| Project/Area Number |
16K18052
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Intelligent mechanics/Mechanical systems
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| Research Institution | Okayama University |
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Principal Investigator |
Wu Qiong 岡山大学, ヘルスシステム統合科学研究科, 助教 (40762935)
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| Project Period (FY) |
2016-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | 選択意図 / 空間注意 / 眼球運動 / 意図推定 / BMIシステム / 深層学習 / リアルタイム / 選択意図推定 / Posner task / Event-related Potential / fMRI / spatial attention / Eye Tracker / Covert attention / Overt attention / Eye movements / Gaze Control / Microsaccade / Fronto-parietal network / Eye frontal field / Visual cortex / 視線 / Brain-Machine Interface |
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| Outline of Final Research Achievements |
The true purpose of human beings can be measured by intention estimation method, which is an important way for investigating the neural mechanism as well as its human-machine interaction theory. Although the research of intention estimation and human-machine interaction which based on explicit factors such as physical representation have achieved great results, the neural mechanism of intention estimation and its human-machine interaction theory verification are not clear at all. In this project, we plan to using eye movement, ERP and fMRI method, extracting the feature signal of eye movement and spatial attention, investigating the neural mechanism of intention estimation and its human-machine interaction theory verification. This project is the first time combines spatial attention and eye movement to estimate the human intention. More importantly, this project will shed new light on the human-machine interaction study which based on intention estimation.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は代表者ら以前の研究成果に基づいて、選択意図を視線と空間注意に関する脳波信号から推定して、その結果をBMI技術への応用ができれば、将来に会話と行動ができない身体障碍者の在室看護、在宅看護などの医療看護ロボットや、思いが届きやすい人間－ロボットの会話システムなどの研究開発への支援に与える可能性がある。さらに、本研究での空間注意、視線制御および選択意図の相互関係に関する脳内メカニズムを解明するが、この成果は、意思決定や行動計画実行等の高次脳機能解明研究にも貢献ができる。
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