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
Link To Document :
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