Robust kalman filter based incremental activation detection for real-time fMRI

We propose a new activation detection method for rt-fMRI data based on robust kalman filter. The This method adds a variation to the update step in the extended kalman filter to fit the unexpected noise in the general linear model, then solving the variation using the convex optimization, finally and use the solved variation to modify the kalman filter . The method can be used in the Functional localization run to find the brain regions associate with the tasks in real time. In clinical application, The method can be used in the functional localization run to obtain the brain regions associate with the tasks in real time, the algorithm can be used in the functional location run especially for the subjects who could not keep peace during the experiments .

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