IEEE Transactions on Information Technology in Biomedicine
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We proposemethods to extract fuzzy features from fMR time-series in order to detect brain activation. Five discriminating features are automatically extracted from fMRI using a sequence of temporal-sliding-windows. A fuzzy model based on these features is first developed by gradientmethod training on a set of initial training data and then incrementally updated. The resulting fuzzy activation maps are then combined to provide a measure of strength of activation for each voxel in human brain; a two-way thresholding scheme is introduced to determine actual activated voxels. The method is tested on both synthetic and real fMRI datasets for functional activation detection, illustrating that it is less vulnerable to correlated noise and is able to adapt to different hemodynamic response functions across subjects through incremental learning.