Decoding semi-constrained brain activity from FMRI using support vector machines and gaussian processes.

ABSTRACT: 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

SUBMITTER: Schrouff J 

PROVIDER: S-EPMC3338538 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature