Two independent lines of evidence suggest that drowsiness and mind-wandering share common neurocognitive processes indexed by ocular parameters (e.g., eyeblink frequency and pupil dynamics). Mind-wandering and drowsiness frequently co-occur, however, such that it remains unclear whether observed oculometric variations are related to mind-wandering, drowsiness, or a mix of both. To address this issue, we assessed fluctuations in mind-wandering and sleepiness during a sustained attention task while ocular parameters were recorded. Results showed that oculometric variations during mind-wandering were fully explained by increased sleepiness. However, mind-wandering and sleepiness had additive deleterious effects on performance that were not fully explained by ocular parameters. These findings suggest that oculometric variations during task performance reflect increased drowsiness rather than processes specifically involved in mind-wandering, and that the neurocognitive processes indexed by oculometric parameters (e.g., regulatory processes of the locus coeruleus norepinephrine system) do not fully explain how mind-wandering and sleepiness cause attentional lapses.

Drowsiness is the intermediate state between wakefulness and sleep. It is characterized by impairments of performance, which can be very dangerous in many activities and can lead to catastrophic accidents in transportation or in industry. There is thus an obvious need for systems that are able to continuously, objectively, and automatically estimate the level of drowsiness of a person busy at a task. We have developed such a system, which is based on the physiological state of a person, and, more specifically, on the values of ocular parameters extracted from images of the eye (photooculography), and which produces a numerical level of drowsiness. In order to test our system, we compared the level of drowsiness determined by our system to two references: (1) the level of drowsiness obtained by analyzing polysomnographic signals; and (2) the performance of individuals in the accomplishment of a task. We carried out an experiment in which 24 participants were asked to perform several Psychomotor Vigilance Tests in different sleep conditions. The results show that the output of our system is well correlated with both references. We determined also the best drowsiness level threshold in order to warn individuals before they reach dangerous situations. Our system thus has significant potential for reliably quantifying the level of drowsiness of individuals accomplishing a task and, ultimately, for preventing drowsiness-related accidents.

Visual pursuit is a key marker of residual consciousness in patients with disorders of consciousness (DOC). Currently, its assessment relies on subjective clinical decisions. In this study, we explore the variability of such clinical assessments, and present an easy-to-use device composed of cameras and video processing algorithms that could help the clinician to improve the detection of visual pursuit in a clinical context. Visual pursuit was assessed by an experienced research neuropsychologist on 31 patients with DOC and on 23 healthy subjects, while the device was used to simultaneously record videos of both one eye and the mirror. These videos were then scored by three researchers: the experienced research neuropsychologist who did the clinical assessment, another experienced research neuropsychologist, and a neurologist. For each video, a consensus was decided between the three persons, and used as the gold standard of the presence or absence of visual pursuit. Almost 10% of the patients were misclassified at the bedside according to their consensus. An automatic classifier analyzed eye and mirror trajectories, and was able to identify patients and healthy subjects with visual pursuit, in total agreement with the consensus on video. In conclusion, our device can be used easily in patients with DOC while respecting the current guidelines of visual pursuit assessment. Our results suggest that our material and our classification method can identify patients with visual pursuit, as well as the three researchers based on video recordings can.

Minimally conscious state (MCS) is a neurological syndrome in which the patient shows signs of partial consciousness after having emerged from unresponsive wakefulness syndrome (UWS), which itself follows a state of coma. Distinguishing between MCS and UWS is complex and has major impact on the clinical management and prognosis of affected patients. Research on disorders of consciousness (DoC) has revealed that (1) visual pursuit, i.e. the ability of a patient to track a moving stimulus, is one of the most decisive clinical signs for establishing the MCS/UWS distinction, and that (2) the most effective moving stimulus for visual pursuit assessment is a mirror where the patient can see his/her own face. In clinical practice, while this guidance is widely followed, the visual pursuit ability is typically assessed on the basis of the clinician's opinion only, i.e. in a subjective thus biased manner. In this paper, we present a new system using cameras and computer vision techniques, which helps clinicians to objectify the assessment of visual pursuit. Our system is specifically designed to work with the moving mirror stimulus in order to follow the recommended, well-established clinical setup. We validate our system on healthy control subjects and give preliminary results obtained with DoC patients.

For each minute of test, we computed: • mean reaction time • percentage of lapses (lapse = RT > 2s or no answer)• level of somnolence (determined by our system). EVALUATION OF THE PERFORMANCE OF AN EXPERIMENTAL SOMNOLENCE QUANTIFICATION SYSTEM IN TERMS OF REACTION TIMES AND LAPSESSomnolence is known to be a major cause of various types of accidents [1], and ocular parameters are recognized to be reliable physiological indicators of somnolence [2]. We have thus developed an experimental somnolence quantification system that uses images of the eye to automatically determine a level of somnolence on a numerical scale. The aim of this study is to show that the level of somnolence determined by our system is well related to the level of performance of subjects accomplishing three reaction-time tests in different sleep deprivation conditions.• 27 participants (12 M, 15 F, mean age of 24.3 years, range of 19-32 years)• Test = reaction time (RT) test (duration of 15 minutes) • Approved by ethics committee.• Région Wallonne (Belgium): for financial support;• Sleep Laboratory (CETES), University Hospital of Liège (Belgium): for expertise, assistance, and use of facilities.The level of somnolence determined by our system based on images of the eye is well "correlated" with the level of performance of a subject accomplishing a task. We have indeed shown that, in the case of a reaction-time task,• mean reaction times and percentages of lapses increased with levels of somnolence determined by our system; • a threshold of 5 on our scale of somnolence (from 0 to 10) is the best for predicting lapses. Our somnolence quantification system has thus significant potential for predicting performance decrements due to somnolence and, ultimately, for preventing somnolence-related accidents.