NDLI: Fast voxel selection of fMRI data based on Smoothed 10 norm

Content Provider	IEEE Xplore Digital Library
Author	Chuncheng Zhang Zhengli Wang Sutao Song Xiaotong Wen Li Yao Zhiying Long
Copyright Year	2014
Description	Author affiliation: State Key Lab. of Cognitive Neurosci. & Learning, Beijing Normal Univ., Beijing, China (Chuncheng Zhang; Li Yao; Zhiying Long) \|\| Dept. of Psychol., Renmin Univ. of China, Beijing, China (Xiaotong Wen) \|\| Sch. of Syst. Sci., Beijing Normal Univ., Beijing, China (Zhengli Wang) \|\| Sch. of Educ. & Psychol., Jinan Univ., Jinan, China (Sutao Song)
Abstract	Feature selection (FS) plays an important role in improving the classification accuracy of multivariate classification techniques in the context of fMRI based decoding due to the “few samples and large features” of fMRI data. The multivariate FS methods are generally time-consuming although they displayed better performance than the univariate FS methods. In this study, we applied a fast sparse representation method based on Smoothed 10 (SLO) algorithm to select relevant features in fMRI data. The performance of Gaussian Naive Bayes (GNB) classifier using voxels selected by SLO and the univariate t-test methods were also compared. Results of both simulated and real fMRI experiments demonstrated that the SLO method largely improved the classification accuracy of GNB compared to the t-test method for all the noise levels.
Starting Page	1
Ending Page	4
File Size	323231
Page Count	4
File Format	PDF
e-ISBN	9781479941490
DOI	10.1109/PRNI.2014.6858553
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-04
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain state decoding Accuracy Feature selection Imaging Gaussian processes Educational institutions Sparse representation Classification algorithms Iterative decoding FMRI Noise level
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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