Model-free optimal de-drifting and enhanced detection in fMRI data
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Shah, Adnan; Seghouane, Abd-Krim
Description
Discriminating between active and non-active brain voxels in noisy functional magnetic resonance imaging (fMRI) data plays an important role when investigating task-related activations of the neuronal sites. A novel method for efficiently capturing drifts
Collections | ANU Research Publications |
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Date published: | 2013 |
Type: | Conference paper |
URI: | http://hdl.handle.net/1885/82657 |
Source: | IEEE International Workshop on Machine Learning for Signal Processing, MLSP |
DOI: | 10.1109/MLSP.2013.6661963 |
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File | Description | Size | Format | Image |
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01_Shah_Model-free_optimal_de-drifting_2013.pdf | 743.13 kB | Adobe PDF | Request a copy |
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