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

    An Improved P300 Extraction using ICA-R for P300-BCI Speller

    Access Status
    Fulltext not available
    Authors
    Lee, Wee Lih
    Tan, Tele
    Leung, Yee Hong
    Date
    2013
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Lih, Wee Lih and Tan, Tele and Leung, Yee Hong. 2013. An Improved P300 Extraction using ICA-R for P300-BCI Speller, in 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul 3-7 2013, pp. 7064-7067. Osaka, Japan: IEEE.
    Source Title
    Proceedings of the 35th Annual Internaitonal Conference of the IEEE Engineering in Medicine and Biology Society
    Source Conference
    35th Annual International conference of the IEEE Engineering in Medicine and Biology Society
    DOI
    10.1109/EMBC.2013.6611185
    URI
    http://hdl.handle.net/20.500.11937/35888
    Collection
    • Curtin Research Publications
    Abstract

    In this study, a new P300 extraction method is investigated by using a form of constrained independent component analysis (cICA) algorithm called one-unit ICA-with-reference (ICA-R) which extracts the P300 signal based on its temporal information. The main advantage of this method compared to the existing ICA-based method is that the desired P300 signal is extracted directly without requiring partial or full signal decomposition and any post-processing on the outcome of the ICA before the P300 signal can be obtained. Since only one IC is extracted, the method is computationally more efficient for real-time P300 BCI applications. In our study, when tested on the BCI competition 2003 dataset IIb, the current state-of-the-art performance is maintained by using the one-unit ICA-R. Besides that, the ability of the method to visualize P300 signals at the single-trial level also suggests it has potential applications in other types of ERP studies.

    Related items

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

    • Single-trial event-related potential extraction through one-unit ICA-with-reference.
      Lee, Wee Lih; Tan, Tele; Falkmer, Torbjorn; Leung, Y. (2016)
      Objective: In recent years, ICA has been one of the more popular methods for extracting event-related potential (ERP) at the single-trial level. It is a blind source separation technique that allows the extraction of an ...
    • Vibration signal denoising for structural health monitoring by residual convolutional neural networks
      Fan, G.; Li, Jun ; Hao, Hong (2020)
      In vibration based structural health monitoring (SHM), measurement noise inevitably exists in the vibration data, which significantly influences the usability and quality of measured vibration signals for structural ...
    • Filtering methods to extract the tide height from Global Navigation Satellite Systems (GNSS) signals for Hydrographic applications
      Alsaaq, Faisal; Kuhn, Michael; El-Mowafy, Ahmed ; Kennedy, Paul (2016)
      Hydrographic surveys have traditionally relied on the availability of tide information for the reduction of sounding observations to a common (chart) datum usually related to a specific tide level. In most cases, tide ...
    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.