| Project/Area Number |
13650475
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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 |
Measurement engineering
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| Research Institution | Institute of Statistical Mathematics |
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Principal Investigator |
OZAKI Tohru ISM, Dept. of Prediction and Control, Prof., 予測制御研究系, 教授 (00000208)
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| Co-Investigator(Kenkyū-buntansha) |
ISHIGURO Makio ISM, Dept. of Prediction and Control, Prof., 予測制御研究系, 教授 (10000217)
SADATOU Norihiro National Okazaki Cooperative Research, Institute of Physiology, Prof., 生理学研究所, 教授 (00273003)
TAKIZAWA Yumi ISM, Dept. of Prediction and Control, Asst. Prof., 予測制御研究系, 助教授 (90280528)
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| Project Period (FY) |
2001 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥3,600,000 (Direct Cost: ¥3,600,000)
Fiscal Year 2002: ¥1,200,000 (Direct Cost: ¥1,200,000)
Fiscal Year 2001: ¥2,400,000 (Direct Cost: ¥2,400,000)
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| Keywords | Hemodynamics / fMRI modeling / Spatial time series model / Innovation Approach / EEG modeling / Inverse Problem / Dynamic Inverse problem / Space-tine Kalman filter / 脳データ / 非線形 / fMR1 |
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| Research Abstract |
Although fMRI data is time-dependent data, it has been analyzed by spatial statisticians without proper attention to the dynamic part. This is because the data is also spatial data at the same time and the statistical analysis has been done by specialists of spatial statistics in the early stage of the development. In the present study, we take a reversal approach, where we treat the data as a 140,000 dimensional time series and tried to find some causal structure in the data using the innovation approach in time series analysis. We also applied the same idea to the solution of inverse problem for EEG data using spatial Kalman filtering scheme. This provides us with a useful tool for dynamic extension of Inverse Solution of ill-posed problems.
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