Abstract
EEG is characterized by unique and individual characteristics. Little research has been done to take into account the individual characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. Then there is the difference of importance between the analyzed frequency components of the EEG. We think that the importance difference shows the individual characteristics. In this paper, we propose a new EEG extraction method of characteristic vector by a latency structure model in individual characteristics (LSMIC). The LSMIC is the latency structure model, which has personal error as the individual characteristics, based on normal distribution. The realcoded genetic algorithms (RGA) are used for specifying the personal error that is unknown parameter. Moreover we propose an objective estimation method that plots the EEG characteristic vector on a visualization space. Finally, the performance of the proposed method is evaluated using a realistic simulation and applied to real EEG data. The result of our experiment shows the effectiveness of the proposed method.
| Original language | English |
|---|---|
| Pages (from-to) | 9-17 |
| Number of pages | 9 |
| Journal | Electronics and Communications in Japan |
| Volume | 92 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2009 Jan |
| Externally published | Yes |
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Keywords
- Electroencephalogram
- Iatency structure model
- Individual characteristics
- Real-coded genetic algorithm
- Visualization
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Networks and Communications
- Physics and Astronomy(all)
- Signal Processing
- Applied Mathematics
Cite this
Extraction of EEG characteristics while listening to music and its evaluation based on a latency structure model with individual characteristics. / Ito, Shin Ichi; Mitsukura, Yasue; Miyamura, Hiroko Nakamura; Saito, Takafumi; Fukumi, Minoru.
In: Electronics and Communications in Japan, Vol. 92, No. 1, 01.2009, p. 9-17.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Extraction of EEG characteristics while listening to music and its evaluation based on a latency structure model with individual characteristics
AU - Ito, Shin Ichi
AU - Mitsukura, Yasue
AU - Miyamura, Hiroko Nakamura
AU - Saito, Takafumi
AU - Fukumi, Minoru
PY - 2009/1
Y1 - 2009/1
N2 - EEG is characterized by unique and individual characteristics. Little research has been done to take into account the individual characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. Then there is the difference of importance between the analyzed frequency components of the EEG. We think that the importance difference shows the individual characteristics. In this paper, we propose a new EEG extraction method of characteristic vector by a latency structure model in individual characteristics (LSMIC). The LSMIC is the latency structure model, which has personal error as the individual characteristics, based on normal distribution. The realcoded genetic algorithms (RGA) are used for specifying the personal error that is unknown parameter. Moreover we propose an objective estimation method that plots the EEG characteristic vector on a visualization space. Finally, the performance of the proposed method is evaluated using a realistic simulation and applied to real EEG data. The result of our experiment shows the effectiveness of the proposed method.
AB - EEG is characterized by unique and individual characteristics. Little research has been done to take into account the individual characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. Then there is the difference of importance between the analyzed frequency components of the EEG. We think that the importance difference shows the individual characteristics. In this paper, we propose a new EEG extraction method of characteristic vector by a latency structure model in individual characteristics (LSMIC). The LSMIC is the latency structure model, which has personal error as the individual characteristics, based on normal distribution. The realcoded genetic algorithms (RGA) are used for specifying the personal error that is unknown parameter. Moreover we propose an objective estimation method that plots the EEG characteristic vector on a visualization space. Finally, the performance of the proposed method is evaluated using a realistic simulation and applied to real EEG data. The result of our experiment shows the effectiveness of the proposed method.
KW - Electroencephalogram
KW - Iatency structure model
KW - Individual characteristics
KW - Real-coded genetic algorithm
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=65249099767&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=65249099767&partnerID=8YFLogxK
U2 - 10.1002/ecj.10009
DO - 10.1002/ecj.10009
M3 - Article
VL - 92
SP - 9
EP - 17
JO - Electronics and Communications in Japan
JF - Electronics and Communications in Japan
SN - 1942-9533
IS - 1
ER -