NDLI: A robust principal component analysis algorithm for EEG-based vigilance estimation

Content Provider	IEEE Xplore Digital Library
Author	Li-Chen Shi Ruo-Nan Duan Bao-Liang Lu
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., Shanghai, China (Li-Chen Shi; Ruo-Nan Duan; Bao-Liang Lu)
Abstract	Feature dimensionality reduction methods with robustness have a great significance for making better use of EEG data, since EEG features are usually high-dimensional and contain a lot of noise. In this paper, a robust principal component analysis (PCA) algorithm is introduced to reduce the dimension of EEG features for vigilance estimation. The performance is compared with that of standard PCA, L1-norm PCA, sparse PCA, and robust PCA in feature dimension reduction on an EEG data set of twenty-three subjects. To evaluate the performance of these algorithms, smoothed differential entropy features are used as the vigilance related EEG features. Experimental results demonstrate that the robustness and performance of robust PCA are better than other algorithms for both off-line and on-line vigilance estimation. The average RMSE (root mean square errors) of vigilance estimation was 0.158 when robust PCA was applied to reduce the dimensionality of features, while the average RMSE was 0.172 when standard PCA was used in the same task.
Starting Page	6623
Ending Page	6626
File Size	173724
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6611074
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Robustness Electroencephalography Estimation Noise Standards Algorithm design and analysis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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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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