Extraction of EEG characteristics while listening to music and its evaluation based on a latency structure model with individual characteristics

• Shin‐Ichi Ito

• Yasue Mitsukura

• Hiroko Nakamura Miyamura

• Takafumi Saito

• Minoru Fukumi

抄録

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 real‐coded 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. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(1): 9–17, 2009; Published online in Wiley InterScience (<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://www.interscience.wiley.com">www.interscience.wiley.com ). DOI 10.1002/ecj.10009

収録刊行物

• Electronics and Communications in Japan

  Electronics and Communications in Japan 92 (1), 9-17, 2009-01

  Wiley

キーワード

• Applied Mathematics
• Electrical and Electronic Engineering
• Computer Networks and Communications
• General Physics and Astronomy
• Signal Processing

詳細情報

• CRID: 1362541648435272960
• NII論文ID: 210000185712
• DOI: 10.1002/ecj.10009
• ISSN: 19429541
• ISSN: 19429533
• Web Site: https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fecj.10009
• Web Site: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecj.10009
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