書誌事項
- タイトル別名
-
- A Consideration for Electroencephalogram Analysis using Self-Organizing Map Based on Learning Algorithm for Plural-Attribue Information
- フクゾクセイ データ タイオウガタ ジコ ソシキカ マップ オ モチイタ ノウハ ブンセキ ニ カンスル イチ コウサツ
この論文をさがす
抄録
<p>This paper discusses a method to detect electroencephalogram (EEG) patterns using a self-organizing map (SOM) based on a learning algorithm for plural-attribute information (SOMPA). The input data for SOMPA has two attributes which are EEG feature and individual feature. We set the EEG feature to main feature and individual feature to sub-attribute information. The winning node in the learning algorithm of SOMPA is determined by using main feature and sub-attribute information. In the preprocessing, we extract the EEG feature vector by calculating the time average on each frequency band which are θ, α and β, respectively. The individual feature is analyzed though the ego analysis using psychological testing. In order to prove the effectiveness of the proposed method, we conduct experiments using real EEG data. The experimental results show that the EEG pattern detection accuracy using SOMPA improves compared with the standard SOM.</p>
収録刊行物
-
- 電気学会論文誌Ｃ（電子・情報・システム部門誌）
-
電気学会論文誌Ｃ（電子・情報・システム部門誌） 137 (2), 302-309, 2017
一般社団法人 電気学会
- Tweet
詳細情報 詳細情報について
-
- CRID
- 1390001204607067392
-
- NII論文ID
- 130005308465
-
- NII書誌ID
- AN10065950
-
- ISSN
- 13488155
- 03854221
-
- NDL書誌ID
- 027968083
-
- 本文言語コード
- ja
-
- データソース種別
-
- JaLC
- NDL
- Crossref
- CiNii Articles
-
- 抄録ライセンスフラグ
- 使用不可