Eye Movement Detection Research Articles

Re-examining our evolutionary propensities toward snakes: Insights from children's inattentional blindness.

The purpose of this study was to investigate the divergent viewpoints regarding fearful stimuli in young children by analyzing variations in eye movement tracking and detection rates when confronted with the sudden appearance of either snakes or lizards. A total of 137 five to six-year-old Chinese children (43.8% male) participated in the study, which utilized the inattentional blindness paradigm. The results indicated that young children did not display any attentional bias toward snakes when compared with lizards, as evidenced by their detection rates and eye movement tracking. Interestingly, the children fixated on lizards earlier than snakes. These findings suggest that the notion of an evolution bias toward ancestral threats may not be as credible as previously believed.

Research on an Eye Control Method Based on the Fusion of Facial Expression and Gaze Intention Recognition

With the deep integration of psychology and artificial intelligence technology and other related technologies, eye control technology has achieved certain results at the practical application level. However, it is found that the accuracy of the current single-modal eye control technology is still not high, which is mainly caused by the inaccurate eye movement detection caused by the high randomness of eye movements in the process of human–computer interaction. Therefore, this study will propose an intent recognition method that fuses facial expressions and eye movement information and expects to complete an eye control method based on the fusion of facial expression and eye movement information based on the multimodal intent recognition dataset, including facial expressions and eye movement information constructed in this study. Based on the self-attention fusion strategy, the fused features are calculated, and the multi-layer perceptron is used to classify the fused features, so as to realize the mutual attention between different features, and improve the accuracy of intention recognition by enhancing the weight of effective features in a targeted manner. In order to solve the problem of inaccurate eye movement detection, an improved YOLOv5 model was proposed, and the accuracy of the model detection was improved by adding two strategies: a small target layer and a CA attention mechanism. At the same time, the corresponding eye movement behavior discrimination algorithm was combined for each eye movement action to realize the output of eye behavior instructions. Finally, the experimental verification of the eye–computer interaction scheme combining the intention recognition model and the eye movement detection model showed that the accuracy of the eye-controlled manipulator to perform various tasks could reach more than 95 percent based on this scheme.

Computational approaches to apply the String Edit Algorithm to create accurate visual scan paths

Eye movement detection algorithms (e.g., I-VT) require the selection of thresholds to identify eye fixations and saccadic movements from gaze data. The choice of threshold is important, as thresholds too low or large may fail to accurately identify eye fixations and saccades. An inaccurate threshold might also affect the resulting visual scan path, the time-ordered sequence of eye fixations and saccades, carried out by the participant. Commonly used approaches to evaluate threshold accuracy can be manually laborious, or require information about the expected visual scan paths of participants, which might not be available. To address this issue, we propose two different computational approaches, labeled as “between-participants comparisons” and “within-participants comparisons.” The approaches were evaluated using the open-source Gazebase dataset, which contained a bullseye-target tracking task, where participants were instructed to follow the movements of a bullseye-target. The predetermined path of the bullseye-target enabled us to evaluate our proposed approaches against the expected visual scan path. The approaches identified threshold values (220°/s and 210°/s) that were 83% similar to the expected visual scan path, outperforming a 30°/s benchmark threshold (41.5%). These methods might assist researchers in identifying accurate threshold values for the I-VT algorithm or potentially other eye movement detection algorithms.

EOG Signal Processor: An SVM-based Multiclass Classifier to Detect Eye Movements

EOG Signal Processor: An SVM-based Multiclass Classifier to Detect Eye Movements

Skip-AttSeqNet: Leveraging skip connection and attention-driven Seq2seq model to enhance eye movement event detection in Parkinson’s disease

To address the limitations of traditional algorithms in detecting eye movement events, particularly in Parkinson’s disease (PD) patients, this study introduces Skip-AttSeqNet. It presents an innovative approach combining skip-connected, one-dimensional convolutional neural networks with an attention-enhanced, bidirectional long short-term memory network. This hybrid architecture significantly advances smooth pursuit (SP) event detection, as evidenced by its performance on both the GazeCom dataset and a unique dataset of PD patient eye movements. Key innovations in this work include the utilization of skip connections and attention mechanisms, along with optimized training–validation set division, collectively enhancing the model’s accuracy while mitigating overfitting. Skip-AttSeqNet outperforms existing algorithms, achieving a 3.2% higher sample-level F1 score and a notable 6.2% increase in event-level F1 scores for SP detection. Furthermore, we established a smooth-pursuit experimental paradigm and identified significant differences in saccade and SP features between PD patients and healthy older adults through statistical analysis using the Mann–Whitney test. These findings underscore the potential of eye movement metrics as biomarkers for PD, thereby not only strengthening PD diagnosis but also enriching the intersection of computer vision and biomedical research domains.

Analysis of Electrooculogram in Detecting Eye Movements Associated with Brain Injury

Electrooculogram (EOG) has been a valuable clinical neurophysiology tool in the past five decades of the twentieth century. It facilitated understanding more about eye movement, which is clinically useful in identifying the neural substrate disrupted due to brain injuries. This is vital since accurate measurement of neural injuries has a direct bearing on a patient’s life. A number of neurological investigations, including neurological assessment and diagnosis, are done based on measuring eye movements. This essay focuses on using EOG technology and its benefits in identifying limitations of using physiological indices in individuals because of measurable ocular pathophysiological parameters. In the current essay, the major objective will be to discuss the EOG features, the EOG record in healthy individuals, and the relationship of EOG recordings to affected individuals following a neurological event. The eye-integrated EOG recorded from the front part of the face measures eye position change and shows an exponential decay of voltage due to its design, eye physiology, and the removal of corneal function slowly from the corneal electrode. This makes it the best choice to provide an eye position record with high accuracy that can be made available easily for clinicians. The rapid recordings obtained using transcranial electromagnetic stimulation could demonstrate that EOG has the potential to provide high pre-surgical planning utilities for brain tumor removal. This technology is also best for identifying physiological variability within an individual; the same concept can be extended to study brain injuries that trigger physiological changes in eye muscles. In effect, it potentially becomes a tool for personalized care wherein severity estimation for brain injuries would be based on measuring ocular function rather than the nature of the injury. Given its clinical potential, the present essay will review EOG technologies to assess eye pixel movement, its potential for clinical evaluation, physiological basis, and the research evidence regarding changes in EOG in affected individuals with brain injury. In this essay, the long-lasting and short-lasting EOG changes are presented as case studies of individuals diagnosed with PTA, hence demonstrating its clinical utility.

PSPN: Pseudo-Siamese Pyramid Network for multimodal emotion analysis.

Emotion recognition plays an important role in human life and healthcare. The EEG has been extensively researched as an objective indicator of intense emotions. However, current existing methods lack sufficient analysis of shallow and deep EEG features. In addition, human emotions are complex and variable, making it difficult to comprehensively represent emotions using a single-modal signal. As a signal associated with gaze tracking and eye movement detection, Eye-related signals provide various forms of supplementary information for multimodal emotion analysis. Therefore, we propose a Pseudo-Siamese Pyramid Network (PSPN) for multimodal emotion analysis. The PSPN model employs a Depthwise Separable Convolutional Pyramid (DSCP) to extract and integrate intrinsic emotional features at various levels and scales from EEG signals. Simultaneously, we utilize a fully connected subnetwork to extract the external emotional features from eye-related signals. Finally, we introduce a Pseudo-Siamese network that integrates a flexible cross-modal dual-branch subnetwork to collaboratively utilize EEG emotional features and eye-related behavioral features, achieving consistency and complementarity in multimodal emotion recognition. For evaluation, we conducted experiments on the DEAP and SEED-IV public datasets. The experimental results demonstrate that multimodal fusion significantly improves the accuracy of emotion recognition compared to single-modal approaches. Our PSPN model achieved the best accuracy of 96.02% and 96.45% on the valence and arousal dimensions of the DEAP dataset, and 77.81% on the SEED-IV dataset, respectively. Our code link is: https://github.com/Yinyanyan003/PSPN.git.

Frequency-encoded eye tracking smart contact lens for human–machine interaction

Eye tracking techniques enable high-efficient, natural, and effortless human-machine interaction by detecting users’ eye movements and decoding their attention and intentions. Here, a miniature, imperceptible, and biocompatible smart contact lens is proposed for in situ eye tracking and wireless eye-machine interaction. Employing the frequency encoding strategy, the chip-free and battery-free lens successes in detecting eye movement and closure. Using a time-sequential eye tracking algorithm, the lens has a great angular accuracy of <0.5°, which is even less than the vision range of central fovea. Multiple eye-machine interaction applications, such as eye-drawing, Gluttonous Snake game, web interaction, pan-tilt-zoom camera control, and robot vehicle control, are demonstrated on the eye movement model and in vivo rabbit. Furthermore, comprehensive biocompatibility tests are implemented, demonstrating low cytotoxicity and low eye irritation. Thus, the contact lens is expected to enrich approaches of eye tracking techniques and promote the development of human-machine interaction technology.

Eye Movement Detection Using Electrooculography and Machine Learning in Cardiac Arrest Patients (P10-2.006)

Eye Movement Detection Using Electrooculography and Machine Learning in Cardiac Arrest Patients (P10-2.006)

Human Eye Activity Monitoring Using Continuous Wave Doppler Radar: A Feasibility Study.

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This article proposes a novel eye movement monitoring method called continuous wave doppler oculogram (cDOG). Unlike the conventional EOG-based eye movement monitoring methods, cDOG based on continuous wave doppler radar sensor (cDRS) can remotely measure human eye activity without placing electrodes on the head. To verify the feasibility of using cDOG for eye movement monitoring, we first theoretically analyzed the association between the radar signal and the corresponding eye movements measured with EOG. Afterward, we conducted an experiment to compare EOG and cDOG measurements under the conditions of eyes closure and opening. In addition, different eye movement states were considered, including right-left saccade, up-down saccade, eye-blink, and fixation. Several representative time domain and frequency domain features obtained from cDOG and from EOG were compared in these states, allowing us to demonstrate the feasibility of using cDOG for monitoring eye movements. The experimental results show that there is a correlation between cDOG and EOG in the time and frequency domain features, the average time error of single eye movement is less than 280.5 ms, and the accuracy of cDOG in eye movement detection is higher than 92.35%, when the distance between the cDRS and the face is 10 cm and eyes is facing the radar directly.

Event detection and classification from multimodal time series with application to neural data

The detection of events in time-series data is a common signal-processing problem. When the data can be modeled as a known template signal with an unknown delay in Gaussian noise, detection of the template signal can be done with a traditional matched filter. However, in many applications, the event of interest is represented in multimodal data consisting of both Gaussian and point-process time series. Neuroscience experiments, for example, can simultaneously record multimodal neural signals such as local field potentials (LFPs), which can be modeled as Gaussian, and neuronal spikes, which can be modeled as point processes. Currently, no method exists for event detection from such multimodal data, and as such our objective in this work is to develop a method to meet this need. Here we address this challenge by developing the multimodal event detector (MED) algorithm which simultaneously estimates event times and classes. To do this, we write a multimodal likelihood function for Gaussian and point-process observations and derive the associated maximum likelihood estimator of simultaneous event times and classes. We additionally introduce a cross-modal scaling parameter to account for model mismatch in real datasets. We validate this method in extensive simulations as well as in a neural spike-LFP dataset recorded during an eye-movement task, where the events of interest are eye movements with unknown times and directions. We show that the MED can successfully detect eye movement onset and classify eye movement direction. Further, the MED successfully combines information across data modalities, with multimodal performance exceeding unimodal performance. This method can facilitate applications such as the discovery of latent events in multimodal neural population activity and the development of brain-computer interfaces for naturalistic settings without constrained tasks or prior knowledge of event times.

Research Progress of Eye Movement Analyses and its Detection Algorithms in Alzheimer's Disease.

Alzheimer's disease (AD) has been considered one of the most challenging forms of dementia. The earlier the people are diagnosed with AD, the easier it is for doctors to find a treatment. Based on the previous literature summarizing the research results on the relationship between eye movement and AD before 2013, this paper reviewed 34 original eye movements research papers only closely related to AD published in the past ten years and pointed out that the prosaccade (4 papers) and antisaccade (5 papers) tasks, reading tasks (3 papers), visual search tasks (3 papers) are still the research objects of many researchers, Some researchers have looked at King-Devick tasks (2 papers), reading tasks (3 papers) and special tasks (8 papers), and began to use combinations of different saccade tasks to detect the relationship between eye movement and AD, which had not been done before. These reflect the diversity of eye movement tasks and the complexity and difficulty of the relationship between eye movement and AD. On this basis, the current processing and analysis methods of eye movement datasets are analyzed and discussed in detail, and we note that certain key data that may be especially important for the early diagnosis of AD by using eye movement studies cannot be miss-classified as noise and removed. Finally, we note that the development of methods that can accurately denoise and classify and quickly process massive eye movement data is quite significant for detecting eye movements in early diagnosis of AD.

Sleep Apnea and Rapid Eye Movement Detection using ResNet-50 and Gradient Boost

Sleep Apnea and Rapid Eye Movement Detection using ResNet-50 and Gradient Boost

Fast eye centre localization using combined unsupervised technics

Eye movements offer precious information about persons? state. Video surveillance, marketing, driver fatigue as well as medical diagnosis assistance applications manage eye behavior. We propose a new method for efficiently detecting eye movement. In this paper, we combine circle eye model with eye feature method to improve the accuracy. A set of detectors estimate the eyes centers to increase the localization rate. As a pre-processing stage, the mean of the edges yields the center of the two eye regions. Image treatment operations reduce the ROI. A Circle Hough Transform (CHT) algorithm is adopted in a modified version as a detector to find the circle eye in the image; the circle center found represents the eye's pupil estimation. We introduced the Maximally Stable Extremal Region (MSER) as a second detector, which has never been used for eye localization. Invariant to continuous geometric transformations and affine intensity changes and detected at several scales, MSERs efficiently detect regions of interest, in our case eye regions, and precisely, their centers. Ellipses fit MSERs, and their centroid estimation match eyes center. We demonstrate that the true eye centers can be found by combining these methods. The validation of the proposed method is performed on a very challenging BioID base. The proposed approach compares well with existing state-of-the-art techniques and achieves an accuracy of 82.53% on the BioID database when the normalized error is less than 0.05, without prior knowledge or any learning model.

Real-time continuous EOG-based gaze angle estimation with baseline drift compensation under non-stationary head conditions

Objective:This work addresses an impractical and unnatural constraint that has been generally enforced in state-of-the-art electrooculography (EOG)-based gaze estimation methods, that of maintaining a stationary head pose and position. Specifically, this work proposes an EOG-based gaze angle (GA) estimation method that accommodates natural variations in the user’s head pose and position. The EOG data collected under non-stationary head conditions, which was used in this work to validate the proposed method, is also being made publicly available. Methods:This work generalises a two-eye verging gaze geometrical model to cater for arbitrary head poses and positions, and also models the dynamics of the vestibulo-ocular reflex (VOR), which refers to the eye-head coordination that normally takes place during gaze shifts under unrestrained head conditions. These methods are validated by incorporating them within a published multiple-model framework for GA estimation. Results:When applied to short EOG data segments, a horizontal and vertical GA estimation error of 1.85 ± 0.51° and 2.19 ± 0.62°, respectively, and an eye movement detection and labelling F-score close to 90% were obtained. These results are comparable to those reported previously under stationary head conditions. Conclusion:This work demonstrates that accurate GA estimation and eye movement detection and labelling can be achieved using EOG signals, even when the user’s head is not stationary. Significance:This work eliminates the need for users to maintain a stationary head pose and position, a common constraint in the field, thus introducing an EOG-based GA estimation method that allows users to move their heads naturally.

EOG Based Eye Blink Detection using VB GUI for Eye Writing Applications

Usage of bio-signals from Electro-Oculo-Gram (EOG) in Human Computer Interaction (HCI) systems is one of the burgeoning study areas as of now. In order to provide support for physically challenged persons (or keypad Contactless HCI) in operating basic computer functions without making any physical touch with the computer keypad or mouse, we propose a framework utilization concept that changes movement of cursor on the computer screen using virtual keypad based on Visual Basic Graphical User Interface (VB GUI) with the eye movement detection through webcam of the system for different computer functions. In this work, VB GUI platform is used for a web browsing application and corresponding platform operation steps have been included. With the VB GUI, the basic cursor movements in the directions of left, right, up, and down to access the web links on the web page which is treated as eye writing in the concept of human computer interaction. Similarly, we can access different computer Apps such as MS-Doc, XL sheets, Audio and video files etc. Further, this paper focus is on prototype development of different home automation system applications.

Fully Automated Segmentation of Human Eyeball Using Three-Dimensional U-Net in T2 Magnetic Resonance Imaging.

To develop and validate a fully automated deep-learning-based tool for segmentation of the human eyeball using a three-dimensional (3D) U-Net, compare its performance to semiautomatic segmentation ground truth and a two-dimensional (2D) U-Net, and analyze age and sex differences in eyeball volume, as well as gaze-dependent volume consistency in normal subjects. We retrospectively collected 474 magnetic resonance imaging (MRI) scans, including different gazing scans, from 119 patients. A 10-fold cross-validation was applied to separate the dataset into training, test, and validation sets for both the 3D U-Net and 2D U-Net. Performance accuracy was measured using four quantitative metrics compared to the ground truth, and Bland-Altman plot analysis was conducted. Age and sex differences in eyeball volume and variability in eyeball volume differences across gazing directions were analyzed. The 3D U-Net outperformed the 2D U-Net with mean accuracy scores >0.95, showing acceptable agreement in the Bland-Altman plot analysis despite a tendency for slight overestimation (mean difference = -0.172 cm³). Significant sex differences and age effects on eyeball volume were observed for both methods (P < 0.05). No significant volume differences were found between the segmentation methods or within each method for the different gazing directions. Significant differences in performance accuracy were identified among the five gazing directions, with the upward direction showing a notably lower performance. Our study demonstrated the effectiveness of 3D U-Net human eyeball volume segmentation using T2-weighted MRI. The robustness and reliability of 3D U-Net across diverse populations and gaze directions support enhanced ophthalmic diagnosis and treatment strategies. Our findings demonstrate the feasibility of using the proposed 3D U-Net model for the automatic segmentation of the human eyeball, with potential applications in various ophthalmic research fields that require the analysis of 3D geometric eye globe shapes or eye movement detection.

The connection between dreaming, the brain and mental functioning: where are we now?

Dreaming has always aroused our curiosity. Theories as to the cause and function of dreams have been described since the beginning of recorded history (George 2020). In the late 19th century, experimental psychologists and psychologically-minded researchers from other disciplines made important methodological contibutions, emptical observations, and conceptual developments to the study of dreams (e.g., Jastrow, 1888; Manacéïne, 1897; De Sanctis, 1899; Vold, 1897). At the end of the 19th century, Mary Whiton Calkins and her female students made pioneering advancements in the psychological science of dreams (Calkins 1893; Weed et al. 1896). Freud’s psychoanalytic theory soon overshadowed these groundbreaking empirical works as the interpretation of dream content and their presumed reflections of the unconscious mind became the focus. The detection of rapid eye movements during sleep in 1953 and the suggestion that dreams occurred exclusively during this newly defined sleep state electrified the field of dream research (Aserinsky and Kleitman 1953; Dement and Kleitman 1957). Although eye movements (Ladd, 1892), increased brain pulsations (Mosso, 1881), and electroencephalographic patterns (Loomis et al., 1937; Davis et al., 1938) had been previously argued to empirically correspond to dreaming, this discovery catalyzed the first “Meeting of Researchers in the Field of EEG and Dreams” at the University of Chicago in 1961 organized by psychologist Allan Rechtschaffen (Association for the Psychophysiological Study of Sleep Records). Renamed the Annual Meeting of the Association of the Psychophysiological Study of Sleep in subsequent years, these early meetings consisted principally of psychiatrists and psychologists, most of whom with interests in dream research. Among them, John Antrobus, Rosalind Cartwright, G. William Domhoff, David Foulkes, Donald R. Goodenough, Calvin S. Hall, Ernest Hartmann and Joe Kamiya, made valuable contributions to our understanding of dreaming through decades of psychological research (Antrobus, 1992; Domhoff and Kamyia, 1964; Ellman and Antrobus, 1991; Foulkes, 1966, 1985; Goodenough et al., 1965; Hall and Van de Castle, 1966; Hartmann, 2010). While David Foulkes tirelessly advocated for his vision of a descriptive and explanitory dream psychology, Rosalind Cartwright developed an applied vision for the field outlining over 100 dream-related questions that remain pertinent to sleep psychology (Cartwright 1977, 1978, 2010). With the rise of sleep medicine and the vicissitudes of funding, dream research drifted to the fringe of sleep research by the end of the 1980s (Foulkes 1996). Nevertheless, dreaming remains a central topic of sleep psychology, and many questions remain to be answered.

Research on pathfinding cognition in complex underground public space utilizing eye movement and EEG synchronous detection

The complex underground public space brings challenge on pathfinding cognition. This research took Luoxiong Road Station in Wuhan as experimental site, using eye movement and electroencephalogram (EEG) synchronous detection, through pathway analysis and space-time fault method, exploring relationship between pathfinding cognition and complex underground public space. It was found that pathfinding habits and pathfinding decision-making were mainly affected by personal factors and environmental factors, respectively. Pathfinders influenced by personal factors, had behaviors such as left-walking or right-walking tendency, and the ratio of brain wave β/α was low (impression ratio ≤1). Pathfinders influenced by environmental factors, interested in the other side situation, vertical traffic and so on, and the ratio of brain wave β/α was high (impression ratio >1), with the frontal and parietal occipital lobe regions of brain being more active. These conclusions provided theoretical and data supplements for underground rail transit environment optimizing design in future.

Classifying blinking and winking EOG signals using statistical analysis and LSTM algorithm

Detection of eye movement types whether the movement of the eye itself or blinking has attracted a lot of recent research. In this paper, one method to detect the type of wink or blink produced by the eye is scrutinized and another method is proposed. We discuss what statistical analysis can teach us about detection of eye movement and propose a method based on long short-term memory (LSTM) networks to detect those types. The statistical analysis is composed of two main steps, namely calculation of the first derivative followed by a digitization step. According to the values of the digitized curve and the duration of the signal, the type of the signal is detected. The success rate reached 86.6% in detection of the movement of the eye when those volunteers are not trained on using our system. However, when they are trained, the detection success rate reached 93.3%. The statistical analysis succeeds in achieving detection of all types of eye movement except one type which is the non-intentional blinking. Although rate of success achieved is high, but as the number of people using this system increases, the error in detection increases that is because it is fixed and not adaptive to changes. However; we learnt from statistical analysis that the first derivative is a very important feature to classify the type of an EOG signal. Next, we propose using the LSTM network to classify EOG signals. The effect of using the first derivative as a feature for identifying the type of EOG signals is discussed. The LSTM algorithm succeeds in detecting the type of EOG signals with a percentage equal to 92% for all types of eye movement.