A method for deriving optimal discriminating features indicative of a subject state when the subject listens to one of a set of musical pieces, comprising a step of extracting frequency features from the subject's EEG signal when the subject is in a first subject state and a second subject state, the frequency features being extracted from more than one frequency band in one set of time segments; and identifying optimal discriminating features from the extracted frequency features, the optimal discriminating features indicative of characteristics of the EEG signal when the subject is in the first subject state and the second subject state, wherein one of the first subject state and the second subject state indicates that the subject likes a musical piece while the other state indicates that the subject does not like the musical piece.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for deriving optimal discriminating features indicative of a subject state when the subject listens to one of a set of musical pieces, the method comprising: extracting frequency features from the subject's EEG signal when the subject is in a first subject state and a second subject state, the frequency features being extracted from more than one frequency band in one set of time segments; and identifying optimal discriminating features from the extracted frequency features, the optimal discriminating features indicative of characteristics of the EEG signal when the subject is in the first subject state and the second subject state, wherein one of the first subject state and the second subject state indicates that the subject likes a musical piece while the other state indicates that the subject does not like the musical piece. 2. The method according to claim 1, wherein the step of identifying optimal discriminating features includes tabulating a matrix including the extracted frequency features in each corresponding frequency band. 3. The method according to claim 1, wherein the step of identifying optimal discriminating features includes deriving optimal spectral filters, each of the optimized spectral filters being frequency band associated with the optimal discriminating features. 4. The method according to claim 3, wherein the step of identifying optimal discriminating features includes obtaining a signal after passing each EEG signal through the optimal spectral filters, each EEG signal being obtained when the subject listens to a corresponding one of the set of musical pieces. 5. The method according to claim 4, wherein the signal is used to calculate a form feature vector for each time segment. 6. The method according to claim 5, wherein the form feature vector is used to generate a musical preference score for each of the set of the musical pieces, the musical preference score indicating the subject's preference of the musical piece. 7. The method according to claim 6, wherein the musical preference score is used to control the set of the musical pieces, wherein the musical pieces is controlled to stop playing when the musical preference score indicates that the subject does not like the musical piece. 8. The method according to claim 1, further comprising the step of extracting musical features associated with each of the set of the musical pieces; and comparing the extracted musical features with the optimal discriminating features to determine which of the extracted musical features are indicative of characteristics of musical pieces that the subject prefers. 9. The method according to claim 8, further comprising developing a model in response to the optimal discriminating features of the subject when listening to one of the set of the musical pieces and the musical features for the one of the set of the musical pieces. 10. The method according to claim 9, wherein the model is used for enhancing musical parameters of an acoustic signal of one or more of the set of musical pieces. 11. The method according to claim 9, wherein the model is used to organize the order of the set of musical pieces. 12. The method according to claim 9, wherein the set of musical pieces are stored in an external database. 13. The method according to claim 9, wherein the step of developing the model further comprises developing the model in response to the optimal discriminating features of one or more other subjects when listening to the one of the set of the musical pieces and the musical features for the one of the set of the musical pieces. 14. A method for developing a model indicative of a subject state when the subject listens to one of a set of musical pieces, the method comprising: extracting frequency features from the subject's EEG signal when the subject is in a first subject state and a second subject state, the frequency features being extracted from more than one frequency band in one set of time frames; identifying optimal discriminating features from the extracted frequency features, the optimal discriminating features indicative of similar characteristics of the EEG signal when subject is in the first subject state and the second subject state; extracting musical features associated with each of a set of the musical pieces; comparing the extracted musical features with the optimal discriminating features to determine which of the extracted musical features are indicative of characteristics of musical pieces that the subject prefers; and developing a model in response to the optimal discriminating features of the subject when listening to one of the set of the musical pieces and the musical features for the one of the set of the musical pieces, wherein one of the first subject state and the second subject state indicates that the subject likes a musical piece while the other state indicates that the subject does not like the musical piece. 15. The method according to claim 14, wherein the step of developing the model further comprises developing the model in response to the optimal discriminating features of one or more other subjects when listening to the one of the set of the musical pieces and the musical features for the one of the set of the musical pieces. 16. The method according to claim 14, wherein the model is used for enhancing musical parameters of acoustic signal of one or more of the set of musical pieces. 17. The method according to claim 14, wherein the model is used to organize the order of the set of musical pieces. 18. The method according to claim 14, wherein the EEG signal is obtained by at least one electrode, the at least one electrode being placed on the forehead of the subject. 19. A system for developing a model indicative of a subject state when the subject listens to one of a set of musical pieces, comprising: an input device for the system operable to receive electroencephalography (EEG) signal; an EEG discriminative feature generator operable to extract frequency features from the received EEG signal when the subject is in a first subject state and a second subject state and identify optimal discriminating features from the extracted frequency features, the optimal discriminating features indicative of similar characteristics of the EEG signal when subject is in the first subject state and the second subject state; a model builder operable to develop a model in response to the optimal discriminating features of the subject when listening to one of the set of the musical pieces and the musical features for the one of the set of the musical pieces; a music scorer operable to generate a musical preference score for each of the set of the musical pieces, the musical preference score indicating the subject's preference of the musical piece; and a music controller operable to control the set of the musical pieces, wherein the musical pieces is controlled to stop playing when the musical preference score indicates that the subject does not like the musical piece, wherein one of the first subject state and the second subject state indicates that the subject likes a musical piece while the other state indicates that the subject does not like the musical piece.