Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Removal of Electrooculogram Artifacts from Electroencephalogram Using Canonical Correlation Analysis with Ensemble Empirical Mode Decomposition

    Access Status
    Fulltext not available
    Authors
    Yang, B.
    Zhang, T.
    Zhang, Y.
    Liu, Wan-Quan
    Wang, J.
    Duan, K.
    Date
    2017
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Yang, B. and Zhang, T. and Zhang, Y. and Liu, W. and Wang, J. and Duan, K. 2017. Removal of Electrooculogram Artifacts from Electroencephalogram Using Canonical Correlation Analysis with Ensemble Empirical Mode Decomposition. Cognitive Computation. 9 (5): pp. 626-633.
    Source Title
    Cognitive Computation
    DOI
    10.1007/s12559-017-9478-0
    ISSN
    1866-9956
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/63532
    Collection
    • Curtin Research Publications
    Abstract

    © 2017, Springer Science+Business Media New York. Electrooculogram (EOG) is one of the major artifacts in the design of electroencephalogram (EEG)-based brain computer interfaces (BCIs). That removing EOG artifacts automatically while retaining more neural data will benefit for further feature extraction and classification. In order to remove EOG artifacts automatically as well as reserve more useful information from raw EEG, this paper proposes a novel blind source separation method called CCA-EEMD (canonical correlation analysis, ensemble empirical mode decomposition). Technically, the major steps of CCA-EEMD are as follows: Firstly, the multiple-channel original EEG signals are separated into several uncorrelated components using CCA. Then, the EOG component can be identified automatically by its kurtosis value. Next, the identified EOG component is decomposed into several intrinsic mode functions (IMFs) by EEMD. The IMFs uncorrelated to the EOG component are recognized and retained, and a new component will be constructed by the retained IMFs. Finally, the clean EEG signals are reconstructed. Keep in mind that the novelty of this paper is that the identified EOG component is not removed directly but used to extract neural EEG data, which would keep more effective information. Our tests with the data of seven subjects demonstrate that the proposed method has distinct advantages over other two commonly used methods in terms of average root mean square error [37.71 ± 0.14 (CCA-EEMD), 44.72 ± 0.13 (CCA), 49.59 ± 0.16 (ICA)], signal-to-noise ratio [3.59 ± 0.24 (CCA-EEMD), -6.53 ± 0.18(CCA), -8.43 ± 0.26 (ICA)] , and classification accuracy [0.88 ± 0.002 (CCA-EEMD), 0.79 ± 0.001 (CCA), 0.73 ± 0.002 (ICA)]. The proposed method can not only remove EOG artifacts automatically but also keep the integrity of EEG data to the maximum extent.

    Related items

    Showing items related by title, author, creator and subject.

    • Discriminative ocular artifact correction for feature learning in EEG analysis
      Li, X.; Guan, Cuntai; Zhang, H.; Ang, K. (2017)
      © 2016 IEEE. Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain-computer interface (BCI) research. It is especially challenging when ...
    • A BIM-based framework for lift planning in topsides disassembly of offshore oil and gas platforms
      Tan, Y.; Song, Y.; Liu, Xin; Wang, X.; Cheng, J. (2016)
      © 2017 Elsevier B.V.Offshore oil and gas platforms (OOGPs) usually have a lifetime of 30-40. years. An increasing number of OOGPs across the world will be retired and decommissioned in the coming decade. Therefore, a safe ...
    • Using the EZ-diffusion model to score a single-category implicit association test of physical activity
      Rebar, Amanda; Ram, N.; Conroy, D. (2015)
      © 2014 Elsevier Ltd. Objective: The Single-Category Implicit Association Test (SC-IAT) has been used as a method for assessing automatic evaluations of physical activity, but measurement artifact or consciously-held ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.