Reference : Decoding semi-constrained brain activity from fMRI using SVM and GP
Scientific conferences in universities or research centers : Scientific conference in universities or research centers
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/2268/115915
Decoding semi-constrained brain activity from fMRI using SVM and GP
English
Schrouff, Jessica mailto [Université de Liège - ULg > > Centre de recherches du cyclotron >]
Kussé, Caroline mailto [Université de Liège - ULg > > Centre de recherches du cyclotron >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Maquet, Pierre mailto [Université de Liège - ULg > > Centre de recherches du cyclotron >]
Phillips, Christophe mailto [Université de Liège - ULg > > Centre de recherches du cyclotron >]
22-Nov-2011
International
I Workshop on Mathematical Modeling and Computational Neuroscience of UFABC
21-11-2011 to 22-11-2012
University of UFABC
São Paulo
Brazil
[en] brain decoding ; fMRI ; semi-constrained
[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 ; Fonds pour la formation à la Recherche dans l'Industrie et dans l'Agriculture (Communauté française de Belgique) - FRIA
Researchers
http://hdl.handle.net/2268/115915

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