• DocumentCode
    3086792
  • Title

    Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes

  • Author

    Matthews, R. ; Turner, P.J. ; McDonald, N.J. ; Ermolaev, K. ; Manus, T.Mc ; Shelby, R.A. ; Steindorf, M.

  • Author_Institution
    QUASAR, San Diego, CA 92121 USA
  • fYear
    2008
  • fDate
    20-25 Aug. 2008
  • Firstpage
    5871
  • Lastpage
    5875
  • Abstract
    This paper describes a compact, lightweight and ultra-low power ambulatory wireless EEG system based upon QUASAR´s innovative noninvasive bioelectric sensor technologies. The sensors operate through hair without skin preparation or conductive gels. Mechanical isolation built into the harness permits the recording of high quality EEG data during ambulation. Advanced algorithms developed for this system permit real time classification of workload during subject motion. Measurements made using the EEG system during ambulation are presented, including results for real time classification of subject workload.
  • Keywords
    Bioelectric phenomena; Biosensors; Electrodes; Electroencephalography; Isolation technology; Mechanical sensors; Real time systems; Sensor phenomena and characterization; Sensor systems; Wireless sensor networks; Algorithms; Brain; Electrodes; Electroencephalography; Equipment Design; Equipment Failure Analysis; Monitoring, Ambulatory; Reproducibility of Results; Sensitivity and Specificity; Telemetry; Workload;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE
  • Conference_Location
    Vancouver, BC
  • ISSN
    1557-170X
  • Print_ISBN
    978-1-4244-1814-5
  • Electronic_ISBN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/IEMBS.2008.4650550
  • Filename
    4650550