( Y i - μ k ) ⁢ ( ( Y i - μ k ) T .