title: Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes.
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.
authors.affiliation: QUASAR, San Diego, CA 92121 USA. robm@quasarusa.com
authors.forename: R
authors.initials: R
authors.lastname: Matthews
authors.forename: P J
authors.initials: PJ
authors.lastname: Turner
authors.forename: N J
authors.initials: NJ
authors.lastname: McDonald
authors.forename: K
authors.initials: K
authors.lastname: Ermolaev
authors.forename: T
authors.initials: T
authors.lastname: Manus
authors.forename: R A
authors.initials: RA
authors.lastname: Shelby
authors.forename: M
authors.initials: M
authors.lastname: Steindorf
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004566:Electrodes; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods; D004867:Equipment Design; D019544:Equipment Failure Analysis; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013686:Telemetry / Q000295:instrumentation*; D016526:Workload