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- KUROOKA TAKETOSHI
- Graduate School of Information Science, Nara Institute of Science and Technology
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- YAMAKAWA MASASHI
- Graduate School of Information Science, Nara Institute of Science and Technology
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- YAMASHITA YUH
- Graduate School of Information Science, Nara Institute of Science and Technology
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- NISHITANI HIROKAZU
- Graduate School of Information Science, Nara Institute of Science and Technology
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抄録
We have been studying the use of multiple channel electroencephalogram (EEG) data to infer a human's thinking state. As a result, we have confirmed off-line thinking state estimation to be effective, in experimental studies on simulator training during malfunctions and mathematics problem solving. In this research, we developed a real-time system that monitors a human's thinking state on the basis of off-line results. First, an artificial neural network (ANN) model and a linear regression model were compared to determine which was more appropriate for real-time use. The ANN model was adopted because of its ease of handling and higher accuracy in thinking state estimation. Then, a prototype real-time thinking state monitoring (RTSM) system with the ANN model was developed and its effectiveness was evaluated experimentally via mathematics problem solving. Finally, we discuss a conception of plant operations with RTSM.
収録刊行物
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- JOURNAL OF CHEMICAL ENGINEERING OF JAPAN
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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 34 (11), 1387-1395, 2001
公益社団法人 化学工学会
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詳細情報 詳細情報について
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- CRID
- 1390282679541234688
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- NII論文ID
- 130000020261
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- NII書誌ID
- AA00709658
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- COI
- 1:CAS:528:DC%2BD3MXovFKqsb8%3D
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- ISSN
- 18811299
- 00219592
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- NDL書誌ID
- 5992930
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
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- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可