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ATR
Neural Information Analysis Laboratories
Department of Computational Brain Imaging |
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Applications of Bayesian inference to noninvasive brain imaging data such as fMRI, MEG, EEG and
NIRS.
Main applications are
- Diffusion optical tomography (2010- )
- MEG/EEG Source Localization Problem (2001-)
- Brain Computer Interface (2007-)
- fMRI decoding. (2005-)
Main tools in statistics
are
- Time Series Analysis (AR model., Hidden Markov model, State Space
model)
- Sparse Bayesian Learning (Automatic Relevance Determination Priors, Variational
Bayesian algorithm).
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ADRESS : 2-2-2,
Hikaridai, Seikacho, Sorakugun, Kyoto 619-0288, Japan
TELL : +81-774-95-1073
FAX : +81-774-95-1259
E-mail : oyamashi(at)atr.jp
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2013 April -
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ATR, Neural Information Analysis Laboratories, Deparment head
- Brain network dynamics estimation with the multimodal integration approach
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2010 April - 2013 March
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ATR, Neural Information Analysis Laboratories, Senior Researcher
- Cross-freuqency coupling of ECoG data (data analysis, method proposal)
- Diffusion optical tomography (experimental validation, data analysis, toolbox)
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2004 Semptember - 2009 March
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ATR, Computational Neuroscience Laboratories, Researcher
- Source localization method with MEG/EEG-fMRI mutlimodal integration (method extension, toolbox)
- EEG/NIRS combined brain machine interface (data analysis)
- Sparse classification for fMRI decoding and brain machine interface (method proposal, toolbox)
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2004 April-August | Institute of statistical mathematics, Part-time researcher
- dynamic source localization extended to non-stationary case (method proposal)
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Ph.D. in
Statitics, 2004 |
The Graduate University for
Advanced Studies, Department of Statistical Science
- Application of the statistical timeseries analysis to neuroimaging data
- Effective connectivity estimation of fMRI (Granger causality analysis)
- Dynamic source localization of EEG (High dimensional Kalman filter)
Title of Ph.D. thesis : Dynamical Inverse
Problem and Causality Analysis of fMRI Data [PDF file]
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M.A. in
Engineering, 2001 |
The University of Tokyo,
Faculty of Engineering, Department of Mathematical Engineering and
Information Physics
- Parameter estimation problem of non-linear AR model
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B.A. in
Engineering, 1999 |
The University of Tokyo,
Faculty of Engineering, Department of Mathematical Engineering and
Information Physics
- Observation noise reduction of chaotic dynamical model
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