TY - GEN
T1 - Proposal for the extraction method of personal comfort and preference by the EEG maps
AU - Ota, Satomi
AU - Ito, Shin Ichi
AU - Mitsukura, Yasue
AU - Fukumi, Minora
PY - 2006/12/1
Y1 - 2006/12/1
N2 - Recently, many researches of the extraction of personal comfort and preference have been studied. Personal comfort and preference depend on a person, therefore the index is needed for the measurement. Then, we aim to understand personal comfort and preference by using the EEG as an index. The purpose of this study is to make the EEG map that makes visual the relationship between the EEG and external stimuli. We think that analyzing the distribution in the EEG map leads to the extraction of personal preference. First of all, we measure EEG patterns in 3 conditions; neutral which is the state relaxing without external stimuli, listening to favorite music, and smelling favorite fragrance. Then, we make the data matrix from the obtain data. Then, we extract features from the matrix by using neural networks. Finally, we make the EEG map to clarify the location of each EEG feature. In order to show the effectiveness of the proposed method, we demonstrate the simulation examples.
AB - Recently, many researches of the extraction of personal comfort and preference have been studied. Personal comfort and preference depend on a person, therefore the index is needed for the measurement. Then, we aim to understand personal comfort and preference by using the EEG as an index. The purpose of this study is to make the EEG map that makes visual the relationship between the EEG and external stimuli. We think that analyzing the distribution in the EEG map leads to the extraction of personal preference. First of all, we measure EEG patterns in 3 conditions; neutral which is the state relaxing without external stimuli, listening to favorite music, and smelling favorite fragrance. Then, we make the data matrix from the obtain data. Then, we extract features from the matrix by using neural networks. Finally, we make the EEG map to clarify the location of each EEG feature. In order to show the effectiveness of the proposed method, we demonstrate the simulation examples.
KW - EEG
KW - External stimuli
KW - Neural networks
UR - http://www.scopus.com/inward/record.url?scp=34250723854&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34250723854&partnerID=8YFLogxK
U2 - 10.1109/SICE.2006.315706
DO - 10.1109/SICE.2006.315706
M3 - Conference contribution
AN - SCOPUS:34250723854
SN - 8995003855
SN - 9788995003855
T3 - 2006 SICE-ICASE International Joint Conference
SP - 604
EP - 607
BT - 2006 SICE-ICASE International Joint Conference
T2 - 2006 SICE-ICASE International Joint Conference
Y2 - 18 October 2006 through 21 October 2006
ER -