Clémentine François

Comparative evaluation of existing and new methods for correcting ocular artifacts in electroencephalographic recordings

EEG signals are often contaminated by ocular artifacts (OAs), in particular when they are recorded for a subject that is, in principle, awake, such as in a study of drowsiness. It is generally desirable to detect and/or correct these OAs before interpreting the EEG signals. We have identified 11 existing methods for dealing with OAs. Their study allowed us to create 16 new methods. We performed a comparative performance evaluation of the resulting 27 distinct methods using a common set of data and a common set of metrics. The data was recorded during a driving task of about two hours in a driving simulator. This led to a ranking of all methods, with five emerging clear winners, comprising two existing methods and three new ones.

The ULg multimodality drowsiness database (called DROZY) and examples of use

Drowsiness is a major cause of accidents, in particular in road transportation. It is thus crucial to develop robust drowsiness monitoring systems. There is a widespread agreement that the best way to monitor drowsiness is by closely monitoring symptoms of drowsiness that are directly linked to the physiology of an operator such as a driver. The best systems are completely transparent to the operator until the moment he/she must react. In transportation, cameras placed in the passenger compartment and looking at least at the face of the driver are most likely the best way to sense physiology related symptoms such as facial expressions and the fine behavior of the eyeballs and eyelids. We present here the new database called DROZY that provides multiple modalities of data to tackle the design of drowsiness monitoring systems and related experiments. We also present two novel systems developed using this database that can make predictions about the speed of reaction of an operator by using near-infrared intensity and range images of his/her face.

Tests of a New Drowsiness Characterization and Monitoring System Based on Ocular Parameters

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.

EVALUATION OF THE PERFORMANCE OF AN EXPERIMENTAL SOMNOLENCE QUANTIFICATION SYSTEM IN TERMS OF REACTION TIMES AND LAPSES

Somnolence is known to be a major cause of various types of accidents, and ocular parameters are recognized to be reliable physiological indicators of somnolence. We have thus developed an experimental somnolence quantification system that uses images of the eye and that produces a level of somnolence on a continuous numerical scale. The aim of this paper is to show that the level of somnolence produced by our system is well related to the level of performance of subjects accomplishing three reaction-time tests in different sleep conditions. Twenty seven subjects participated in the study and images of their right eye were continuously recorded during the tests. Levels of somnolence, reaction times (RTs), and percentages of lapses were computed for each minute of test. Results show that the values of these three parameters increase significantly with sleep deprivation. We determined the best threshold on our scale of somnolence to predict lapses, and we also shown that correlations exist with some of the ocular parameters. Our somnolence quantification system has thus significant potential to predict performance decrements of subjects accomplishing a task.