3 results for Peiris, M.T.R.

  • Investigation of lapses of consciousness using a tracking task: preliminary results

    Peiris, M.T.R.; Jones, R.D.; Carroll, G.J.; Bones, P.J. (2004)

    Conference Contributions - Published
    University of Canterbury Library

    In many high-risk occupations, it is critical that a person remains alert at all times. There is much to be gained by being able to monitor a person on-line and detect lapses of consciousness (LoC) so that remedial action can be taken (e.g., a rest break) to ensure that safety is maintained. In this study, 15 normal subjects were observed on two sessions while they performed a continuous tracking task for a period of 1 hour. EEG, eye movements, tracking performance data and a video of the subject were recorded during the session. This paper presents some preliminary results on the phenomenon of lapsing. Only 4 of the 15 subjects did not have a LoC at some stage. Seven subjects had LoCs more than 45 times and 4 more than 100 times during the 2 hours. The mean rate of lapsing over all subjects was 29.1 LoC/h. In contrast, lapses in performance were caused by both lapses of consciousness (30.1%) and attention (69.9%). There was no correlation found between age of subject and number of lapses of consciousness.

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  • Identification of vigilance lapses using EEG/EOG by expert human raters

    Peiris, M.T.R.; Jones, R.D.; Davidson, P.R.; Carroll, G.J.; Signal, T.L.; Parkin, P.J.; van den Berg, M.; Bones, P.J. (2005)

    Conference Contributions - Other
    University of Canterbury Library

    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.

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  • Fractal dimension of the EEG for detection of behavioural microsleeps

    Peiris, M.T.R.; Jones, R.D.; Davidson, P.R.; Bones, P.J.; Myall, D.J. (2005)

    Conference Contributions - Published
    University of Canterbury Library

    The fractal dimension (FD) of EEG has been shown to be of value in the detection of epileptic seizures. In this paper, we assess its usefulness in detecting behavioural microsleeps. Fifteen non-sleep-deprived normal subjects performed two 1-hour sessions of a continuous tracking task while EEG, EOG and facial video were recorded. Higuchi’s algorithm was used to calculate the FD of the EEG. Video lapses were scored independently from tracking performance by a human rater. A subset of data was rated independently by three human raters observing both tracking performance and the video rating to identify behavioural microsleep events. The mean point-biserial correlation between FD and the mean human rating was -0.213 indicating modest agreement. Crossvalidated detection performance of the FD was poor with a mean correlation (? = -0.099). This suggests that, on its own, FD of the EEG is unlikely to be useful for detecting microsleeps.

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