EEG Characteristic Extraction Method of Listening Music and Objective Estimation Method Based on Latency Structure Model in Individual Characteristics
-
- Ito Shin-ichi
- Tokyo University of Agriculture and Technology
-
- Mitsukura Yasue
- Tokyo University of Agriculture and Technology
-
- Nakamura Miyamura Hiroko
- Tokyo University of Agriculture and Technology
-
- Saito Takafumi
- Tokyo University of Agriculture and Technology
-
- Fukumi Minoru
- The University of Tokushima
Bibliographic Information
- Other Title
-
- 個人特性を考慮した潜在構造モデルによる音楽聴取時の脳波特徴抽出法と客観的評価方法
- コジン トクセイ オ コウリョシタ センザイ コウゾウ モデル ニ ヨル オンガク チョウシュジ ノ ノウハ トクチョウ チュウシュツホウ ト キャッカンテキ ヒョウカ ホウホウ
Search this article
Description
EEG is characterized by the 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 a real EEG data. The result of our experiment shows the effectiveness of the proposed method.
Journal
-
- IEEJ Transactions on Electronics, Information and Systems
-
IEEJ Transactions on Electronics, Information and Systems 127 (6), 874-881, 2007
The Institute of Electrical Engineers of Japan
- Tweet
Keywords
Details 詳細情報について
-
- CRID
- 1390001204606321152
-
- NII Article ID
- 130000090464
-
- NII Book ID
- AN10065950
-
- ISSN
- 13488155
- 03854221
-
- NDL BIB ID
- 8842820
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
- NDL
- Crossref
- CiNii Articles
-
- Abstract License Flag
- Disallowed