Statistical Inverse Problem and its Applications to Complex Systems
| Project/Area Number |
14580346
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | Gifu University |
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Principal Investigator |
KISHIDA Kuniharu Gifu University, Faculty of Engineering, Professor, 工学部, 教授 (90115402)
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| Project Period (FY) |
2002 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥3,200,000 (Direct Cost: ¥3,200,000)
Fiscal Year 2004: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2003: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2002: ¥1,400,000 (Direct Cost: ¥1,400,000)
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| Keywords | magnetoencephalography / statistical inverse problems / identification of feedback system / independent component analysis / connection of brain functions / system dynamics / noises in nuclear power reactor / blind identification / 誘発磁場 / イノベーションモデル / 体性感覚野 / 正中神経繰り返し刺激 / 聴覚野 / 連関 / ゆらぎ解析 / フィードバックシステム / 体性感覚 |
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| Research Abstract |
In this research statistical inverse problem on complex systems such as brain functions or engineering plants is examined by using the identification method based on feedback system theory. Since the localization of brain activities was well understood in the last century, activities of associated regions in brain are interested for understanding of higher order brain functions, i.e., language, recognition, vision activities, in the present century.
At the beginning of research, it seemed to be difficult to attack such topics for time series data of magnetoencephalography (MEG), since there are many parallel processing of activities in brain. However, repeated electrical or sound stimuli give periodical activities in brain, and they are called by the evoked magnetic field. Then, we could separate particular evoked magnetic fields from background magnetic fields of brain by using the independent component analysis based on temporal structure. One example is the auditory evoked field, and the other is the somatosensory evoked field. After selection of evoked MEG we can examine dynamics included in MEG time series data by the identification method based on feedback system theory. That is, transfer functions between observable channels were identified, and their impulse responses were obtained. They give us dynamics between regions of brains. The Gaussianity in the framework of the method was important to obtain the stationarity of time series data, since it is observed in not only MEG data but also fluctuations of neutron number of nuclear power plants.
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Report
(4 results)
Research Products
(30 results)
