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    EEG subspace analysis and classification using principal angles for brain-computer interfaces

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    http://hdl.handle.net/10217/167006
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    Ashari_colostate_0053A_12972.pdf (7.781Mb)
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    Abstract
    Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to reducing the effects of similar difficulties in other domains is the use of principal ...
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    Author(s)
    Ashari, Rehab Bahaaddin

    Advisor(s)
    Anderson, Charles W.

    Date Issued
    2015
    Format
    born digital; doctoral dissertations
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    • 2000-2019 - CSU Theses and Dissertations
    • Theses and Dissertations - Department of Computer Science

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