dc.contributor.author |
Alkan, Sarper
|
|
dc.contributor.author |
Yarman-Vural, Fatos T.
|
|
dc.date.accessioned |
2020-04-19T23:51:22Z |
|
dc.date.available |
2020-04-19T23:51:22Z |
|
dc.date.issued |
2014 |
|
dc.identifier.citation |
Alkan, Sarper; Yarman-Vural, Fatos T., "Localization of Semantic Category Classification in FMRı Images", 22nd IEEE Signal Processing and Communications Applications Conference (SIU), pp. 2178-2181, (2014). |
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dc.identifier.issn |
2165-0608 |
|
dc.identifier.uri |
http://hdl.handle.net/20.500.12416/3371 |
|
dc.description.abstract |
In this study, we provide a methodology to localize the brain regions that contribute to semantic category classification. For this purpose we first cluster the data using spectral clustering. Then we extract local features within each cluster by using mesh-arc descriptors. Finally, we test the classification accuracy of each cluster against a hypothesis testing measure we provide here. We have found that, for the experimental task at hand, calcerine fissure and angular gyrus were most effective in classification. These results are shown to be match well with the nature of the experiment. Thus the validity of our approach is confirmed. |
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dc.language.iso |
eng |
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dc.publisher |
IEEE |
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dc.rights |
info:eu-repo/semantics/closedAccess |
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dc.subject |
Feature Extraction |
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dc.subject |
Feature Clustering |
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dc.subject |
Brain Decoding |
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dc.subject |
Classification |
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dc.subject |
Hypothesis Testing |
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dc.subject |
Multi Voxel Pattern Analysis (MVPA) |
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dc.subject |
Functional Magnetic Resonance Imaging (fMRI) |
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dc.title |
Localization of Semantic Category Classification in FMRı Images |
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dc.type |
conferenceObject |
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dc.relation.journal |
22nd IEEE Signal Processing and Communications Applications Conference (SIU) |
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dc.identifier.startpage |
2178 |
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dc.identifier.endpage |
2181 |
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dc.contributor.department |
Çankaya Üniversitesi, Mühendislik Fakültesi, Mekatronik Mühendisliği Bölümü |
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