Eda Mizrak

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We represent the sequence of fMRI (Functional Magnetic Resonance Imaging) brain volumes recorded during a cognitive stimulus by a graph which consists of a set of local meshes. The corresponding cognitive process, encoded in the brain, is then represented by these meshes each of which is estimated assuming a linear relationship among the voxel time series(More)
Functional Magnetic Resonance Imaging (fMRI) methods produce high dimensional representation of cognitive processes under heavy noise due to the limitations of hardware and measurement techniques. In order to reduce the noise and extract useful information from the fMRI data, a sequence of pre-processing techniques, such as smoothing with spatial filters(More)
Bag-of-words (BoW) modeling has yielded successful results in document and image classification tasks. In this paper, we explore the use of BoW for cognitive state classification. We estimate a set of common patterns embedded in the fMRI time series recorded in three dimensional voxel coordinates by clustering the BOLD responses. We use these common(More)
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