EFFICIENT LEARNING FROM MULTIPLE BRAIN IMAGING DATA SETS
Grant number: FT130101394 | Funding period: 2014 - 2019
Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in biomedical image analysis. This project will lead to fundamental contributions as well as techniques that address both problems: extraction of relevant features information from multisubject brain imaging data sets of the same modality or from fusion of brain i..View full description
Related publications (30)
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Xin Zhou, Abd-Krim Seghouane, Adnan Shah, Hamish Innes-Brown, Will Cross, Ruth Litovsky, Colette M McKay
An experiment was conducted to investigate the feasibility of using functional near-infrared spectroscopy (fNIRS) to image cortica..
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Sequential dictionary learning algorithms have been successfully applied to functional magnetic resonance imaging (fMRI) data anal..
Connectivity in Language Areas of the Brain in Cochlear Implant Users as Revealed by fNIRS
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Many studies, using a variety of imaging techniques, have shown that deafness induces functional plasticity in the brain of adults..