TY - GEN
T1 - Influence of music listening on the cerebral activity by analyzing EEG
AU - Ogawa, Takahiro
AU - Ota, Satomi
AU - Ito, Shin Ichi
AU - Mitsukura, Yasue
AU - Fukumi, Minoru
AU - Akamatsu, Norio
PY - 2005/12/1
Y1 - 2005/12/1
N2 - In order to solve a stress problem, researchers have studied music therapy. It takes the therapist and patient a long time to select the music. Because the music used in music therapy is of various type. If the music for it is easily selectable, the music therapy can be carried out more effectively. In this paper, the purpose is extraction of features that may be influenced by the music. We pay attention to EEG (electroencephalogram) as an objective and absolute scale. In this paper, we propose a method that extracts features of the EEG by PCA (principal component analysis) and CDA (canonical discriminant analysis). Then we analyze each feature data by NN (neural network). In order to examine whether the proposal system is effective, we try computer simulations for the EEG classification. According to recognition rate by the NN, it was considered that the CDA extracted and classified the features of the EEG better than the PCA.
AB - In order to solve a stress problem, researchers have studied music therapy. It takes the therapist and patient a long time to select the music. Because the music used in music therapy is of various type. If the music for it is easily selectable, the music therapy can be carried out more effectively. In this paper, the purpose is extraction of features that may be influenced by the music. We pay attention to EEG (electroencephalogram) as an objective and absolute scale. In this paper, we propose a method that extracts features of the EEG by PCA (principal component analysis) and CDA (canonical discriminant analysis). Then we analyze each feature data by NN (neural network). In order to examine whether the proposal system is effective, we try computer simulations for the EEG classification. According to recognition rate by the NN, it was considered that the CDA extracted and classified the features of the EEG better than the PCA.
UR - http://www.scopus.com/inward/record.url?scp=33745290867&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33745290867&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33745290867
SN - 3540288945
SN - 9783540288947
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 657
EP - 663
BT - Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
T2 - 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
Y2 - 14 September 2005 through 16 September 2005
ER -