The invention relates to a method for reconstructing intended activities from a first representation of neural signals which is indicative of an intended activity. For second representations, a degree of agreement between the first representation and each second representation from a plurality of predetermined second representations that are indicative of intended activities is determined on the basis of a predetermined agreement criterion, and a second representation of the neural signals is selected from the plurality of second representations on the basis of the degree of agreement. The selected second representation is the reconstructed intended activity. The invention further relates to a database for use in a method according to the invention and a signal processing device for reconstructing intended activities.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for reconstruction of intended activities from a first representation (R1) of neural signals (S) which is indicative of an intended activity, wherein the first representation (R1) is generated using a predetermined method from the neural signals (S), for each second (Ri) from a plurality of predetermined second representations (RR), each of which being representative of a predetermined activity, wherein the plurality of predetermined second representations (RR) are generated from predetermined activities, a degree of correspondence is determined respectively between each of the respective second representations (Ri) and the first representation (R1) according to a predetermined correspondence criterion, and depending on the degree of correspondence, a single second representation (R2) of the neural signals (S) from the plurality of second representations (RR) is selected as the second representation (R2) representative of the intended activity, and the activity represented by the selected representation (R2) is reconstructed as the intended activity. 2. The method of claim 1, wherein the determination of the degree of correspondence in accordance with the correspondence criterion comprises a determination of a total correspondence probability (Pi) for each second representation (Ri) from the plurality of second representations (RR), wherein the total correspondence probability (Pi) from a combination of first partial probabilities (TPi), which are created for each second representation (Ri) from the plurality of second representations (RR) and wherein the total correspondence probability (Pi) for each second representation (Ri) indicates the probability with which the respective second representation (Ri) of the first representation (R1) is assignable. 3. The method of claim 2, wherein the second representation (R2) with the greatest total correspondence probability (Pi) is selected. 4. The method of claim 2, wherein each of the first partial probabilities (TPi) is weighted by a weighting factor (Gi), wherein the weighting factor (Gi) can be selected differently for each partial probability (TPi). 5. The method of claim 2, wherein forming the first partial probabilities (TPi) comprises at least one of determining a partial probability (TP1i) based on the frequency of the second representation (Ri) within the plurality of predetermined second representations (RR), determining a partial probability (TP2i) based on semantic relationships of the second representation (Ri) to already selected second representations, and determining a partial probability (TP3i) based on a similarity of the second representation (Ri) to the first representation (R1). 6. The method of claim 1, wherein prior to the selection of the second representation (R2) from the plurality of second representations (RR), it is verified whether the total correspondence probability (Pi) of the second representation (R2) to be selected exceeds a predetermined threshold (PS), and wherein when the predetermined threshold (PS) is not exceeded, the first representation (R1) is the reconstructed intended activity. 7. The method of claim 2, wherein on the basis of the respectively determined total correspondence probability (Pi), a number of second partial probabilities (TPj) is determined, with which in turn a new total correspondence probability (Pi) for each second representation (Ri) from the plurality of second representations (RR) is determined. 8. The method of claim 1, wherein the plurality of predetermined second representations (RR) is stored in a database (DB). 9. The method of claim 8, wherein for each second representation (Ri), a selection frequency, and/or for the second representations (Ri) stored in the database, a number of selection sequences is stored in the database. 10. The method of claim 8, wherein first representations are stored in the database, and wherein the stored first representations are correlated in the database to selected second representations, wherein first representations correlated to a second representation (Ri) are used to determine the similarity of the first representations (R1) to a second representation (Ri). 11. The method of claim 10, wherein the second representation is replaced by the first representation which has been correlated to the second representation (Ri). 12. The method of claim 1, wherein the first representations and the second representations comprise at least one out of movements, speech, emotions, pictures, smells, and tactile sense. 13. The method of claim 1, wherein the signals (S) are derived from a neural activity of the brain of a human being. 14. The method of claim 13, wherein the signals (S) are derived from a man-machine-interface arranged outside the human being. 15. The method of claim 1, wherein prior to selecting a second representation (R2), it is verified whether the degree of correspondence satisfies a predetermined criterion, and if the degree of correspondence does not satisfy the predetermined criterion, the intended activity is reconstructed from the first representation (R1). 16. The method of claim 1, wherein the predetermined method comprises applying a predetermined model, with which a neural brain activity is translated into the intended activity, wherein the total of intended activities forms a continuous parameter space. 17. A database (DB) for use in a method for reconstructing intended activities according to claim 1, wherein the database is adapted to store second representations (RR), which respectively are representative of a predetermined activity, and to select stored second representations according to a predetermined correspondence criterion and compare them to first representations, which are generated from a measured neural activity of the brain of a human being. 18. The database of claim 17, wherein the database is further adapted to store first representations, which have been generated from a measured neural activity of the brain of a human being, and to link the first representations to the second representations stored in the database and/or replace second representations stored in the database by the first representation. 19. The method of claim 1, wherein a tool comprising at least one out of a prosthesis, speakers and display means is controlled by a selected second representation. 20. A signal processing means (SVE) for reconstruction of intended activities, which can be coupled to a database (DB) and an interface (I), wherein the interface (I) is adapted to receive signals (S) from a man-machine interface (MMI), wherein the signals represent a neural activity of the brain of a human being, and wherein the signal processing means is adapted to generate a first representation (R1) from neural signals (S) using a predetermined method, determine, for second representations (Ri) from a plurality of predetermined second representations (RR), which respectively are representative for a predetermined activity, wherein the predetermined second representations (RR) are generated from predetermined activities, respectively a degree of correspondence between the respective second representation (Ri) and the first representation (R1) according to a predetermined correspondence criterion, and depending on the degree of correspondence, select a second representation (R2) of the neural signals (S) from the plurality of second representations (RR) as the second representation (R2) representative of the intended activity, and to reconstruct the activity represented by the selected representation (R2) as the intended activity. 21. The signal processing means of claim 20, wherein prior to selecting a second representation (R2), it is verified whether the degree of correspondence satisfies a predetermined criterion, and if the degree of correspondence does not satisfy the predetermined criterion, the intended activity is reconstructed from the first representation (R1). 22. The signal processing means of claim 20, wherein the predetermined method comprises applying a predetermined model by means of which a neural activity of the brain is translated into the intended activity, wherein the entirety of intended activities for a continuous parameter space. 23. The of claim 1, wherein each one of the representations respectively represents a sonogram. 24. The method of claim 1, wherein each one of the representations respectively represents a trajectory of motions. 25. The method of claim 1, wherein each one of the representations respectively is an image representation.