Eye Movement Characteristics Research Articles

Abnormal eye movement features in patients with depression: Preliminary findings based on eye tracking technology

BackgroundSaccadic eye movement (SEM) has been considered a non-invasive potential biomarker for the diagnosis of depression in recent years, but its application is not yet mature. In this study, we used eye-tracking technology to identify the eye movements of patients with depression to develop a new method for objectively identifying depression. MethodsThirty-six patients with depression as the depression group, while thirty-six matched healthy individuals as the control group were recruited and completed eye movement tests, including two tasks: the prosaccade task and the antisaccade task. iViewX RED 500 eye-tracking instruments from SMI were used to collect eye movement data for both groups. ResultsIn the prosaccade task, there was no difference between the depression and control groups(t = 0.019， P > 0.05). In general, with increasing angle, both groups showed significantly higher peak velocity (F = 81.72， P < 0.0001), higher mean velocity (F = 32.83, P = 0.000), and greater SEM amplitude (F = 24.23, P < 0.0001). In the antisaccade task, there were significant differences in correct rate (t = 3.219, P = 0.002) and mean velocity (F = 3.253， P < 0.05) between the depression group and the control group. In the anti-effect analysis, there were significant differences in correct rate (F = 67.44, P < 0.0001) and accuracy (F = 79.02, P < 0.0001) between the depression group and the control group. Both groups showed longer latency and worse correct rate and precision in the antisaccade task compared with the prosaccade task. ConclusionPatients with depression showed different eye movement features, which could be potential biomarkers for clinical identification. Further studies must validate these results with larger sample sizes and more clinical populations.

Eye movement characteristics and visual fatigue assessment of virtual reality games with different interaction modes.

This study aimed to investigate the eye movement characteristics and visual fatigue of virtual reality games with different interaction modes. Eye movement data were recorded using the built-in eye tracker of the VR device and eye movement parameters were calculated from the recorded raw data. The Visual Fatigue Scales and Simulator Sickness Questionnaire were used to subjectively assess visual fatigue and overall discomfort of the VR experience. Sixteen male and 17 female students were recruited for this study. Results showed that both the primary and 360 mode of VR could cause visual fatigue after 30 min of gameplay, with significant differences observed in eye movement behavior between the two modes. The primary mode was more likely to cause visual fatigue, as shown by objective measurements of blinking and pupil diameter. Fixation and saccade parameters also showed significant differences between the two modes, possibly due to the different interaction modes employed in the 360 mode. Further research is required to examine the effects of different content and interactive modes of VR on visual fatigue, as well as to develop more objective measures for assessing it.

Eye Movements Induced by Stimulation of the Utricle

Pathophysiology of benign paroxysmal positional vertigo (BPPV) is canalolithiasis in the semicircular canal. The principle of Epley maneuver is moving debris from a long arm to the utricle, therefore debris stimulates the utricular macula in the sitting position after the treatment. To clarify the characteristics of eye movements evoked by the stimulation of unilateral macula of the utricle.

Eye movement characteristics in a mental rotation task presented in virtual reality.

Eye-tracking technology provides a reliable and cost-effective approach to characterize mental representation according to specific patterns. Mental rotation tasks, referring to the mental representation and transformation of visual information, have been widely used to examine visuospatial ability. In these tasks, participants visually perceive three-dimensional (3D) objects and mentally rotate them until they identify whether the paired objects are identical or mirrored. In most studies, 3D objects are presented using two-dimensional (2D) images on a computer screen. Currently, visual neuroscience tends to investigate visual behavior responding to naturalistic stimuli rather than image stimuli. Virtual reality (VR) is an emerging technology used to provide naturalistic stimuli, allowing the investigation of behavioral features in an immersive environment similar to the real world. However, mental rotation tasks using 3D objects in immersive VR have been rarely reported. Here, we designed a VR mental rotation task using 3D stimuli presented in a head-mounted display (HMD). An eye tracker incorporated into the HMD was used to examine eye movement characteristics during the task synchronically. The stimuli were virtual paired objects oriented at specific angular disparities (0, 60, 120, and 180°). We recruited thirty-three participants who were required to determine whether the paired 3D objects were identical or mirrored. Behavioral results demonstrated that the response times when comparing mirrored objects were longer than identical objects. Eye-movement results showed that the percent fixation time, the number of within-object fixations, and the number of saccades for the mirrored objects were significantly lower than that for the identical objects, providing further explanations for the behavioral results. In the present work, we examined behavioral and eye movement characteristics during a VR mental rotation task using 3D stimuli. Significant differences were observed in response times and eye movement metrics between identical and mirrored objects. The eye movement data provided further explanation for the behavioral results in the VR mental rotation task.

Individualized Tracking of Neurocognitive-State-Dependent Eye-Movement Features Using Mobile Devices

With current clinical techniques, it is difficult to assess a patient's neurodegenerative disease (e.g., Alzheimer's) state accurately and frequently. The most widely used tests are qualitative or only performed intermittently, motivating the need for quantitative, accurate, and unobtrusive metrics to track disease progression. Clinical studies have shown that saccade latency (an eye movement measure of reaction time) and error rate (the proportion of eye movements in the wrong direction) may be significantly affected by neurocognitive diseases. Nevertheless, how these features change over time as a disease progresses is underdeveloped due to the constrained recording setup. In this work, our goal is to first understand how these features change over time in healthy individuals. To do so, we used a mobile app to frequently and accurately measure these features outside of the clinical environment from 80 healthy participants. We analyzed their longitudinal characteristics and designed an individualized longitudinal model using a Gaussian process. With a system that can measure eye-movement features on a much finer timescale in a broader population, we acquired a better understanding of eye-movement features from healthy individuals and provided research directions in understanding whether eye-movement features can be used to track neurocognitive states.

Research on drivers’ eye movement characteristics in the combined section of longitudinal slope and curve in the extra-long underwater tunnel

To explore the effects of a longitudinal slope, a curve, and their combined action on the eye movement characteristics and driving safety of drivers in extra-long underwater tunnel, vehicle tests are conducted. Their purpose is to obtain data on the pupil area change rate, blink duration, fixation duration, and average saccade time when a driver passes through each longitudinal slope and curve combination section. The change laws of‘ the eye movement characteristics in different curves and longitudinal slopes are analyzed. Moreover, the change models of the pupil area change rate, blink duration, fixation duration, and average saccade time of drivers under the combined action of a longitudinal slope and a curve are established. Considering multiple eye movement characteristics comprehensively, the TOPSIS method is used to analyze the drivers’ visual load of different longitudinal slope and curve combination sections. The results show that the interaction between a longitudinal slope and a curve has a significant impact on the eye movement characteristics of the drivers, with their combined effect being greater than that of a single factor, i.e., longitudinal slope or curve. A large longitudinal slope and a small curve radius imply a high pupil area change rate, long fixation duration, short blink duration, short average saccade time, and high visual load.

The influence of the stimulus category on the characteristics of eye movements in visual search

Introduction. The speed and efficiency of visual search are important characteristics of the activities of a significant range of specialists working with visual information. Specialists have carefully studied the features of visual search from the point of view of cognitive psychology and in connection with the modeling of oculomotor activity. It has been shown that its effectiveness and the parameters of search eye movements are influenced by both stimulation factors and some cognitive factors (features of the subject's preset, the influence of previous search stages, etc.). We assume that the search features are also related to how well a person represents the desired stimulus: whether it belongs to a familiar semantic category, how accurate and unambiguous its visual representation is.&#x0D; The study aims to explore the relationship between the parameters of eye movements in visual search and the features of the mental representation of the target object.&#x0D; Materials and methods. We have created sets of stimuli belonging to different semantic categories, characterized by different accuracy of mental representations, but having similar sensory qualities. Each object (both the target stimulus and the distractor) in the search field was a black circle on a white background with four black segments inside. The segments formed one of three configurations: a) a face; b) a Roman numeral; c) a random configuration. The study participants were asked to find in a matrix of 84 objects a specific target stimulus belonging to one of the three specified categories. The scientists recorded the participant's eye movements during the search.&#x0D; Results. The results of the study generally confirmed the hypothesis about the relationship of search characteristics with the category of the target object. The smallest number of errors, the minimum duration of fixation and the maximum amplitude of saccades characterized the search for Roman numerals — objects with a well-formed and well-defined visual representation. The most difficult, as expected, was the search for objects representing random configurations of segments.&#x0D; Limitations. Since the study was largely exploratory in nature, the results obtained require clarification on a wider sample. It is possible that the characteristics of the search were influenced by such a factor not controlled in the study as the configuration of the lines, namely, the presence or absence of their intersections, which should be taken into account in further work.&#x0D; Conclusions. The better the visual representation of the target object is formed, the more successful its search is. In this case, eye movements are characterized by a shorter duration of fixation and a higher frequency of saccades of greater amplitude.&#x0D; Ethics. The study was conducted in compliance with the ethical principles set out in the Helsinki Declaration of the World Medical Association, all participants gave voluntary informed consent.

Holistic processing and visual characteristics of regulated and spontaneous expressions.

The rapid and efficient recognition of facial expressions is crucial for adaptive behaviors, and holistic processing is one of the critical processing methods to achieve this adaptation. Therefore, this study integrated the effects and attentional characteristics of the authenticity of facial expressions on holistic processing. The results show that both regulated and spontaneous expressions were processed holistically. However, the spontaneous expression details did not indicate typical holistic processing, with the congruency effect observed equally for aligned and misaligned conditions. No significant difference between the two expressions was observed in terms of reaction times and eye movement characteristics (i.e., total fixation duration, fixation counts, and first fixation duration). These findings suggest that holistic processing strategies differ between the two expressions. Nevertheless, the difference was not reflected in attentional engagement.

Effect of Directional Auditory Takeover Request on Takeover Behavior and Eye Movement in Conditionally Automated Driving

Conditionally automated driving (CAD) systems allow the driver to temporarily disengage from driving tasks. The significant concern of CAD is to ensure a safe and timely transition to the manual driving mode when the system exceeds its limits and issues a takeover request (TOR). The aim of this study was to investigate the effect of directional auditory TOR on the driver takeover process. A within-subject experimental design was used: twenty-four participants drove four times in an automated driving simulation scenario and received two non-directional auditory TORs and two directional auditory TORs (TOR directions corresponding to the orientation of potential hazards). The takeover behavior and eye movement characteristics relevant to the takeover process were collected. The results showed that directional auditory TOR enabled drivers to shift their visual attention to the hazards’ area faster than non-directional auditory TOR, accelerating the driver’s understanding of the road environment and improving takeover behavior. The study may provide a reference for the design of takeover requests for conditionally automated driving.

Identifying Depressive Disorder With Sleep Electroencephalogram Data: A Study Based on Deep Learning

To explore the effectiveness of using deep learning network combined Vision Transformer (ViT) and Transformer to identify patients with depressive disorder on the basis of their sleep electroencephalogram (EEG) signals. The sleep EEG signals of 28 patients with depressive disorder and 37 normal controls were preprocessed. Then, the signals were converted into image format and the feature information on frequency domain and spatial domain was retained. After that, the images were transmitted to the ViT-Transformer coding network for deep learning of the EEG signal characteristics of the rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep in patients with depressive disorder and those in normal controls, respectively, and to identify patients with depressive disorder. Based on the ViT-Transformer network, after examining different EEG frequencies, we found that the combination of delta, theta, and beta waves produced better results in identifying depressive disorder. Among the different EEG frequencies, EEG signal features of delta-theta-beta combination waves in REM sleep achieved 92.8% accuracy and 93.8% precision for identifying depression, with the recall rate of patients with depression being 84.7%, and the F0.5 value being 0.917±0.074. When using the delta-theta-beta combination EEG signal features in NREM sleep to identify depressive disorder, the accuracy was 91.7%, the precision was 90.8%, the recall rate was 85.2%, and the F0.5 value was 0.914±0.062. In addition, through visualization of the sleep EEG of different sleep stages for the whole night, it was found that classification errors usually occurred during transition to a different sleep stage. Using the deep learning ViT-Transformer network, we found that the EEG signal features in REM sleep based on delta-theta-beta combination waves showed better effect in identifying depressive disorder.

Sensitivity Study of Highway Tunnel Light Environment Parameters Based on Pupil Change Experiments and CNN Judging Method

There is a sparsity of research regarding the nonlinear relationship between the sensitivity of the light environment parameters in the middle section of the tunnel under multi-factor conditions in multiple samples. Due to the lack of research, the present study was conducted in order to investigate said relationship. To determine the parameters of the eye-movement characteristics required for the convolutional neural network prediction evaluation, a tunnel simulation model was established using DIALux10 simulation software and a series of dynamic driving tests were conducted based on an indoor simulation experimental platform. Further, through employing the residual network ResNet to extract data features and the pyramidal pooling network module, a convolutional neural network judging model with adaptive learning capabilities was established for investigating the nonlinear relationship of sensitivity of light environment parameters. Following the test, the degree of influence on the diameter of the pupil for the different levels of each factor were: the optimal configuration of the staggered layout on either side of the lamp arrangement, the optimal 3 m height under the different sidewall painting layout height conditions, the optimal green painting color under the different sidewall painting color conditions, and the optimal 6500 k under different LED light source color temperature conditions. The results of the present study serve to expand the use of the convolutional neural network model in tunnel light environment research and provide a new path for evaluating the quality of tunnel light environment.

Study on the eye movement characteristics of the badminton practitioners of different levels regarding visual attention.

Badminton is a highly sophisticated, fierce, and competitive tactical game. It requires the same action of hitting a ball with different landing points. Therefore, the complexity of badminton practitioner's sports decision-making is relatively high. Accordingly, it is extremely important to study the difference between the eye movement characteristics of different levels of badminton athletes and the difference between the eye movement characteristics of different sports levels of amateur athletes. Overall, 15 students from the badminton professional training team of the Physical Education College of the Jiangxi Science and Technology Normal University and 15 students from the common public sports and badminton course were included as experimental participants in the present study. The laboratory experimental test on the virtual sports situation in badminton was conducted using an eye tracker. The eye movement index of both the badminton professionals and the experimental participants was recorded for statistical analysis, and the following results were obtained: (1) In the cognitive decision-masking task, the reaction time of the professional badminton practitioners was faster than that of the amateur practitioners. Similarly, in the intuitive decision-masking task, the reaction time and accuracy of the former were better than those of the latter. (2) The professional badminton practitioners' group was able to process and integrate the searched information in the process of sports attention selection information; although the amateur group was able to search and filter information, they were passive and could not actively process and assimilate the searched information. (3) The professional badminton practitioners could allocate their attention reasonably and process information in the process of attention transfer, while their amateur counterparts were affected easily by external interference factors. The level of motor intelligence of badminton practitioners in the professional group was higher than that of the amateur practitioners. Thus, these two groups of different levels showed attention transfer. (4) The mental skills of the professional group were higher than those of the amateur group.

Eye movement characteristics are not significantly influenced by psychiatric comorbidities in people with visual snow syndrome

Visual snow syndrome (VSS) is a neurological disorder primarily affecting the processing of visual information. Using ocular motor (OM) tasks, we previously demonstrated that participants with VSS exhibit altered saccade profiles consistent with visual attention impairments. We subsequently proposed that OM assessments may provide an objective measure of dysfunction in these individuals. However, VSS participants also frequently report significant psychiatric symptoms. Given that that these symptoms have been shown previously to influence performance on OM tasks, the objective of this study was to investigate whether psychiatric symptoms (specifically: depression, anxiety, fatigue, sleep difficulties, and depersonalization) influence the OM metrics found to differ in VSS. Sixty-one VSS participants completed a battery of four OM tasks and a series of online questionnaires assessing psychiatric symptomology. We revealed no significant relationship between psychiatric symptoms and OM metrics on any of the tasks, demonstrating that in participants with VSS, differences in OM behaviour are a feature of the disorder. This supports the utility of OM assessment in characterising deficit in VSS, whether supporting a diagnosis or monitoring future treatment efficacy.

A methodology to detect pilot perception of warning information by eye movement data and deep residual shrinkage networks

AbstractThis paper studied the use of eye movement data to form criteria for judging whether pilots perceive emergency information such as cockpit warnings. In the experiment, 12 subjects randomly encountered different warning information while flying a simulated helicopter, and their eye movement data were collected synchronously. Firstly, the importance of the eye movement features was calculated by ANOVA (analysis of variance). According to the sorting of the importance and the Euclidean distance of each eye movement feature, the warning information samples with different eye movement features were obtained. Secondly, the residual shrinkage network modules were added to CNN (convolutional neural network) to construct a DRSN (deep residual shrinkage networks) model. Finally, the processed warning information samples were used to train and test the DRSN model. In order to verify the superiority of this method, the DRSN model was compared with three machine learning models, namely SVM (support vector machine), RF (radom forest) and BPNN (backpropagation neural network). Among the four models, the DRSN model performed the best. When all eye movement features were selected, this model detected pilot perception of warning information with an average accuracy of 90.4%, of which the highest detection accuracy reached 96.4%. Experiments showed that the DRSN model had advantages in detecting pilot perception of warning information.

Особенности движений глаз в задаче зрительного поиска в зависимости от вербализуемости и симметричности стимулов

Background. Visual search is considered today as an active process, usually implemented with the participation of eye movements. Modern search models assume the influence of both stimulus and cognitive variables on its characteristics and effectiveness. However, in research, as a rule, only one type of variables is controlled, which requires the creation of new experimental paradigms that allow studying the influence of both types of factors and their interaction. Objective. Investigation of the influence of factors of verbalizability and symmetry of the target stimulus on the effectiveness and speed of its search and characteristics of search eye movements. Methods. The created stimuli were identical in basic stimulus characteristics (color, shape, size, slope, number of elements) but differed in the symmetry of configuration and the level of verbalizability. An intragroup experimental plan was used. Eye movements were recorded in the process of visual search with the SMI HiSpeed 1250 eye tracker. Sample. The total sample of the study consisted of 48 participants: 18 people (10 men, 8 women, average age — 20) — at the stage of assessing the verbalizability of stimuli; 30 people (6 men, 24 women, average age — 20) — at the main stage. Results. None of the factors affected the effectiveness of visual search, it remained at a high level. In the absence of the target stimulus in the matrix, the search for symmetrical targets with verbalizability was completed faster, the search for asymmetric stimuli with low verbalizability was the slowest. Both factors influenced the features of searching eye movements: the search for "simple" (symmetrical and/or highly verbalizable) stimuli was characterized by shorter fixations and wide-amplitude saccades than the search for "complex" targets. ANOVA showed that there is no interaction between the factors of verbalizability and symmetry. Conclusion. The results generally support the hypothesis about the influence of symmetry and verbalizability on the visual search, testifying in favor of their independent influence on the dynamics of search eye movements.

Effect of Perspective on Eye Movement of Aesthetic Preference Research on Wai Shape of New Chinese Style Arhat Bed

The new chinese style Arhat bed is taken as the research objective to explore the effect of people’s view on people’s aesthetic preferences for Wai shape of new chinese Arhat bed, using the eye movement analysis method through comprehensive analysis on eye movement data from volunteers. Eye movements characteristics of 20 volunteers in watching pictures of different new chinese Arhat bed Wai shape are recorded by SR research Eyelink head mounted eye tracker. Experimental results show that eye movement data of men and women are in the same trend, but there are few data in instability and the male and female have a significant correlation with the time and the number of fixation points, with a obvious regression effect, under two different presented Perspective of products. In all 16 samples selected, samples 12 has the most fixation and fixation times, while samples 15 and samples 13 has the least. Visual fatigue and the difference between 3D model and the real Arhat bed products have some effects on the experimental data. It makes no difference for people to choose products from different perspectives in where the products are placed, and people prefer the Wai shape which contains column and is simple.

Eye tracking as a tool for assessing social cognition: A case-control study comparing patients with psychosis and healthy controls

Background: Eye movement dysfunction is a common abnormality in Schizophrenia and other psychotic disorders. Various eye movement paradigms can be used to measure predefined parameters using eye trackers. The current study intends to understand the eye movement characteristics in individuals suffering from psychosis using newer eye-tracking instruments with better precision. Methodology: In this study, we included 29 individuals meeting the diagnostic criteria for schizophrenia spectrum and other psychotic disorders based on the clinical assessment as per Diagnostic and Statistical Manual of Mental Disorders, 5th Edition along with 29 age- and gender-matched healthy controls and measured various eye tracking parameters using a Tobii Pro Spectrum. We compared the eye-tracking parameters between both groups. Results: There were significant differences between the groups in the free-viewing task. The average duration of fixations was higher, whereas the number of fixations and total amplitude of saccades was found to be reduced in the patient group. The peak velocity of the first saccade was also seen to be reduced in the patient group. However, there were no abnormalities in the anti-saccade, predictive saccade, memory saccade, and smooth pursuit tasks. Eye movement parameters were also found to be significantly correlated with the cognitive task measurements. Conclusion: These findings confirm the presence of eye movement abnormalities in psychosis spectrum disorders and their role in cognitive deficits in psychosis. Altered free viewing may be linked to deficits in social cognition found in patients with schizophrenia.

Three-Stream Convolutional Neural Network for Depression Detection With Ocular Imaging.

Depression is a prevalent and severe mental disorder that significantly affects both mind and body, leading to persistent feelings of sadness, despair, and impaired functionality. Diagnosis of depression primarily relies on clinical assessment and observation of symptoms. However, due to the lack of objective indicators, the experience and skills of doctor may lead to misdiagnosis. Current researches indicate that eye movement patterns and pupil dilation can serve as potential biomarkers for emotional and cognitive dysregulation in individuals with depression. However, most studies are based on manually extracted eye movement features, overlooking a significant portion of information available in ocular imaging. This paper proposes Three-Stream Convolutional Neural Network (TSCNN) for detecting depression, leveraging both spatio-temporal information of raw ocular imaging and paradigmatic semantic features. We suggest using optical flow with different sampling intervals to capture temporal features. In the third stream, we employ an encoder to learn semantic information from paradigm images and use it as prior knowledge. Finally, we utilize a fully connected network for classification, achieving an accuracy of 79.3% on our self-collected dataset. The proposed method may demonstrate significant clinical utility in the future.

User Authentication by Eye Movement Features Employing SVM and XGBoost Classifiers

User Authentication by Eye Movement Features Employing SVM and XGBoost Classifiers

Nystagmus patterns classification framework based on deep learning and optical flow

Benign paroxysmal positional vertigo (BPPV) is the most common vestibular peripheral vertigo disease characterized by brief recurrent vertigo with positional nystagmus. Clinically, it is common to recognize the patterns of nystagmus by analyzing infrared nystagmus videos of patients. However, the existing approaches cannot effectively recognize different patterns of nystagmus, especially the torsional nystagmus. To improve the performance of recognizing different nystagmus patterns, this paper contributes an automatic recognizing method of BPPV nystagmus patterns based on deep learning and optical flow to assist doctors in analyzing the types of BPPV. Firstly, we present an adaptive method for eliminating invalid frames that caused by eyelid occlusion or blinking in nystagmus videos and an adaptive method for segmenting the iris and pupil area from video frames quickly and efficiently. Then, we use a deep learning-based optical flow method to extract nystagmus information. Finally, we propose a nystagmus video classification network (NVCN) to categorize the patterns of nystagmus. We use ConvNeXt to extract eye movement features and then use LSTM to extract temporal features. Experiments conducted on the clinically collected datasets of infrared nystagmus videos show that the NVCN model achieves an accuracy of 94.91% and an F1 score of 93.70% on nystagmus patterns classification task as well as an accuracy of 97.75% and an F1 score of 97.48% on torsional nystagmus recognition task. The experimental results prove that the framework we propose can effectively recognize different patterns of nystagmus.