The spatial-frequency characteristics of the visual system were studied in mentally healthy persons and in patients with schizophrenia using a test of contrast sensivity in a comparison of the...

The objectification of diagnostics is one of the urgent problems of psychiatry. The aim is to study the relationship between changes in contrast sensitivity and cognitive impairment in schizophrenia and depression. The contrast sensitivity parameters were considered to be evidence of the functional state and the nature of interaction of the magnocellular and parvocellular visual systems, which provide mechanisms for global and local information analysis. The relationship between the features of their interaction with the processes of memory, attention, and thinking in schizophrenia and depression has been established. In schizophrenia, there is a mismatch in the functioning of the magnocellular and parvocellular neuronal systems, with a decrease in the activity of both systems. In depression, the level of activity of the magnocellular system is preserved, while the activity of the parvocellular system is significantly reduced. Thus, in depression, a different mismatch in the interaction of the magnocellular and parvocellular systems is observed compared to schizophrenia. The relationship of the activity of the magnocellular system with the characteristics of working memory and the properties of attention is shown. The balance shift towards the activity of the magnocellular system is accompanied by impaired selective attention and decreased voluntary regulation. Regarding thinking, the interrelation of the magnocellular system with the processes of abstraction and categorization and that of the parvocellular system with the selection and assessment of significant features is shown.

Modern mental fatigue detection methods include many parameters for evaluation. For example, many researchers use human subjective evaluation or driving parameters to assess this human condition. Development of a method for detecting the functional state of mental fatigue is an extremely important task. Despite the fact that human operator support systems are becoming more and more widespread, at the moment there is no open-source solution that can monitor this human state based on eye movement monitoring in real time and with high accuracy. Such a method allows the prevention of a large number of potential hazardous situations and accidents in critical industries (nuclear stations, transport systems, and air traffic control). This paper describes the developed method for mental fatigue detection based on human eye movements. We based our research on a developed earlier dataset that included captured eye-tracking data of human operators that implemented different tasks during the day. In the scope of the developed method, we propose a technique for the determination of the most relevant gaze characteristics for mental fatigue state detection. The developed method includes the following machine learning techniques for human state classification: random forest, decision tree, and multilayered perceptron. The experimental results showed that the most relevant characteristics are as follows: average velocity within the fixation area; average curvature of the gaze trajectory; minimum curvature of the gaze trajectory; minimum saccade length; percentage of fixations shorter than 150 ms; and proportion of time spent in fixations shorter than 150 milliseconds. The processing of eye movement data using the proposed method is performed in real time, with the maximum accuracy (0.85) and F1-score (0.80) reached using the random forest method.

Fatigue is a functional state characterized by a persistent decrease in performance. A system for recording the degree of driver fatigue based on the tremor modulation signal model is proposed. The amplitude and frequency of natural tremor oscillations of the eye are taken as an indicator of the degree of fatigue. It has been experimentally proven that tremor is an adequate marker of the condition of fatigue. The objectivity and effectiveness of the proposed method were evaluated in comparison with classical methods for assessing the functional state of the central nervous system—the simple visual-motor reaction and visual-motor coordination times. It was found that the probability of correct prediction of the condition of fatigue by measuring the parameters of natural tremor oscillations of the eye (amplitude and frequency) with the data of simple visual-motor response and visual-motor coordination task is 87.4 and 88.6%, respectively.