Emre Aksan

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Functional Magnetic Resonance Imaging (fMRI) data consists of time series for each voxel recorded during a cog-nitive task. In order to extract useful information from this noisy and redundant data, techniques are proposed to select the voxels that are relevant to the underlying cog-nitive task. We propose a simple and efficient algorithm for decoding the(More)
—An information theoretic approach is proposed to estimate the degree of connectivity for each voxel with its neighboring voxels. The neighborhood system is defined by spatial and functional connectivity metrics. Then, a local mesh of variable size is formed around each voxel using spatial or functional neighborhood. The mesh arc weights, called Mesh Arc(More)
Functional magnetic resonance imaging produces high dimensional data, with a less then ideal number of labelled samples for brain decoding tasks (predicting brain states). In this study, we propose a new deep temporal convolutional neu-ral network architecture with spatial pooling for brain decoding which aims to reduce dimensionality of feature space along(More)
In this study, we propose a new approach to construct a two-level functional brain network. The nodes of the first-level network are the voxels of the functional Magnetic Resonance Images (fMRI) recorded during an object recognition task. The nodes of the network at the second-level are the anatomic regions of the brain. The arcs of the first level are(More)
—In this study, the degree of connectivity for each voxel, which is the unit element of functional Magnetic Resonance Imaging (fMRI) data, with its neighboring voxels is estimated. The neighborhood system is defined by spatial connectivity metrics and a local mesh of variable size is formed around each voxel using spatial neighborhood. Then, the mesh arc(More)
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