脳波の個人特性推定モデルとその評価方法に関する一考察

DOI Web Site Web Site 被引用文献1件 参考文献13件

書誌事項

タイトル別名
  • A Model for Extracting Personal Features of an Electroencephalogram and Its Evaluation Method
  • ノウハ ノ コジン トクセイ スイテイ モデル ト ソノ ヒョウカ ホウホウ ニ カンスル イチ コウサツ

この論文をさがす

抄録

This paper introduces a model for extracting features of an electroencephalogram (EEG) and a method for evaluating the model. In general, it is known that an EEG contains personal features. However, extraction of these personal features has not been reported. The analyzed frequency components of an EEG can be classified as the components that contain significant number of features and the ones that do not contain any. From the viewpoint of these feature differences, we propose the model for extracting features of the EEG. The model assumes a latent structure and employs factor analysis by considering the model error as personal error. We consider the EEG feature as a first factor loading, which is calculated by eigenvalue decomposition. Furthermore, we use a k-nearest neighbor (kNN) algorithm for evaluating the proposed model and extracted EEG features. In general, the distance metric used is Euclidean distance. We believe that the distance metric used depends on the characteristic of the extracted EEG feature and on the subject. Therefore, depending on the subject, we use one of the three distance metrics: Euclidean distance, cosine distance, and correlation coefficient. Finally, in order to show the effectiveness of the proposed model, we perform a computer simulation using real EEG data.

収録刊行物

被引用文献 (1)*注記

もっと見る

参考文献 (13)*注記

もっと見る

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ