Slow eye movement (SEM) are a sensitive indicator of lowered consciousness or drowsiness in man. A new computerized method for detection of SEM was introduced. A linear regression analysis was applied in each moving window for approximation to the tangent line on the electro-oculogram curve. The results revealed that SEM were more frequent and their duration was shorter at stage wake than at sleep stages 1 and 2. The method was practically suitable for objective detection and measurement of SEM.

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Electroencephalographic (EEG) alpha activity at the wake‐sleep transition was studied in six right‐handed adults in terms of hemispheric asymmetry and regional differences. Twelve‐channel EEGs with linked mastoid references were recorded together with horizontal and vertical electro‐oculograms (EOGs). Two types of alpha coefficient were obtained every 5.12 s by computing the relative proportion of right vs. left alpha band power and of anterior vs. posterior alpha band power. Four stages were scored using EEG sleep patterns (theta waves, vertex sharp waves, spindles, and K complex) and slow eye movement (SEM): stage W had neither EEG sleep patterns nor SEM; stage D1 had SEM and no EEG sleep patterns; stage D2 had theta waves or vertex sharp waves and SEM; stage S had spindles or K complex without SEM. It was found that the two types of alpha coefficient changed as a function of EEG‐EOG stage and were correlated. Right‐decreased and anterior‐shifted alpha activities were manifest at stages D2 and S. Drowsiness was considered to be a heterogeneous state, exhibiting different spatial changes in alpha activity between stages D1 and D2.

Eye movements during closed eyes closely reflect changes of the arousal level during transition from wakefulness to sleep. Because they contain both rapid and slow eye movements (REM and SEM), it has been difficult to detect them automatically. Hiroshige recently developed the method of linear regression analysis for automatic detection of the two types of eye movements, and we have developed a template matching method for autodetection. The aim of the present study was to compare both auto-detection methods and visual scoring for REM and SEM. The results revealed high agreement between the two quantitative methods and the visual scoring, indicating that auto-detection of eye movements is useful for quantitative evaluation of arousal level.