Abstract
We propose methods 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 gradient method 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.
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Zhou, J., Rajapakse, J.C. (2006). Extraction of Fuzzy Features for Detecting Brain Activation from Functional MR Time-Series. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_108
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DOI: https://doi.org/10.1007/11893295_108
Publisher Name: Springer, Berlin, Heidelberg
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