title: Multi-subject brain decoding with multi-task feature selection.
abstract: In the neural science society, multi-subject brain decoding is of great interest. However, due to the variability of activation patterns across brains, it is difficult to build an effective decoder using fMRI samples pooled from different subjects. In this paper, a hierarchical model is proposed to extract robust features for decoding. With feature selection for each subject treated as a separate task, a novel multi-task feature selection method is introduced. This method utilizes both complementary information among subjects and local correlation between brain areas within a subject. Finally, using fMRI samples pooled from all subjects, a linear support vector machine (SVM) classifier is trained to predict 2-D stimuli-related images or 3-D stimuli-related images. The experimental results demonstrated the effectiveness of the proposed method.
authors.affiliation: School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Liye
authors.initials: L
authors.lastname: Wang
authors.affiliation: School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Xiaoying
authors.initials: X
authors.lastname: Tang
authors.affiliation: School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Weifeng
authors.initials: W
authors.lastname: Liu
authors.affiliation: School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Yuhua
authors.initials: Y
authors.lastname: Peng
authors.affiliation: School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Tianxin
authors.initials: T
authors.lastname: Gao
authors.affiliation: Chinese PLA General Hospital (301 Hospital), Beijing 100853, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009415:Nerve Net / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D013647:Task Performance and Analysis
keywords: Multi-subject brain decoding; classification; fMRI; hierarchical model; multi-task feature selection