TY - GEN
T1 - Robust multiple external stimuli classification in functional MRI images
AU - Zhao, Fei
AU - Man, Hong
AU - Comaniciu, Cristina
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/15
Y1 - 2016/6/15
N2 - Functional magnetic resonance imaging (fMRI) is an effective imaging modality in analyzing neural activation of human brain to external stimuli. The general analysis process includes feature selection and classification. Feature selection can be limited to specific anatomical regions or be implemented by using univariate or multi-variate statistical methods. However, both of these introduce some limitations for decoding different brain states. This paper introduces a new hybrid univariate and multivariate feature selection scheme, and explores different feature selection methods, i.e. orthogonal matching pursuit (OMP), smoothed Io (SL0) and principal feature analysis (PFA). The selected feature voxels are then classified by support vector machine (SVM) to produce predictions of perceived stimuli. Experimental results on Haxby single subject multiple stimuli dataset show the high effectiveness of this new multivoxel pattern analysis (MVPA) method.
AB - Functional magnetic resonance imaging (fMRI) is an effective imaging modality in analyzing neural activation of human brain to external stimuli. The general analysis process includes feature selection and classification. Feature selection can be limited to specific anatomical regions or be implemented by using univariate or multi-variate statistical methods. However, both of these introduce some limitations for decoding different brain states. This paper introduces a new hybrid univariate and multivariate feature selection scheme, and explores different feature selection methods, i.e. orthogonal matching pursuit (OMP), smoothed Io (SL0) and principal feature analysis (PFA). The selected feature voxels are then classified by support vector machine (SVM) to produce predictions of perceived stimuli. Experimental results on Haxby single subject multiple stimuli dataset show the high effectiveness of this new multivoxel pattern analysis (MVPA) method.
KW - fMRI
KW - feature selection
KW - sparse representation
UR - http://www.scopus.com/inward/record.url?scp=84978437472&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84978437472&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2016.7493215
DO - 10.1109/ISBI.2016.7493215
M3 - Conference contribution
AN - SCOPUS:84978437472
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 78
EP - 81
BT - 2016 IEEE International Symposium on Biomedical Imaging
T2 - 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
Y2 - 13 April 2016 through 16 April 2016
ER -