title: Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data.
abstract: BACKGROUND
  Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming.
  
  
  RESULTS
  Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time.
  
  
  CONCLUSIONS
  The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
authors.forename: Sutao
authors.initials: S
authors.lastname: Song
authors.forename: Zhichao
authors.initials: Z
authors.lastname: Zhan
authors.forename: Zhiying
authors.initials: Z
authors.lastname: Long
authors.forename: Jiacai
authors.initials: J
authors.lastname: Zhang
authors.forename: Li
authors.initials: L
authors.lastname: Yao
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000145:classification / Q000379:methods / Q000706:statistics & numerical data; D019295:Computational Biology / Q000145:classification / Q000379:methods / Q000706:statistics & numerical data; D001330:Electronic Data Processing / Q000145:classification / Q000379:methods*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000145:classification / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software / Q000145:classification