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