1 Department of Informatics and Mathematical Modeling, Technical University of Denmark
Generalizability in a multi-subject fMRI study is investigated. The analysis is based on principal and independent component representations. Subsequent supervised learning and classification is carried out by canonical variates analysis and clustering methods. The generalization error is estimated by cross-validation, forming the so-called learning curves. The fMRI case story is a motor-control study, involving multiple applied static force levels. Despite the relative complexity of this case study, the classification of the 'stimulus' shows good generalizability, measured by the test set error rate. It is shown that independent component representation leads to improvement in the classification rate, and that canonical variates analysis is needed for making generalization cross multiple subjects.
Canonical Variates Analysis (CVA),| Principal Component Analysis (PCA); functional Magnetic Resonance Imaging (fMRI); Multiple Subjects; Independent Component Analysis (ICA)