title: Principal feature analysis: a multivariate feature selection method for fMRI data.
abstract: Brain decoding with functional magnetic resonance imaging (fMRI) requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA) has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise) or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.
authors.affiliation: China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, China.
authors.forename: Lijun
authors.initials: L
authors.lastname: Wang
authors.forename: Yu
authors.initials: Y
authors.lastname: Lei
authors.forename: Ying
authors.initials: Y
authors.lastname: Zeng
authors.forename: Li
authors.initials: L
authors.lastname: Tong
authors.forename: Bin
authors.initials: B
authors.lastname: Yan
mesh_terms: D001931:Brain Mapping / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data; D010775:Photic Stimulation; D060388:Support Vector Machine