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    THE EFFECTS OF LOSSY EEG COMPRESSION ON ERP ANALYSIS

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    Author
    Phillips, Andrew J.
    Creusere, Charles D.
    Affiliation
    New Mexico State University, Klipsch School of Electrical & Computer Engineering
    Issue Date
    2018-11
    
    Metadata
    Show full item record
    Publisher
    International Foundation for Telemetering
    Journal
    International Telemetering Conference Proceedings
    URI
    http://hdl.handle.net/10150/631654
    Additional Links
    http://www.telemetry.org/
    Abstract
    This paper analyzes lossy data compression in the specific context of event-related potential (ERP) analysis of electroencephalography (EEG) data. The lossy data compression techniques analyzed here are bit-rate quantization and frequency truncation using the discrete cosine transform (DCT). Within the context of both methods it is demonstrated that ERP analysis waveforms yield significant data compression advantages over raw EEG data. It is found from the experimental results that for any given quantization error bound, utilization of ERP analysis requires approximately 3 fewer bits per EEG sample than normalized EEG data. Additionally, given any error bound for frequency truncation, at least 30% more total DCT coefficients can be discarded when utilizing ERP analysis instead of raw EEG data. The results hold significant implications for large-scale medical applications that rely on ERP analysis of EEG data.
    Language
    en_US
    ISSN
    0884-5123
    0074-9079
    Sponsors
    International Foundation for Telemetering
    Collections
    International Telemetering Conference Proceedings, Volume 54 (2018)

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