Direction Of Eye Movement Research Articles

Neurons in Primate Area MSTd Signal Eye Movement Direction Inferred from Dynamic Perspective Cues in Optic Flow.

Smooth eye movements are common during natural viewing; we frequently rotate our eyes to track moving objects or to maintain fixation on an object during self-movement. Reliable information about smooth eye movements is crucial to various neural computations, such as estimating heading from optic flow or judging depth from motion parallax. While it is well established that extraretinal signals (e.g., efference copies of motor commands) carry critical information about eye velocity, the rotational optic flow field produced by eye rotations also carries valuable information. Although previous work has shown that dynamic perspective cues in optic flow can be used in computations that require estimates of eye velocity, it has remained unclear where and how the brain processes these visual cues and how they are integrated with extraretinal signals regarding eye rotation. We examined how neurons in the dorsal region of the medial superior temporal area (MSTd) of two male rhesus monkeys represent the direction of smooth pursuit eye movements based on both visual cues (dynamic perspective) and extraretinal signals. We find that most MSTd neurons have matched preferences for the direction of eye rotation based on visual and extraretinal signals. Moreover, neural responses to combinations of these signals are well predicted by a weighted linear summation model. These findings demonstrate a neural substrate for representing the velocity of smooth eye movements based on rotational optic flow and establish area MSTd as a key node for integrating visual and extraretinal signals into a more generalized representation of smooth eye movements.SIGNIFICANCE STATEMENT We frequently rotate our eyes to smoothly track objects of interest during self-motion. Information about eye velocity is crucial for a variety of computations performed by the brain, including depth perception and heading perception. Traditionally, information about eye rotation has been thought to arise mainly from extraretinal signals, such as efference copies of motor commands. Previous work shows that eye velocity can also be inferred from rotational optic flow that accompanies smooth eye movements, but the neural origins of these visual signals about eye rotation have remained unknown. We demonstrate that macaque neurons signal the direction of smooth eye rotation based on visual signals, and that they integrate both visual and extraretinal signals regarding eye rotation in a congruent fashion.

Bezpečnosť účastníkov cestnej premávky a ich vplyv na vnímanie dopravnej situácie

When analysing an accident scene, one of the significant tasks is to evaluate the driver's ability to perceive the traffic situation according to different aspects. Currently, there are various eye movement measurement systems designed to perform a high-quality reconstruction of the direction of eye movement of road users depending on time. In addition to the basic physiology of the perception of road users, the paper deals with using the Viewpointsystem® measurement system designed to analyse eye movement when perceiving the traffic situation. The article assesses the development of traffic accidents in the Slovak Republic according to the consequences of traffic accidents and categories of road users. As part of determining the decisive moment for a collision situation, an analysis of the direction of the eye movement of the driver of the vehicle during a dangerous traffic situation while driving on a road section located in a rural area is performed.

Importance of two-dimensional gaze analyses in the assessment of reading performance in patients with retinitis pigmentosa.

The causes of reading difficulties in people with peripheral visual field loss are not fully understood. We conducted a two-dimensional gaze analysis on eye movements during reading in patients with retinitis pigmentosa to investigate the causes of reading difficulties in relation to the central visual field using a binocular eye mark recorder (EMR-9). Twenty-seven patients with retinitis pigmentosa whose central visual field narrowed to ≤ 20° using Goldmann kinetic perimetry (I/4 target) and this present study included eight healthy participants. The participants' visual acuities were corrected to better than +0.4 logMAR. Correlations and multivariate regression analyses were investigated between the number of letters read correctly, the I/4 central visual field, V/4 perifoveal and peripheral visual field, and visual acuity. Multivariate regression analysis revealed that all these parameters played almost equal roles in the number of letters read correctly. In the two-dimensional gaze analysis, the task performance time of patients during reading increased as the I/4 central visual field narrowed. The task performance time was more clearly correlated with the rotation saccade (r = 0.428, p <0.05) and the distance of the vertical direction (ΣY) of eye movements (r = 0.624, p < 0.01), but not with regressive saccade and the distance of the horizontal direction (ΣX). Visual acuity was correlated with the task performance time (-0.436, <0.05) but not with eye movement directionality. Reading difficulties in patients with retinitis pigmentosa result from impaired eye movement directionality. Understanding eye measurements for people with tunnel vision required a two-dimensional gaze analysis. The two-dimensional gaze analysis also showed that the involvement of the perifoveal and peripheral visual fields, visual acuity, and I/4 central visual field was important for reading in people with tunnel vision.

A machine learning eye movement detection algorithm using electrooculography.

Eye movement quantification in polysomnograms (PSG) is difficult and resource intensive. Automated eye movement detection would enable further study of eye movement patterns in normal and abnormal sleep, which could be clinically diagnostic of neurologic disorders, or used to monitor potential treatments. We trained a long short-term memory (LSTM) algorithm that can identify eye movement occurrence with high sensitivity and specificity. We conducted a retrospective, single-center study using one-hour PSG samples from 47 patients 18-90 years of age. Team members manually identified and trained an LSTM algorithm to detect eye movement presence, direction, and speed. We performed a 5-fold cross validation and implemented a "fuzzy" evaluation method to account for misclassification in the preceding and subsequent 1-second of gold standard manually labeled eye movements. We assessed G-means, discrimination, sensitivity, and specificity. Overall, eye movements occurred in 9.4% of the analyzed EOG recording time from 47 patients. Eye movements were present 3.2% of N2 (lighter stages of sleep) time, 2.9% of N3 (deep sleep), and 19.8% of REM sleep. Our LSTM model had average sensitivity of 0.88 and specificity of 0.89 in 5-fold cross validation, which improved to 0.93 and 0.92 respectively using the fuzzy evaluation scheme. An automated algorithm can detect eye movements from EOG with excellent sensitivity and specificity. Noninvasive, automated eye movement detection has several potential clinical implications in improving sleep study stage classification and establishing normal eye movement distributions in healthy and unhealthy sleep, and in patients with and without brain injury.

Comparison of LSTM And SVM For Classification of Eye Movements in EOG Signals

People with disabilities suffer from inability to communicate with their surroundings, so Human-Computer Interaction (HCI) technologies are used to have a means of communication for people with disabilities with their surroundings. HCI is an emerging technology in the disciplines of Artificial Intelligence and Biomedical Engineering. To power an external device, HCI technology uses several basic signals such as ECG, EMG, and EEG. Electrooculography (EOG) is a technique for measuring the potential difference between the cornea and the retina located between the front and back of the human eye, and the main application of EOG is to determine the directions of different eye movements. This study aims to assess eye movement for communication by persons with disabilities using electrocardiogram (EOG) data. In this study, the Supporting Vector Machine (SVM) and Long- Short term memory (LSTM) classification techniques was used and two types of features (statistical and time domain features) were used. Classification accuracy was 90.7% and 93.9% when using SVM with statistical domain and time domain features, respectively ,whereas Classification accuracy was 90.1% when using LSTM .

A cognitive process occurring during sleep is revealed by rapid eye movements.

Since the discovery of rapid eye movement (REM) sleep, the nature of the eye movements that characterize this sleep phase has remained elusive. Do they reveal gaze shifts in the virtual environment of dreams or simply reflect random brainstem activity? We harnessed the head direction (HD) system of the mouse thalamus, a neuronal population whose activity reports, in awake mice, their actual HD as they explore their environment and, in sleeping mice, their virtual HD. We discovered that the direction and amplitude of rapid eye movements during REM sleep reveal the direction and amplitude of the ongoing changes in virtual HD. Thus, rapid eye movements disclose gaze shifts in the virtual world of REM sleep, thereby providing a window into the cognitive processes of the sleeping brain.

Diagnosis of colour vision deficits using eye movements

We set out to develop a simple objective test of functional colour vision based on eye movements made in response to moving patterns. We exploit the finding that while the motion of a colour-defined stimulus can be cancelled by adding a low-contrast luminance-defined stimulus moving in the opposite direction, the “equivalent luminance contrast” required for such cancellation is reduced when colour vision is abnormal. We used a consumer-grade infrared eye-tracker to measure eye movements made in response to coloured patterns drifting at different speeds. An automated analysis of these movements estimated individuals’ red-green equiluminant point and their equivalent luminance contrast. We tested 34 participants: 23 colour vision normal controls, 9 deuteranomalous and 2 protanomalous individuals. We obtained reliable estimates of strength of directed eye movements (i.e. combined optokinetic and voluntary tracking) for stimuli moving at 16 deg/s and could use these data to classify participants’ colour vision status with a sensitivity rate of 90.9% and a specificity rate of 91.3%. We conclude that an objective test of functional colour vision combining a motion-nulling technique with an automated analysis of eye movements can diagnose and assess the severity of protanopia and deuteranopia. The test places minimal demands on patients (who simply view a series of moving patterns for less than 90 s), requires modest operator expertise, and can be run on affordable hardware.

Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain-Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate.

Brain–computer interfaces (BCIs) based on electroencephalogram (EEG) have recently attracted increasing attention in virtual reality (VR) applications as a promising tool for controlling virtual objects or generating commands in a “hands-free” manner. Video-oculography (VOG) has been frequently used as a tool to improve BCI performance by identifying the gaze location on the screen, however, current VOG devices are generally too expensive to be embedded in practical low-cost VR head-mounted display (HMD) systems. In this study, we proposed a novel calibration-free hybrid BCI system combining steady-state visual-evoked potential (SSVEP)-based BCI and electrooculogram (EOG)-based eye tracking to increase the information transfer rate (ITR) of a nine-target SSVEP-based BCI in VR environment. Experiments were repeated on three different frequency configurations of pattern-reversal checkerboard stimuli arranged in a 3 × 3 matrix. When a user was staring at one of the nine visual stimuli, the column containing the target stimulus was first identified based on the user’s horizontal eye movement direction (left, middle, or right) classified using horizontal EOG recorded from a pair of electrodes that can be readily incorporated with any existing VR-HMD systems. Note that the EOG can be recorded using the same amplifier for recording SSVEP, unlike the VOG system. Then, the target visual stimulus was identified among the three visual stimuli vertically arranged in the selected column using the extension of multivariate synchronization index (EMSI) algorithm, one of the widely used SSVEP detection algorithms. In our experiments with 20 participants wearing a commercial VR-HMD system, it was shown that both the accuracy and ITR of the proposed hybrid BCI were significantly increased compared to those of the traditional SSVEP-based BCI in VR environment.

EEG Based Automated Detection of Six Different Eye Movement Conditions for Implementation in Personal Assistive Application

Different forms of human expressions are now being extensively used in present-day human–machine interfaces to provide assistive support to the elderly and disabled population. Depending on the subject condition, expressions conveyed in terms of eye movements are often found to provide the most efficient way of communication. Nowadays, standard Electroencephalogram (EEG) based arrangements, normally used to analyze neurological states are also being adopted for the detection of eye movements. Although, EEG-based recent state-of-the-arts researches are lagging as the majority of the works either detects eye movements in a lesser direction or uses a higher feature dimension with limited classification accuracy. In this study, a robust, simple and automated algorithm is proposed that exploits the analysis of the EEG signal to classify eye movements in six different directions. The algorithm uses discrete wavelet transformation to denoise the EEG signals acquired from six different leads. Then, from the reconstructed wavelet coefficients of each lead, two features are extracted and combined to form a binary feature map. The obtained binary feature map itself facilitates distinct visual classification of the eye movements. Finally, a unique value generated from the calculated weighted sum of the binary map is used to classify six different types of eye movements via a threshold-based classification technique. The algorithm presents high average accuracy, sensitivity, specificity of 95.85%, 95.83% and 95.83% respectively, using a single value only. Compared to other state-of-the-art methods, the adopted unique binarization methodology and the obtained results indicate the immense potential of the proposed algorithm to be implemented in personal assistive applications.

Basic framework of the vestibulo-ocular reflex.

Direct and indirect pathways mediating the vestibulo-ocular reflexes, considering contributions made by the vestibular commissure, cerebellum, and vestibular efferent neurons are reviewed. This background leads to a detailed treatment of three-dimensional aspects of the vestibulo-ocular reflex, comparing the planes of the labyrinthine semicircular canals with the pulling directions of the extraocular muscles. By applying matrix algebra and rotational vectors, Robinson provides insights into the comparative anatomy of the vestibular system in different species, how central circuits process raw vestibular signals in three dimensions, and how the directions of eye movement caused by brainstem and cerebellar lesions can be explained.

Measuring cognitive load using eye tracking technology in learning Content

The objective of this study is to determine the effect of a cognitive process called Spatial Contiguity on students engaged in a learning activity. In organizing observed information, cognitive processes beyond attention influence the direction and duration of eye movements. Four volunteers participated in a pilot experiment developed using Eye-tracking to measure cognitive processing of stimuli from learning materials presented in two different formats. Eye-tracking is a tool used to record the eye movements of subjects while performing tasks. The results showed statistically significant differences in cognitive processing measures based on the way learning content is presented.

Anisotropic silver nanowire dielectric composites for self-healable triboelectric sensors with multi-directional tactile sensitivity

Self-healable wearable devices with tactile directional sensitivity have attracted significant attention due to their damage-free sustainable and precise detection of directional body motions in various applications such as hand gesture monitoring and electrooculography (EOG) sensors. In this study, a self-powered triboelectric sensor with tactile directional sensitivity and self-healing property based on the dielectric multilayers of aligned silver nanowire (AgNW) composite gel sandwiched between self-healable polymers has been developed. Its output performances are superior to those of similar devices without the AgNW gel due to the stronger interfacial polarization of the dielectric multilayers and charge trapping by AgNWs. Moreover, the aligned AgNW composite gel possesses anisotropic dielectric properties, which enhances the contact direction-sensitive triboelectric performance of the sensor during rolling or rubbing motions. The fabricated sensor can be used in wearable EOG and electrocardiography applications because of the ability to monitor extremely weak blood pulse waves in the temporal artery around the human eye, while its directional force sensitivity allows differentiating eight eye movement directions. The successful use of the multilayered structure containing the self-healable and aligned AgNW composite gel as a triboelectric dielectric material opens a new avenue for designing self-healable wearable sensors with tactile directional sensitivity.

Development of a Smart Sleep Mask with Multiple Sensors.

In this work, we demonstrated a Smart Sleep Mask with several integrated physiological sensors such as 3-axis accelerometers, respiratory acoustic sensor, and an eye movement sensor. In particular, using infrared optical sensors, eye movement frequency, direction, and amplitude can be directly monitored and recorded during sleep sessions. We also developed a mobile app for data storage, signal processing and data analytics. Aggregation of these signals from a single wearable device may offer ease of use and more insights for sleep monitoring and REM sleep assessment. The user-friendly mask design can enable at-home use applications in the studies of digital biomarkers for sleep disorder related neurodegenerative diseases. Examples include REM Sleep Behavior Disorder, epilepsy event detection and stroke induced facial and eye movement disorder.Clinical Relevance-Many diseases such as stroke, epilepsy, and Parkinson's disease can cause significant abnormal events during sleep or are associated with sleep disorder. A smart sleep mask may serve as a simple platform to provide various physiological signals and generate clinical meaningful insights by revealing the neurological activities during various sleep stages.

Ergonomics analysis based on intention inference

Traditional intention inference methods rely solely on EEG, eye movement or tactile feedback, and the recognition rate is low. To improve the accuracy of a pilot’s intention recognition, a human-computer interaction intention inference method is proposed in this paper with the fusion of EEG, eye movement and tactile feedback. Firstly, EEG signals are collected near the frontal lobe of the human brain to extract features, which includes eight channels, i.e., AF7, F7, FT7, T7, AF8, F8, FT8, and T8. Secondly, the signal datas are preprocessed by baseline removal, normalization, and least-squares noise reduction. Thirdly, the support vector machine (SVM) is applied to carry out multiple binary classifications of the eye movement direction. Finally, the 8-direction recognition of the eye movement direction is realized through data fusion. Experimental results have shown that the accuracy of classification with the proposed method can reach 75.77%, 76.7%, 83.38%, 83.64%, 60.49%,60.93%, 66.03% and 64.49%, respectively. Compared with traditional methods, the classification accuracy and the realization process of the proposed algorithm are higher and simpler. The feasibility and effectiveness of EEG signals are further verified to identify eye movement directions for intention recognition.

Piecewise empirical mode Bayesian estimation – A new method to denoise electrooculograms

Electrooculograms (EOG) indicate the magnitude and direction of eye movements. The EOG contain several types of noise and interference, which makes their analysis difficult. The existing denoising methods fail to preserve certain eye movement signatures, mainly in regions where there is a change of eye movement type, for e.g., fixation to saccades. Hence, the prime aim of the study is to denoise EOG signals, while preserving the signatures in such conditions. In this paper, we propose a new denoising method that addresses the aforementioned issue. In this algorithm, we first divide the signal into sub-signals based on the region of changes in the eye movement type. For each sub-signal, we separate the intrinsic mode oscillations from noise. Finally, the filtered sub-signals are concatenated to form the reconstructed signal. The reconstruction artifacts at the joining region are removed by Bayesian estimation. We name this algorithm as the Piecewise Empirical Mode Bayesian Estimation (PEMBE). We demonstrate that PEMBE is more effective in the denoising process as compared to the existing methods, as tested on the Physiosig EOG. The PEMBE is also found to be useful in the removal of the baseline wander.

Effects of cognitive tasks on eye movements.

Eye movements are guided by vestibular and visual information. The vestibulo-ocular knowledge of the vestibule includes eye movements in the opposite direction of head movements. This study investigated the effect of auditory "what" and "where" tasks on the visual "where" to evaluate the effects of cognitive tasks on eye movements. All subjects underwent testing with videonystagmography (VNG). The VNG battery of saccade and tracking oculomotor tests were performed. The study design was planned in three stages: (1) without any cognitive tasks, eye movements were recorded with VNG, (2) participants were asked to tell "what" sound they heard during VNG recording, and (3) the subject was asked to tell from what direction, or "where," the sound had come from during VNG recording. Providing individuals with a cognitive task changed the test results negatively in all parameters. Giving cognitive tasks to individuals spoils all VNG parameters. The VNG oculomotor tests are affected by the dual tasks. Daily tasks such as sound object localization and recognition have a significant effect on scanning the visual environment in daily life such as during driving or walking in a crowded environment.

Tracking Eye Movements During Sleep in Mice.

Eye movement is not only for adjusting the visual field and maintaining the stability of visual information on the retina, but also provides an external manifestation of the cognitive status of the brain. Recent studies showed similarity in eye movement patterns between wakefulness and rapid eye movement (REM) sleep, indicating that the brain status of REM sleep likely resembles that of awake status. REM sleep in humans could be divided into phasic REM and tonic REM sleep according to the difference in eye movement frequencies. Mice are the most commonly used animal model for studying neuronal and molecular mechanisms underlying sleep. However, there was a lack of details for eye movement patterns during REM sleep, hence it remains unknown whether REM sleep can be further divided into different stages in mice. Here we developed a device combining electroencephalogram (EEG), electromyogram (EMG) as well as eye movements recording in mice to study the eye movement patterns during sleep. We implanted a magnet beneath the conjunctiva of eye and tracked eye movements using a magnetic sensor. The magnetic signals showed strong correlation with video-oculography in head-fixed mice, indicating that the magnetic signals reflect the direction and magnitude of eye movement. We also found that the magnet implanted beneath the conjunctiva exhibited good biocompatibility. Finally, we examined eye movement in sleep–wake cycle, and discriminated tonic REM and phasic REM according to the frequency of eye movements, finding that compared to tonic REM, phasic REM exhibited higher oscillation power at 0.50 Hz, and lower oscillation power at 1.50–7.25 Hz and 9.50–12.00 Hz. Our device allowed to simultaneously record EEG, EMG, and eye movements during sleep and wakefulness, providing a convenient and high temporal-spatial resolution tool for studying eye movements in sleep and other researches in mice.

Postural balance and oculomotor control are influenced by neck kinaesthetic functions in elite ice hockey players

BackgroundResearch indicates that neck kinaesthetic awareness plays an important role in oculomotor and balance control, however the relationship has not been studied in athletes. As performance in ice hockey demands visual perception acuity during skating, while constantly shifting between unilateral and bilateral stances in sports specific posture more in-depth relationship should be studied. Research questionWhat is the relationship between neck kinaesthetic awareness, postural balance and eye movement control in professional ice hockey players and non-trained individuals? MethodsIn this observational study, centre-of-pressure was measured using force plate in twenty-eight hockey players and thirty non-trained participants during different stances in eyes-opened and eyes-closed conditions. Butterfly test and Head-to-Neutral Relocation test were performed to assess neck kinaesthesia. Horizontal smooth pursuit eye movements were measured using video-oculography. Linear regression was used to determine the relationship between neck kinaesthesia, body sway and oculomotor control. ResultsTime-on-target in Butterfly test was able to predict low to medium proportions of variance in amplitude and velocity parameters for single leg stances in hockey players (R2 = .220–.698). Head-to-Neutral Relocation test was able to predict low to medium proportion of variance in average eye movement velocity during first 100 milliseconds of smooth pursuit initiation for both groups (R2 = .262–.541). SignificanceFindings from our study suggest that cervical spine afferent input plays an important role in maintaining unilateral postural balance in hockey players, with less evidence presented in controls. Sports specific posture or upper body loading could lead to adaptations in neck proprioception, not frequently considered when searching for balance related injury risk factors or performance deficits. Our study suggests, that in addition to balance control, neck kinaesthesia can also affect oculomotor performance which was present in both groups. This is especially evident when initiating changes in eye movement direction.

A Fast and Effective System for Analysis of Optokinetic Waveforms with a Low-Cost Eye Tracking Device.

Optokinetic nystagmus (OKN) is an involuntary eye movement induced by motion of a large proportion of the visual field. It consists of a “slow phase (SP)” with eye movements in the same direction as the movement of the pattern and a “fast phase (FP)” with saccadic eye movements in the opposite direction. Study of OKN can reveal valuable information in ophthalmology, neurology and psychology. However, the current commercially available high-resolution and research-grade eye tracker is usually expensive. Methods & Results: We developed a novel fast and effective system combined with a low-cost eye tracking device to accurately quantitatively measure OKN eye movement. Conclusions: The experimental results indicate that the proposed method achieves fast and promising results in comparisons with several traditional approaches.

Visuomotor Transformations Are Modulated by Focused Ultrasound over Frontal Eye Field

Neuromodulation with focused ultrasound (FUS) is being widely explored as a non-invasive tool to stimulate focal brain regions because of its superior spatial resolution and coverage compared with other neuromodulation methods. The precise effects of FUS stimulation on specific regions of the brain are not yet fully understood. Here, we characterized the behavioral effects of FUS stimulation directly applied through a craniotomy over the macaque frontal eye field (FEF). In macaque monkeys making directed eye movements to perform visual search tasks with direct or arbitrary responses, focused ultrasound was applied through a craniotomy over the FEF. Saccade response times (RTs) and error rates were determined for trials without or with FUS stimulation with pulses at a peak negative pressure of either 250 or 425 kPa. Both RTs and error rates were affected by FUS. Responses toward a target located contralateral to the FUS stimulation were approximately 3 ms slower in the presence of FUS in both monkeys studied, while only one exhibited a slowing of responses for ipsilateral targets. Error rates were lower in one monkey in this study. In another search task requiring making eye movements toward a target (pro-saccades) or in the opposite direction (anti-saccades), the RT for pro-saccades increased in the presence of FUS stimulation. Our results indicate the effectiveness of FUS to modulate saccadic responses when stimulating FEF in awake, behaving non-human primates.