Human Psychophysiological State Research Articles

Effects of Automation and Transparency on Human Psychophysiological States and Perceived System Performance in Construction Safety Automation: An Electroencephalography Experiment

Effects of Automation and Transparency on Human Psychophysiological States and Perceived System Performance in Construction Safety Automation: An Electroencephalography Experiment

The contribution of neuroscience in evaluating human-robot collaboration: a multidimensional approach

Collaborative robots (cobots) are a recent introduction in the industrial sector and are designed to work on shared tasks with humans with the aim to provide physical and cognitive support. This has led to a growing interest in the study of factors affecting human-robot collaboration (HRC) with the idea of making cobots more responsive to the human psychophysiological state. Several studies have begun to investigate dimensions such as mental workload and stress of the individual interacting with a cobot using behavioural and neurophysiological metrics, leading to a fruitful convergence between the worlds of neuroscience and robotics. It is therefore discussed the utility of a multidisciplinary and multidimensional approach in the study of HRC. Relevant physiological, neurophysiological, behavioural, and subjective measures are presented, as well as the necessity of their integration in HRC research. It is also introduced the importance of considering in HRC individual differences in terms of cognitive and emotional functioning, and factors related to individual representations and interpersonal environment.

Аппаратно-программный комплекс для диагностики психофизиологического состояния человека в процессе решения когнитивных задач

Аппаратно-программный комплекс для диагностики психофизиологического состояния человека в процессе решения когнитивных задач

Social Allostasis Regulation by Robots

Social allostasis, the process by which social interactions dynamically regulate psychophysiological states (e.g., emotions), is attracting attention. Social allostasis can significantly impact people's mental and physical wellbeing. However, adaptive social relationships are not necessarily available to all people. The use of robots is expected to solve this problem. In this paper, we present three studies of our group that prove the influence of social interactions with robots on human psychophysiological states. Our results showed that robots could amplify human subjective and physiological emotional responses through conversation, touch, and television co-viewing experiences. These results suggest that robots may be helpful for adaptive social allostasis regulation.

Changes in the Power and Coupling of Infra-Slow Oscillations in the Signals of EEG Leads during Stress-Inducing Cognitive Tasks

A change in the human psychophysiological state, caused by stress in particular, affects the processes of autonomic control, the activity of which is reflected in infra-slow oscillations of brain potentials with a frequency of less than 0.5 Hz. We studied the infra-slow oscillations in scalp electroencephalogram (EEG) signals in the frequency ranges of 0.05–0.15 Hz and 0.15–0.50 Hz that are associated with the processes of sympathetic and parasympathetic control, respectively, in healthy subjects at rest and during stress-inducing cognitive tasks. The power spectra of EEG signals, the phase coherence coefficients, and indices of directional coupling between the infra-slow oscillations in the signals of different EEG leads were analyzed. We revealed that, compared with the state of rest, the stress state is characterized by a significant decrease in the power of infra-slow oscillations and changes in the structure of couplings between infra-slow oscillations in EEG leads. In particular, under stressful conditions, a decrease in both intrahemispheric and interhemispheric coupling between EEG leads occurred in the range of 0.05–0.15 Hz, while a decrease in intrahemispheric and an increase in interhemispheric couplings was observed in the range of 0.15–0.50 Hz.

Diagnosis and monitoring of a person's psychophysiological state based on multidimensional transient functions of the oculo-motor system

An important scientific and practical problem has been solved, which consists in the creation of methods and tools for nonlinear dynamic identification "input-output" of the human oculomotor system (OMS). Identification is carried out based on Volterra models in the form of multidimensional transient functions (n-dimensional integrals from Volterra kernels) using innovative eye tracking technology. As test signals, visual stimuli are used, which are displayed on the monitor screen at different distances from the starting position, which formally corresponds to the action of step signals with different amplitudes at the OMS input. Experimental studies of the OMS of individuals were carried out using the Tobii Pro TX300 eye tracker. The transient functions of the first and diagonal intersections of the transient functions of the second and third order were determined based on the data of oculographic studies. The models differ from the known ones in that they provide the possibility of modeling the OMS in each interval of input signals beyond the radius of convergence of the Volterra series. The obtained multidimensional functions are used as a source of primary data in the implementation of intelligent information technology for the diagnosis and monitoring of human psychophysiological states. A set of heuristic features are proposed, which are determined using integral and differential transformations of the obtained transient functions of the OMS. The informativeness of individual signs and all their possible combinations in pairs according to the indicator of the probability of correct recognition was studied. The research results were obtained using the construction of Bayesian classifiers in different spaces of the proposed features. Figs.: 2. Refs.: 15 titles.
 Keywords: psychophysiological state, diagnosis, monitoring, oculo-motor system, identification, Volterra model, multidimensional transient functions, test visual stimuli, eye-tracking technology

A gradient-based automatic optimization CNN framework for EEG state recognition

Objective. The electroencephalogram (EEG) signal, as a data carrier that can contain a large amount of information about the human brain in different states, is one of the most widely used metrics for assessing human psychophysiological states. Among a variety of analysis methods, deep learning, especially convolutional neural network (CNN), has achieved remarkable results in recent years as a method to effectively extract features from EEG signals. Although deep learning has the advantages of automatic feature extraction and effective classification, it also faces difficulties in network structure design and requires an army of prior knowledge. Automating the design of these hyperparameters can therefore save experts’ time and manpower. Neural architecture search techniques have thus emerged. Approach. In this paper, based on an existing gradient-based neural architecture search (NAS) algorithm, partially-connected differentiable architecture search (PC-DARTS), with targeted improvements and optimizations for the characteristics of EEG signals. Specifically, we establish the model architecture step by step based on the manually designed deep learning models for EEG discrimination by retaining the framework of the search algorithm and performing targeted optimization of the model search space. Corresponding features are extracted separately according to the frequency domain, time domain characteristics of the EEG signal and the spatial position of the EEG electrode. The architecture was applied to EEG-based emotion recognition and driver drowsiness assessment tasks. Main results. The results illustrate that compared with the existing methods, the model architecture obtained in this paper can achieve competitive overall accuracy and better standard deviation in both tasks. Significance. Therefore, this approach is an effective migration of NAS technology into the field of EEG analysis and has great potential to provide high-performance results for other types of classification and prediction tasks. This can effectively reduce the time cost for researchers and facilitate the application of CNN in more areas.

A CNN identified by reinforcement learning-based optimization framework for EEG-based state evaluation

Objective. Electroencephalogram (EEG) data, as a kind of complex time-series, is one of the most widely-used information measurements for evaluating human psychophysiological states. Recently, numerous works applied deep learning techniques, especially the convolutional neural network (CNN), into EEG-based research. The design of the hyper-parameters of the CNN model has a great influence on the performance of the model. Therefore, automatically designing these hyper-parameters can save the time and labor of experts. This leads to the appearance of the neural architecture search technique. In this paper, we propose a reinforcement learning (RL)-based step-by-step framework to efficiently search for CNN models. Approach. Specifically, the deep Q network in RL is first used to determine the depth of convolutional layers and the connection modes among layers. Then particle swarm optimization algorithm is used to fine-tune the number and size of convolution kernels. Through this step-by-step strategy, the search space can be narrowed in each step for saving the overall time cost. This framework is employed for both EEG-based sleep stage classification and driver drowsiness evaluation tasks. Main results. The results show that compared with state-of-the-art methods, the high-performance CNN models identified by the proposed optimization framework, can achieve high overall accuracy and better root mean squared error in the two tasks. Significance. Therefore, the proposed optimization framework has a great potential to provide high-performance results for other kinds of classification and prediction tasks. In this way, it can greatly save researchers’ time cost and promote broader applications of CNNs.

Statistical Approach for Subject’s State Identification by Face and Neck Thermograms with Small Training Sample

Existing methods of human psychophysiological states identification are either contact, or do not allow a test without the active involvement of a subject in this procedure. The use of thermal imaging systems has number of advantages: the lack of physical contact with the system, and the fact that temperature is a reliable indicator of health. In the work a new feature space consisting of 465 features divided into static (mean values in each face and neck area, correlation of temperatures between areas in each frame) and dynamic (ratio or correlation of temperatures between areas for subsequent frames) groups is proposed. The evaluation of the informativeness of the obtained features for psychophysiological state recognition is provided. The most informative features were found. Several classifiers were tested: a perceptron-type neural network, a network of quadratic forms, as well as a modified Bayes hypothesis formula, which showed the best result. The predicted probability of errors is less than 0.005.

THE INFLUENCE OF HUMAN PSYCHOPHYSIOLOGICAL STATE SECURITY TEHNOSFERNUJU

The article is devoted to the influence of human Psychophysiological State on tehnosfernuju security. Discussed in detail various psycho-physiological state of the person in the special conditions of employment and professional activities, styles of his behavior. Particular attention is paid to the analysis, the so-called «human factor», infallibility complex, chronic fatigue syndrome, intensification of mental activity, long nervous-mental tension.

Fractal analysis of the relationship between the visual complexity of laser show pictures and a human psychophysiological state

This paper presents the results of the fractal analysis of the relationship between the visual complexity of laser show pictures and its human aesthetic perception. Recommendations on the use of laser effects in the art therapy are presented

Investigating the effect of the reality gap on the human psychophysiological state in the context of human-swarm interaction

The reality gap is the discrepancy between simulation and reality—the same behavioural algorithm results in different robot swarm behaviours in simulation and in reality (with real robots). In this paper, we study the effect of the reality gap on the psychophysiological reactions of humans interacting with a robot swarm. We compare the psychophysiological reactions of 28 participants interacting with a simulated robot swarm and with a real (non-simulated) robot swarm. Our results show that a real robot swarm provokes stronger reactions in our participants than a simulated robot swarm. We also investigate how to mitigate the effect of the reality gap (i.e., how to diminish the difference in the psychophysiological reactions between reality and simulation) by comparing psychophysiological reactions in simulation displayed on a computer screen and psychophysiological reactions in simulation displayed in virtual reality. Our results show that our participants tend to have stronger psychophysiological reactions in simulation displayed in virtual reality (suggesting a potential way of diminishing the effect of the reality gap).

Investigating the effect of increasing robot group sizes on the human psychophysiological state in the context of human–swarm interaction

We study the psychophysiological state of humans when exposed to robot groups of varying sizes. In our experiments, 24 participants are exposed sequentially to groups of robots made up of 1, 3 and 24 robots. We measure both objective physiological metrics (skin conductance level and heart rate), and subjective self-reported metrics (from a psychological questionnaire). These measures allow us to analyse the psychophysiological state (stress, anxiety, happiness) of our participants. Our results show that the number of robots to which a human is exposed has a significant impact on the psychophysiological state of the human and that higher numbers of robots provoke a stronger response.

Pupillometric sleepiness measurements PST with concurrent video optic sensor of pupillary size

The article describes the design solutions of a mobile headset device used for the assessment of involuntary and oscillatory movements of the pupils. The pupillographic sleepiness test (PST) is used to assess the human psychophysiological state influenced by the level of drowsiness or alertness by observing the so-called fatigue wave. Since the overall size of the pupil is controlled predominantly by sympathetic inhibition of the parasympathetic Edinger–Westphal nuclei, spontaneous changes in its size are considered to be the result of decreasing central sympathetic activity. In the state of reduced alertness, changes in pupil size recorded in the dark show slow rhythmic oscillations, called fatigue waves. PST must be carried out in complete darkness (infrared goggles) and silence for a duration of 11min. Authors used the results of studies conducted before the development of the device. Those results played a significant role in shaping the structural assumptions on the sensor in its function as an indicator of the level of human fatigue. The article includes the results of the experimental research on the sensor's testing process involving variable frequency parameters of light, luminance and chrominance excitations. The obtained results revealed that the type of light stimulating the retina has significant impact on the oscillation parameters of pupils.

Methods of investigating the influence of sensory stimuli on human psychophysiological state

This article is a review of most recent patented non-invasive psychometric methods by foreign authors. The reviewed articles allow to define the specificity and the extent of influence of sensory stimuli on human psychological state by recording the physiological changes that correspond with certain states. The methods are classified by physiological indices chosen by the researchers as informative signs that indicate certain emotional states of test subjects.

The effect of space—frequency characteristics of the organ of vision on the human psychophysiological state and working capacity

During the long-term stay in closed environment of the orbital space station, it is extremely important that the light conditions and color scheme were favorable for the working capacity of a cosmonaut. The systems of lighting should ensure visual comfort for the crew members under stressful conditions and, in addition, they should appropriate to their health and recovery of functional reserves during the long-duration flight. To solve this task, the distribution of luminosity and chromaticity within a closed space should be taken into account. For better understanding the perception of visual information, we studied the color space-frequency characteristics of the organ of vision (SFC VO). A set of sensors was used to estimate psychophysiological parameters. After preadaptation of an operator, VO contrast sensitivity was measured by determining the thresholds of advent and disappearance of the test objects; this was accompanied by fixation of the time and adaptive brightness. Analysis of the experimental data made it clear that the color contrast sensitivity and directly related SFC of the organ of vision are valuable markers for interrelationships between processing of visual information and psychophysiological state of a human. Note that the color SFC VO not only illustrates deviations but also enables their quantification. Changes in psychophysiological state are accompanied by the appropriate changes in SFC VO, which should be taken into account in developing the neural information technologies used for health recovery and light-and-color control of environment.

FUNCTIONAL ORGANIZATION OF HUMAN PSYCHOPHYSIOLOGICAL STATES WITH RELATION OF ADAPTATION LEVEL TO STRAIN MUSCULAR ACTIVITY

The influence ofstrain muscular activity to forming of human psychophysiological states was studied. The results are showed that psychophysiological states in human with higher adaptation level to strain muscular activity are characterized of more determined organization of information processing system and system of heart rate regulation for concerning of human with simple adaptation level.