Spatial time series modeling offMRI and EEG data

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1) Dynamic modeling of fMRI data and the connectivity analysisWe 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 solutionEEG 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.