TY - GEN
T1 - Detecting method of music to match the user's mood in prefrontal cortex EEG activity using the GA
AU - Ito, Shin Ichi
AU - Mitsukura, Yasue
AU - Fukumi, Minoru
AU - Cao, Jianting
PY - 2007/12/1
Y1 - 2007/12/1
N2 - In this paper, we propose a method for detecting the mood much music for prefrontal cortex electroencephalogram (EEG) activity. The analyzed EEG frequencies contain significant and immaterial information components. We focused on the combinations of the significant frequency. These frequency combinations are thought to express personal features of EEG activity. In the proposed method, we calculate the spectrum of these frequency combinations rates that does not include the noise frequency components and evaluates whether the music matches the user's mood through a simple threshold processing. Then, a genetic algorithm (GA) is used to specify the frequency of personal features on the EEG. The threshold vale used the threshold processing is the average value of the spectrum rates specified EEG frequency combinations. Finally, the performance of the proposed method is evaluated using real EEG data.
AB - In this paper, we propose a method for detecting the mood much music for prefrontal cortex electroencephalogram (EEG) activity. The analyzed EEG frequencies contain significant and immaterial information components. We focused on the combinations of the significant frequency. These frequency combinations are thought to express personal features of EEG activity. In the proposed method, we calculate the spectrum of these frequency combinations rates that does not include the noise frequency components and evaluates whether the music matches the user's mood through a simple threshold processing. Then, a genetic algorithm (GA) is used to specify the frequency of personal features on the EEG. The threshold vale used the threshold processing is the average value of the spectrum rates specified EEG frequency combinations. Finally, the performance of the proposed method is evaluated using real EEG data.
KW - Electroencephalogram
KW - Fast fourier transform
KW - Genetic algorithm
KW - User's mood
UR - http://www.scopus.com/inward/record.url?scp=48349127925&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=48349127925&partnerID=8YFLogxK
U2 - 10.1109/ICCAS.2007.4406685
DO - 10.1109/ICCAS.2007.4406685
M3 - Conference contribution
AN - SCOPUS:48349127925
SN - 8995003871
SN - 9788995003879
T3 - ICCAS 2007 - International Conference on Control, Automation and Systems
SP - 2142
EP - 2145
BT - ICCAS 2007 - International Conference on Control, Automation and Systems
T2 - International Conference on Control, Automation and Systems, ICCAS 2007
Y2 - 17 October 2007 through 20 October 2007
ER -