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Fully automated reduction of ocular artifacts in high-dimensional neural data.

Kelly, John W and Siewiorek, Daniel P and Smailagic, Asim and Collinger, Jennifer L and Weber, Douglas J and Wang, Wei (2011) Fully automated reduction of ocular artifacts in high-dimensional neural data. IEEE Trans Biomed Eng, 58 (3). 598 - 606.

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Abstract

The reduction of artifacts in neural data is a key element in improving analysis of brain recordings and the development of effective brain-computer interfaces. This complex problem becomes even more difficult as the number of channels in the neural recording is increased. Here, new techniques based on wavelet thresholding and independent component analysis (ICA) are developed for use in high-dimensional neural data. The wavelet technique uses a discrete wavelet transform with a Haar basis function to localize artifacts in both time and frequency before removing them with thresholding. Wavelet decomposition level is automatically selected based on the smoothness of artifactual wavelet approximation coefficients. The ICA method separates the signal into independent components, detects artifactual components by measuring the offset between the mean and median of each component, and then removing the correct number of components based on the aforementioned offset and the power of the reconstructed signal. A quantitative method for evaluating these techniques is also presented. Through this evaluation, the novel adaptation of wavelet thresholding is shown to produce superior reduction of ocular artifacts when compared to regression, principal component analysis, and ICA.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Kelly, John W
Siewiorek, Daniel P
Smailagic, Asim
Collinger, Jennifer Lcollinger@pitt.eduCOLLINGR
Weber, Douglas Jdougweber@pitt.eduDJW500000-0002-9782-3497
Wang, Weiwangwei3@pitt.eduWANGWEI3
Centers: Other Centers, Institutes, or Units > Human Engineering Research Laboratories
Date: March 2011
Date Type: Publication
Journal or Publication Title: IEEE Trans Biomed Eng
Volume: 58
Number: 3
Page Range: 598 - 606
DOI or Unique Handle: 10.1109/tbme.2010.2093932
Schools and Programs: Swanson School of Engineering > Bioengineering
School of Health and Rehabilitation Sciences > Rehabilitation Science and Technology
Refereed: Yes
Uncontrolled Keywords: Artifacts, Electrooculography, Humans, Magnetoencephalography, Man-Machine Systems, Principal Component Analysis, Regression Analysis, Wavelet Analysis
Funders: NIBIB NIH HHS (1R01EB007749), NINDS NIH HHS (1R21NS056136), NCRR NIH HHS (5UL1RR024153)
MeSH Headings: Artifacts; Electrooculography--methods; Humans; Magnetoencephalography; Man-Machine Systems; Principal Component Analysis; Regression Analysis; Wavelet Analysis
PubMed ID: 21097374
Date Deposited: 30 Jan 2013 20:57
Last Modified: 30 Oct 2018 14:02
URI: http://d-scholarship.pitt.edu/id/eprint/17146

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