| Project/Area Number |
15500193
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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 | Institute of Statistical Mathematics |
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Principal Investigator |
OZAKI Tohru ISM, Dept.of Prediction and Control, Professor, 予測制御研究系, 教授 (00000208)
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| Co-Investigator(Kenkyū-buntansha) |
SADATOU Norihiro Okazaki Cooperative Research Institute of Physiology, Professor, 生理学研究所, 教授 (00273003)
ISHIGURO Makio ISM, Dept.of Prediction and Control, Professor, 予測制御研究系, 教授 (10000217)
TAKIZAWA Yumi ISM, Dept.of Prediction and Control, Professor, 予測制御研究系, 助教授 (90280528)
YAMASHITA Okito ISM, Dept.of Prediction and Control, Researcher, 予測制御研究系, 非常勤研究員
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| Project Period (FY) |
2003 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥3,600,000 (Direct Cost: ¥3,600,000)
Fiscal Year 2004: ¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2003: ¥1,900,000 (Direct Cost: ¥1,900,000)
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| Keywords | fMRI modeling / spatial time series model / hemodyanmics / innovation approach / EEG modeling / inverse problem / dynamic inverse problem / space-time Kalman filter |
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| Research Abstract |
1) Dynamic modeling of fMRI data and the connectivity analysis
We introduced a spatial time series model called NN-ARX model (Nearest Neighbor AutoRegressive model with eXogenous variables), for fMRI data.
Several advantage of the present model over SPM has been presented. Application of the method to the connectivity study was also shown using the fMRI experiment data of visual stimulus.
2) Dynamic EEG inverse solution
EEG inverse problem was reformulated as a state space modeling of multi-channel EEG time series data. Here the state space is a dynamic primary current of (huge dimensional) voxels. A computationally efficient spatial temporal Kalman filtering algorithm was presented. An inverse solution was introduced as a filtered estimate of the state obtained by the proposed spatial Kalman filtering algorithm.
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