TY - GEN
T1 - Construction of the interest prediction models for nursery school child using a single-channel electroencephalograph
AU - Kanoga, Suguru
AU - Mitsukura, Yasue
PY - 2013/1/1
Y1 - 2013/1/1
N2 - This paper aims to construct the interest prediction models for nursery school child using a single-channel electroencephalograph (EEG). Recently, the number of dual income households who leave their children in nursery schools have been increasing in Japan. Such parents are not able to grasp their children's behavior in daily life. Considering these issues, the researches related to child behavioral analysis have been proceeded by using image data taken from digital cameras. However, it is difficult to acquire the behavioral information from the digital cameras at anytime, anywhere. Therefore, we are focusing on wearable systems for keeping an eye on a child. Specifically, we adopt the EEG to design the constructing system. In this paper, we acquire single-channel EEG recordings from nursery school children when they watch picture-story shows. Furthermore, we apply a non-negative matrix factorization (NMF) to artifactitious rejection and a genetic algorithm-partial least squares (GA-PLS) regression to detect important frequency components and design the interest prediction models for the child using a single-channel EEG. As a result, we showed that over 60% estimation accuracy could be obtained all except one subject and the specific combinations of the frequency components selected by the GA-PLS, and we also could confirm that the NMF could remove the eye blink artifacts.
AB - This paper aims to construct the interest prediction models for nursery school child using a single-channel electroencephalograph (EEG). Recently, the number of dual income households who leave their children in nursery schools have been increasing in Japan. Such parents are not able to grasp their children's behavior in daily life. Considering these issues, the researches related to child behavioral analysis have been proceeded by using image data taken from digital cameras. However, it is difficult to acquire the behavioral information from the digital cameras at anytime, anywhere. Therefore, we are focusing on wearable systems for keeping an eye on a child. Specifically, we adopt the EEG to design the constructing system. In this paper, we acquire single-channel EEG recordings from nursery school children when they watch picture-story shows. Furthermore, we apply a non-negative matrix factorization (NMF) to artifactitious rejection and a genetic algorithm-partial least squares (GA-PLS) regression to detect important frequency components and design the interest prediction models for the child using a single-channel EEG. As a result, we showed that over 60% estimation accuracy could be obtained all except one subject and the specific combinations of the frequency components selected by the GA-PLS, and we also could confirm that the NMF could remove the eye blink artifacts.
KW - electroencephalograph
KW - genetic algorithm-partial least squares
KW - interest
KW - non-negative matrix factorization
KW - nursery school children
UR - http://www.scopus.com/inward/record.url?scp=84897820348&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897820348&partnerID=8YFLogxK
U2 - 10.1109/SPC.2013.6735118
DO - 10.1109/SPC.2013.6735118
M3 - Conference contribution
AN - SCOPUS:84897820348
SN - 9781479922093
T3 - Proceedings - 2013 IEEE Conference on Systems, Process and Control, ICSPC 2013
SP - 129
EP - 134
BT - Proceedings - 2013 IEEE Conference on Systems, Process and Control, ICSPC 2013
PB - IEEE Computer Society
T2 - 2013 IEEE Conference on Systems, Process and Control, ICSPC 2013
Y2 - 13 December 2013 through 15 December 2013
ER -