Reference : Decoding Directed Brain Activity in fMRI using Support Vector Machines and Gaussian P...
Scientific congresses and symposiums : Poster
Human health sciences : Multidisciplinary, general & others
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/2268/138409
Decoding Directed Brain Activity in fMRI using Support Vector Machines and Gaussian Processes
English
Schrouff, Jessica [Université de Liège - ULiège > > Centre de recherches du cyclotron >]
Kussé, Caroline [Université de Liège - ULiège > > Centre de recherches du cyclotron >]
Wehenkel, Louis [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Maquet, Pierre [Université de Liège - ULiège > > Centre de recherches du cyclotron >]
Phillips, Christophe [Université de Liège - ULiège > > Centre de recherches du cyclotron >]
26-Jun-2011
Yes
International
Organization of Human Brain Mapping
from 26-06-2011 to 30-06-2011
Québec
Canada
[en] fMRI ; machine learning ; SVM
[en] Predicting a particular cognitive state from a specific pattern of fMRI voxel values is still a methodological challenge. Decoding brain activity is usually performed in highly controlled experimental paradigms characterized by a series of distinct states induced by a temporally constrained experimental design. In more realistic conditions, the number, sequence and duration of mental states are unpredictably generated by the individual, resulting in complex and imbalanced fMRI data sets. This study tests the classification of brain activity, acquired on 16 volunteers using fMRI, during mental imagery, a condition in which the number and duration of mental events were not externally imposed but self-generated. To deal with these issues, two classification techniques were considered (Support Vector Machines, SVM, and Gaussian Processes, GP), as well as different feature extraction methods (General Linear Model, GLM and SVM). These techniques were combined in order to identify the procedures leading to the highest accuracy measures. Our results showed that 12 data sets out of 16 could be significantly modeled by either SVM or GP. Model accuracies tended to be related to the degree of imbalance between classes and to task performance of the volunteers. We also conclude that the GP technique tends to be more robust than SVM to model unbalanced data sets.
Centre de Recherches du Cyclotron - CRC
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS ; Bial Fundation ; University of Liège
Researchers ; Professionals
http://hdl.handle.net/2268/138409
http://www.humanbrainmapping.org/i4a/pages/index.cfm?pageID=3420

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