ドメイン適応により個人の少量脳波データを拡張する感情推定モデル

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2022/07/01

タイトル(英語): An Emotion Recognition Model Augmented a Small Amount of Individual EEG by Domain Adaptation

著者名: 古川　翔也（名古屋工業大学 大学院工学研究科 工学専攻 創造工学系プログラム），佐久間　拓人（名古屋工業大学 大学院工学研究科 工学専攻 情報工学系プログラム），加藤　昇平（名古屋工業大学 大学院工学研究科 工学専攻 情報工学系プログラム／名古屋工業大学 情報科学フロンティア研究院）

著者名(英語): Shoya Furukawa (CreativeEngineering Program, Deptartment of Engineering, Graduate School of Engineering, Nagoya Institute of Technology), Takuto Sakuma (Computer Science Program, Deptartment of Engineering, Graduate School of Engineering, Nagoya Institute of Technology), Shohei Kato (Computer Science Program, Deptartment of Engineering, Graduate School of Engineering, Nagoya Institute of Technology/Frontier Research Institute for Information Science, Nagoya Institute of Technology)

キーワード: 感情推定，脳波，ドメイン適応，ディープニューラルネットワーク　　emotion recognition，EEG，domain adaptation，deep neural networks

要約(英語): In recent years, making computers understand the emotions of users is necessary because emotions are an important factor in human communication. Among many methods of recognizing emotions, EEG is widely used because it has high temporal resolution and it is impossible to disguise intentionally. However, it is necessary to acquire new user data and construct the personal Emotion recognition model since EEG varies widely among individuals. The conventional methods lack practicality because it builds a different model for every new user data. Our method builds a model in a single training using new user data. To reduce the number of new user data for training and to relieve the burden of EEG measurement, we adapt existing user data. We absorb the individual differences in EEG between a new user and existing users by domain adaptation using a small number of new user data and construct a model by deep neural networks. From the experiments, we confirmed that the proposed method performs as well as the conventional methods, even though it is built with single training using new user data.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.142 No.7 （2022） 特集：2021年電子・情報・システム部門大会

本誌掲載ページ: 771-780 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/142/7/142_771/_article/-char/ja/