NDLI: PCA-SIR: A new nonlinear supervised dimension reduction method with application to pain prediction from EEG

Content Provider	IEEE Xplore Digital Library
Author	Yiheng Tu Yeung Sam Hung Li Hu Zhiguo Zhang
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Pokfulam, China (Yiheng Tu; Yeung Sam Hung) \|\| Sch. of Psychol., Southwest Univ., Chongqing, China (Li Hu) \|\| Sch. of Chem. & Biomed. Eng., Nanyang Technol. Univ., Singapore, Singapore (Zhiguo Zhang)
Abstract	Dimension reduction is critical in identifying a small set of discriminative features that are predictive of behavior or cognition from high-dimensional neuroimaging data, such as EEG and fMRI. In the present study, we proposed a novel nonlinear supervised dimension reduction technique, named PCA-SIR (Principal Component Analysis and Sliced Inverse Regression), for analyzing high-dimensional EEG time-course data. Compared with conventional dimension reduction methods used for EEG, such as PCA and partial least-squares (PLS), the PCA-SIR method can make use of nonlinear relationship between class labels (i.e., behavioral or cognitive parameters) and predictors (i.e., EEG samples) to achieve the effective dimension reduction (e.d.r.) directions. We applied the new PCA-SIR method to predict the subjective pain perception (at a level ranging from 0 to 10) from single-trial laser-evoked EEG time courses. Experimental results on 96 subjects showed that reduced features by PCA-SIR can lead to significantly higher prediction accuracy than those by PCA and PLS. Therefore, PCA-SIR could be a promising supervised dimension reduction technique for multivariate pattern analysis of high-dimensional neuroimaging data.
Starting Page	1004
Ending Page	1007
File Size	692746
Page Count	4
File Format	PDF
ISBN	9781467363891
DOI	10.1109/NER.2015.7146796
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neuroimaging Pain Brain modeling Feature extraction Electroencephalography Data models Principal component analysis
Content Type	Text
Resource Type	Article

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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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