title: Identification of vigilance lapses using EEG/EOG by expert human raters.
abstract: It is critically important for certain occupational groups to remain highly alert throughout their working day. For safety reasons, it would be useful to automatically detect lapses in performance using EEG/EOG. Automating the detection process could be simplified considerably if we could mimic human experts. Surprisingly, it is unclear to what extent human EEG raters are able to detect lapses. Consequently, we undertook a study in which 4 expert EEG raters assessed the level of alertness of 10 air traffic controllers by observing a combination of their EEG and EOG while they performed a 10 min psychomotor vigilance task (PVT). They were specifically required to identify lapses or sleep episodes that might lead to a lapse in PVT performance. A reaction time .. 500 ms was defined as a PVT lapse. There was a total of 101 lapses (mean duration = 1.00 s). Of these, only 6 lapses were detected by one or more raters and all of these were marked as ;sleep'. Overall the human expert raters were unable to reliably identify lapses based only on EEG and EOG. This poor performance suggests an automated system would need to identify subtle features not overtly visible in the EEG.
authors.affiliation: Van der Veer Institute for Parkinson's and Brain Research, Christchurch, New Zealand.
authors.forename: M T
authors.initials: MT
authors.lastname: Peiris
authors.forename: R D
authors.initials: RD
authors.lastname: Jones
authors.forename: P R
authors.initials: PR
authors.lastname: Davidson
authors.forename: G J
authors.initials: GJ
authors.lastname: Carroll
authors.forename: T L
authors.initials: TL
authors.lastname: Signal
authors.forename: P J
authors.initials: PJ
authors.lastname: Parkin
authors.forename: M
authors.initials: M
authors.lastname: van den Berg
authors.forename: P J
authors.initials: PJ
authors.lastname: Bones