A method of continuous decoding of motion for a direct neural interface. The method of decoding estimates a motion variable from an observation variable obtained by a time-frequency transformation of the neural signals. The observation variable is modelled using a HMM model whose hidden states include at least an active state and an idle state. The motion variable is estimated using a Markov mixture of experts where each expert is associated with a state of the model. For a sequence of observation vectors, the probability that the model is in a given state is estimated, and from this a weighting coefficient is deduced for the prediction generated by the expert associated with this state. The motion variable is then estimated by combination of the estimates of the different experts with these weighting coefficients.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of continuous decoding of motion for a direct neural interface which acquires neural signals using a plurality of electrodes, whereina command variable, y(t) is estimated which represents kinematic motion variables, using a mixture of experts, each expert being associated with one of the hidden states of a HMM model which models an observation variable represented by an observation vector, x(t), where the HMM model comprises at least one active state and one idle state, said method comprising: a calibration phase for estimating the parameters of the HMM model and of a linear prediction parameter of each expert; pre-treatment of neural signals, said treatment including a time-frequency transformation over a moving observation window, in order to obtain a sequence of observation vectors. x[1:t] estimating the mixture coefficients (gk(t),k=1, . . . , K) of the various experts from the probability of said sequence of observation vectors (P(x[1:t])) and from the probability of the respective states associated with these experts, given said sequence (P(zk(t)=1|x[1:t])); estimating by each expert. Ek, said motion variable from the running observation vector (x(t)) and the linear prediction parameter of said expert; combining estimates of the various experts using mixing coefficients in order to provide an estimate of the command variable. 2. The method of continuous decoding of motion for a direct neural interface according to claim 1, wherein the hidden states of the HMM model comprise, for each element of a plurality of elements commanded respectively by a component of the motion variable, an active state during which said element is operated and an idle state during which said element is idle. 3. The method of continuous decoding of motion for a direct neural interface according to claim 2, wherein the hidden states of the HMM module further comprise, for each of said elements, a motion preparation state which immediately succeeds an idle state and which immediately precedes an active state of said element. 4. The method of continuous decoding of motion for a direct neural interface according to claim 1, wherein, during the calibration phase, the HMM model parameters and expert linear parameters are obtained from a sequence of observation vectors and a sequence of motion variable measurements, according to a supervised learning method 5. The method of continuous decoding of motion for a direct interface according to claim 4, wherein the linear parameters of the experts are obtained with a partial least squares regression. 6. The method of continuous decoding of motion for a direct interface according to claim 4, wherein the linear parameters of the experts are obtained with a generalised partial least squares regression. 7. The method of continuous decoding of motion for a direct neural interface according to claim 1, wherein, during the calibration phase, the HMM model parameters and linear parameters of the experts are obtained from a sequence of observation vectors and a sequence of motion variable measurements, according to a semi-supervised learning method. 8. The method of continuous decoding of motion for a direct neural interface according to claim 7, wherein the linear parameters of the experts are obtained with an expectation-maximisation algorithm. 9. The method of continuous decoding of motion for a direct neural interface according to claim 1, wherein, during the calibration phase, the HMM model parameters and linear parameters of the experts are obtained from a sequence of observation vectors and a sequence of motion variable measurements, according to a non-supervised learning method. 10. The method of continuous decoding of motion for a direct neural interface according to claim 9, wherein the parameters of the HMM model and the linear parameters of the experts are obtained with an expectation-maximisation algorithm. 11. The method of continuous decoding of motion for a direct neural interface according to claim 1, wherein the time-frequency transformation is decomposition into wavelets. 12. The method of continuous decoding of motion for a direct neural interface according to claim 1, wherein the mixture coefficient gk(t) associated with the expert Ek is obtained by g k  ( t ) = P  ( z k , x  [ 1  :   t ] ) P  ( x  [ 1  :   t ] ) wherein P(x[1:t])) is the observation probability of the sequence of observation vectors and P(zk,x[1:t]) is the state probability associated with the expert, given the sequence observed, with the probabilities P(x[1:t]) and P(zk,x[1:t]) being obtained using a forward algorithm. 13.The method of continuous decoding of motion for a direct neural interface according to claim 1, wherein the estimate ŷk(t) of the variable of motion by the expert Ek is obtained by ŷk(t)=βkx(t) where βk is a matrix of size N×M where is the dimension of the observation vectors and N the dimension of the command variable. 14. The method of continuous decoding of motion for a direct neural interface according to claim 1, wherein the neural signals are electro-cortical signals.