Eye Movement Detection Research Articles

CyberEye: New Eye-Tracking Interfaces for Assessment and Modulation of Cognitive Functions beyond the Brain.

The emergence of innovative neurotechnologies in global brain projects has accelerated research and clinical applications of BCIs beyond sensory and motor functions. Both invasive and noninvasive sensors are developed to interface with cognitive functions engaged in thinking, communication, or remembering. The detection of eye movements by a camera offers a particularly attractive external sensor for computer interfaces to monitor, assess, and control these higher brain functions without acquiring signals from the brain. Features of gaze position and pupil dilation can be effectively used to track our attention in healthy mental processes, to enable interaction in disorders of consciousness, or to even predict memory performance in various brain diseases. In this perspective article, we propose the term ‘CyberEye’ to encompass emerging cognitive applications of eye-tracking interfaces for neuroscience research, clinical practice, and the biomedical industry. As CyberEye technologies continue to develop, we expect BCIs to become less dependent on brain activities, to be less invasive, and to thus be more applicable.

FPGA implementation of eye movement detection algorithm

In this paper, we propose an simple and efficient VLSI hardware architecture is implemented for eye movement detection. For Eye movement detection reading activity Electrooculography (EOG) signal is considered. Here, for denoising the noisy EOG signal efficient FIR filter and for decomposition of denoised EOG signal an efficient Haar wavelet transform architecture is used respectively. The modified VLSI hardware architecture method detected the saccade (left movement of eye and right movement of eye) and blink efficiently. The hardware architecture of the eye movement detection algorithm functionality is verified by using Xilinx System Generator hardware co-simulation tool. The eye movement detection algorithm is implemented on the ZedBoard FPGA using Xilinx Vivado design suite.

Eye Movement Prediction Based on Adaptive BP Neural Network

This paper uses adaptive BP neural networks to conduct an in-depth examination of eye movements during reading and to predict reading effects. An important component for the implementation of visual tracking systems is the correct detection of eye movement using the actual data or real-world datasets. We propose the identification of three typical types of eye movements, namely, gaze, leap, and smooth navigation, using an adaptive BP neural network-based recognition algorithm for eye movement. This study assesses the BP neural network algorithm using the eye movement tracking sensors. For the experimental environment, four types of eye movement signals were acquired from 10 subjects to perform preliminary processing of the acquired signals. The experimental results demonstrate that the recognition rate of the algorithm provided in this paper can reach up to 97%, which is superior to the commonly used CNN algorithm.

Kontrol Robot Menggunakan Gerakan Mata Berbasis Sinyal Electrooculography (EOG)

Biomedical technology has now been widely adopted as a means of monitoring the human body in real-time. For example, to detect eye movement. In the medical world, eye movement can be used to determine the type of disease. With the application of human-machine interface (HMI) technology, eyeball movement can be developed in the robotics industry as robot navigation. For example, by moving the eyeball left and right, the robot can interpret the eye signal to move left and right. The interaction between the eyeball movement and the robot is of particular concern in this study. This study aimed to design a measuring instrument for eye movement detection using Electrooculography (EOG) techniques to move a wheeled robot. The EOG measuring instrument consisting of an instrument differential amplifier, a low pass filter, and a high pass filter has been applied in this research. The signal generator technique on EOG is carried out by placing electrodes on three sides of the face, namely forehead (G), left horizontal (H-), right horizontal (H +). The experimental results showed a significant difference between the left and right eye movement amplitude signals. This amplitude is used to classify the movement of the robot wheel towards the left and right. The process of sending robot signals and EOG measuring instruments uses Bluetooth HC-05 serial communication. Based on the research results, it is proven that the robot manages to move left and right according to the eyeball movement.

Non-invasive recognition of eye torsion through optical imaging of the iris pattern in ocular proton therapy

The introduction of non-invasive imaging techniques such as MRI imaging for treatment planning and optical eye tracking for in-room eye localization would obviate the requirement of clips implantation for many patients undergoing ocular proton therapy. This study specifically addresses the issue of torsional eye movement detection during patient positioning. Non-invasive detection of eye torsion is performed by measuring the iris pattern rotations using a beams eye view optical camera. When handling images of patients to be treated using proton therapy, a number of additional challenges are encountered, such as changing eye position, pupil dilatation and illumination. A method is proposed to address these extra challenges while also compensating for the effect of cornea distortion in eye torsion computation. The accuracy of the proposed algorithm was evaluated against corresponding measurement of eye torsion using the clips configuration measured on x-ray images. This study involves twenty patients who received ocular proton therapy at Paul Scherrer Institute and it is covered by ethical approval (EKNZ 2019-01987).

A Wearable Brain-Computer Interface Instrument with Aug- Mented Reality-Based Interface for General Applications

In the project we are demonstrating the combined usage Augmented Reality(AR) and brain faced com- puter interface(BI) which can be used to control the robotic acuratorby.Thismethod is more simple and more user friendly. Here brainwave senor will work in its normal setting detecting alpha, beta, and gam- ma signals. These signals are decoded to detect eye movements. These are very limited on its own since the number of combinations possible to make higher andmorecomplextaskpossible.Asasolutiontothis AR is integrated with the BCI application to make control interface more user friendly. Thisapplication can be used in many cases including many robotic anddevicecontrollingcases.HereweuseBCI-ARto detect eye paralysis that can be archive by detecting eyelidmovementofpersonbywearingheadbend.

A Wearable Brain-Computer Interface Instrument with Aug- Mented Reality-Based Interface for General Applications

In the project we are demonstrating the combined usage Augmented Reality(AR) and brain faced com- puter interface(BI) which can be used to control the robotic acurator by. This method is more simple and more user friendly. Here brainwave senor will work in its normal setting detecting alpha, beta, and gam- ma signals. These signals are decoded to detect eye movements. These are very limited on its own since the number of combinations possible to make higher and more complex task possible. Asa solution to this AR is integrated with the BCI application to make control interface more user friendly. This application can be used in many cases including many robotic and device controlling cases. Here we use BCI-AR to detect eye paralysis that can be archive by detecting eye lid movement of person by wearing head bend.

ANFIS fusion algorithm for eye movement recognition via soft multi-functional electronic skin

Eye movement detection has attracted increasing attention in the fields of safety driving, eye motion tracking, psychological assessment and telemedicine. Soft multi-functional electronic skin (SMFES) is designed to collect electrooculogram (EOG), skin temperature and sweat signals simultaneously for eye movement detection. Serpentine structure is adopted to ensure the stretchability of SMFES for satisfying large deformation (>30%) of the soft skin surface. The paper demonstrates that EOG, skin temperature and sweat signals are successfully collected under different eye movements. The feature data from EOG, skin temperature and sweat signals are extracted with different eye movements, and the principal component analysis (PCA) method is adopted to reduce the dimensionality of the feature space. The paper also proposes an intelligent data fusion algorithm for eye movement classification whose input vector is represented by the first three principal components. Adaptive neuro fuzzy inference system (ANFIS) is built to classify and recognize the eye movements (Up, Down, Left, and Right). Furthermore, experiments have demonstrated that ANFIS algorithm achieves 90% recognition accuracy of such eye movements. This work demonstrates that SMFES integrated with data fusion algorithm can successfully solve the eye movement tracking problem, with significant impact in safety driving and wearable electronics.

Determination of Ocular Artifacts in the Clinical EEG Software by a Peripheral Device

The occurrence of physiological artifacts generated by eye movements in electrical brain activity (electroencephalography, EEG) is a well-known problem in clinical practice. In order to increase the accuracy of the detection of eye movements during EEG examination, additional electrooculogram channels (electrooculography, EOG) with a standard PC keyboard are used. The EOG technique is not always comfortable for patients. Another issue is that the use of EOG channels in the EEG examination leads to the prolongation of time required for patient preparation. To solve these problems, we developed a new peripheral device suitable for the indication of common ocular artifacts in EEG. The obtained differences between the recommended methods (i.e., EOG, PC keyboard) and our new device have been presented using RMSE (root mean square error). The presented equipment can be used either during EEG examination or after registration of EEG signals in order to indicate the ocular artifacts. Furthermore, this device is compatible with the EEG software used in clinical practice.

Budgerigars have complex sleep structure similar to that of mammals.

Birds and mammals share specialized forms of sleep including slow wave sleep (SWS) and rapid eye movement sleep (REM), raising the question of why and how specialized sleep evolved. Extensive prior studies concluded that avian sleep lacked many features characteristic of mammalian sleep, and therefore that specialized sleep must have evolved independently in birds and mammals. This has been challenged by evidence of more complex sleep in multiple songbird species. To extend this analysis beyond songbirds, we examined a species of parrot, the sister taxon to songbirds. We implanted adult budgerigars (Melopsittacus undulatus) with electroencephalogram (EEG) and electrooculogram (EOG) electrodes to evaluate sleep architecture, and video monitored birds during sleep. Sleep was scored with manual and automated techniques, including automated detection of slow waves and eye movements. This can help define a new standard for how to score sleep in birds. Budgerigars exhibited consolidated sleep, a pattern also observed in songbirds, and many mammalian species, including humans. We found that REM constituted 26.5% of total sleep, comparable to humans and an order of magnitude greater than previously reported. Although we observed no spindles, we found a clear state of intermediate sleep (IS) similar to non-REM (NREM) stage 2. Across the night, SWS decreased and REM increased, as observed in mammals and songbirds. Slow wave activity (SWA) fluctuated with a 29-min ultradian rhythm, indicating a tendency to move systematically through sleep states as observed in other species with consolidated sleep. These results are at variance with numerous older sleep studies, including for budgerigars. Here, we demonstrated that lighting conditions used in the prior budgerigar study-and commonly used in older bird studies-dramatically disrupted budgerigar sleep structure, explaining the prior results. Thus, it is likely that more complex sleep has been overlooked in a broad range of bird species. The similarities in sleep architecture observed in mammals, songbirds, and now budgerigars, alongside recent work in reptiles and basal birds, provide support for the hypothesis that a common amniote ancestor possessed the precursors that gave rise to REM and SWS at one or more loci in the parallel evolution of sleep in higher vertebrates. We discuss this hypothesis in terms of the common plan of forebrain organization shared by reptiles, birds, and mammals.

Implementation of driver's drowsiness assistance model based on eye movements detection

Annually, lots of persons are carrying lifelong disabilities or losing their lives owing to fatal accidents on the road. In addition to mechanical failures and people errors, driver's drowsiness represents one of the fundamental reasons for fatal accidents on the road. When drivers feel drowsy, lots of physiological and behavioral symptoms appear such as changes in the waves of the human brain, changes in facial expressions, variations in eye activity, decreasing head movements, etc. Therefore, there is a significant necessity to provide developed models of driver's drowsiness detection that exploit these symptoms for reducing accidents by warning drivers of drowsiness and fatigue. This paper concentrates on proposing a driver's drowsiness assistance model to monitor and alarm drivers by utilizing a behavioral-based method (eye movements detection method). In the proposed method of detecting eye movements (closed/opened), the Advanced Local Binary Pattern (Advanced LBP) is used, in which the descriptors are utilized to represent eye images to extract the tissue features of different persons in the driving car to see if the driver is in a drowsy state or not and this occurs after recording the driver's video and detecting the eyes of the driver. To extract the features in this way, the image of the eyes is divided into small regions through the Advanced LBP and sequenced into a single feature vector, where this method is used to determine the similarity features in the training group and to classify the eye image. The Naive Bayes classifier (NB) and Support Vector Machine (SVM) are utilized for giving good accuracy. The results indicate that the system has a high accuracy rate compared with the other existing methods, where the accuracy rate of NB and SVM using an eye detection dataset with training 90 % and testing 10% is 96 % and 97 %, respectively

Retracted: Evaluation of user concentration in ubiquitous and cognitive artificial intelligence‐assisted English online guiding system integrating face and eye movement detection

SummaryIn recent years, artificial intelligence technology has made significant breakthroughs. Big data, cloud computing, speech recognition, deep learning, and so on have become new hot spots after the Internet of things. The rapid development of artificial intelligence technology promotes the innovation of foreign language teaching ideas and learning methods. The new ecology and new paradigm of foreign language guiding must be reconstructed. The biggest difference between the Internet of things and the Internet is that the Internet of things is directly connected with all kinds of sensors. It does not need people to input information through the keyboard but automatically obtain information and carry out automatic processing. The deep integration of smart classroom and English online teaching will help to promote the reform and innovation of English teaching and provide a new way for the development of English online teaching. In this paper, we analyze the fusion of face recognition technology and eye tracking system and analyze the user focus evaluation theory in the Internet of things environment. Based on the above technology, this paper proposes an online English teaching user focus evaluation system in the context of artificial intelligence.

Microsaccades in Applied Environments: Real-World Applications of Fixational Eye Movement Measurements.

Across a wide variety of research environments, the recording of microsaccades and other fixational eye movements has provided insight and solutions into practical problems. Here we review the literature on fixational eye movements—especially microsaccades—in applied and ecologically-valid scenarios. Recent technical advances allow noninvasive fixational eye movement recordings in real-world contexts, while observers perform a variety of tasks. Thus, fixational eye movement measures have been obtained in a host of real-world scenarios, such as in connection with driver fatigue, vestibular sensory deprivation in astronauts, and elite athletic training, among others. Here we present the state of the art in the practical applications of fixational eye movement research, examine its potential future uses, and discuss the benefits of including microsaccade measures in existing eye movement detection technologies. Current evidence supports the inclusion of fixational eye movement measures in real-world contexts, as part of the development of new or improved oculomotor assessment tools. The real-world applications of fixational eye movement measurements will only grow larger and wider as affordable high-speed and high-spatial resolution eye trackers become increasingly prevalent.

Prosthetic hand with 2-dimensional motion based EOG signal control

A prosthetic hand is an artificial device that resembles a human hand which can help the human with a physical disability. Previously, the development of a prosthetic hand is designed in various method, from passive to bionic. Electrooculography (EOG) is a technique for measuring potential differences between the front (positive pole formed by the cornea) and the back (negative pole formed by the retina) of the eyeball which can be used to detect eye movements. The purpose of this study is to design a prosthetic hand with two degrees (2D) of freedom using EOG based control. This system consists of electrodes, EOG amplifier, Bluetooth transmitter-receiver, servo motors, and hand prosthetics. In this study, the system will recognize the eye movements, namely front, right, left, up, and down. The system will recognize the motion based on a threshold value. In the hardware implementation, the system was composed of five electrode sensors which installed around the eye, instrumentation amplifier, high pass filter, low pass filter, noninverting amplifier, summing amplifier, a notch filter circuit, and Arduino UNO microcontroller. In EOG data acquisition, this study involved ten healthy subjects. After the evaluation with five trial for each motion, the error for each eye movement is 0%, 0%, 36%, 4% and 16% for right, left, top, bottom, and front, respectively. This study provided an alternative method to control a prosthetics hand with good performance.

A neuromarketing aktuális helyzete és a mintaelemszámra vonatkozó kihívásai, különös tekintettel a szemkamerás mérésekre

One of the neuromarketing particularities is that it can be classified as qualitative and quantitative research technique since the number of participants is more closely related to qualitative sample sizes, however, the huge amount of measured data allow us to analyze them quantitatively. This summary focuses on the evaluation of a special type of observation, namely eye tracking methodology, particularly on to define the appropriate sample size. This method is based on the detection of eye movements to measure unconscious reactions, along with its great advantage that is it can provide reliable statistical results with a low sample size due to the similarity of the brain mechanism. These results can be interpreted in connection with the behavior of a specific target group. The author attempts to summarize the literature recommendations about the appropriate sample size in eye tracking projects if reliability is a relevant aspect.

A high performance asynchronous EOG speller system

In this study, an asynchronous region based electrooculogram (EOG) speller system has been proposed with fourty two targets consisting of alphabets, numbers and special characters. The total set of targets were arranged into six groups and each group has seven targets. The target groups were displayed in the form of a hexagon, three target groups on each side (right and left) of the display. The proposed system uses single blink or wink eye movements for selecting the desired target. It adopts two step recognition approach, 1) group selection and 2) target identification from the selected group. A pair of opposite target groups were highlighted at the same time in a random order, like an oddball paradigm. The paradigm randomly highlights all target groups once in a single trial. The subject should blink or wink for target group selection in synchrony with the highlights. In a similar manner the desired target is identified from the selected target group. A multi thresholding algorithm is used for eye movement detection. The online and offline experiments were conducted on ten healthy subjects for validating the proposed speller system. An average online classification accuracy of 98.482% is obtained with the information transfer rate (ITR) of 126.656 bits/min.

Translating the Violent Content of Grimm Brothers’ Fairy Tales: An Eye-Tracker Experiment

O rastreamento ocular tem sido introduzido como uma ferramenta para analisar os processos cognitivos dos tradutores. Pesquisas recentes com rastremento ocular concentram-se em examinar processos de tradução. No nosso conhecimento, no entanto, tal ferramenta ainda não foi usada para detectar comportamentos de movimento ocular relacionados ao Transtorno de Estresse Pós-Traumático (TEPT) dos tradutores, já que tais estudos estão apenas emergindo. Este artigo apresenta um estudo experimental que investiga os processos cognitivos envolvidos na tradução de três versões condensadas de contos de fadas dos irmãos Grimm. Os textos selecionados apresentam conteúdos violentos pelos quais esses contos são renomados, desde adaptações mais modernas que removem o conteúdo violento a histórias com violência explícita. Os textos foram traduzidos do espanhol (L2) para o inglês (L1). Os índices de fixação e duração da fixação foram calculados para cada participante em dez Áreas de Interesse (AOI), representando desafios na tradução de vários aspectos de conteúdo gramatical e violento apresentados nesses contos.

Two Improving Methods of EOG-based Eye Movement Detection for HCI

Two Improving Methods of EOG-based Eye Movement Detection for HCI

A COMMUNICATION AID SYSTEM BY FEATURE EXTRACTION OF ELECTROOCULOGRAPHIC SIGNALS

People with serious movement disabilities due to neurodegenerative diseases have problems in their communication with others. Considerable numbers of communication aid systems have been developed in the past. Especially, some of the systems driven by eye movements are thought to be effective for such people. Electrooculographic (EOG) signal reflects the eye movement and the specific pattern of eye movement can be seen in EOG signals. This paper proposes a communication aid system by extracting the features of EOG. The system consists of a computer, analog-to-digital converter, biological amplifier and two monitors. Two monitors, one for a system user and the other for other people, display the same information. Five items are presented in the monitor, and a user selects those items according to the situation in the communication. Selection of the items is done by combining three eye movements: gaze at left, gaze at right and successive blinks. Basic concept of the communication aid system was designed by taking into account the current state of a subject’s movement disability. Then, the design of a screen and the algorithm for detecting eye movement pattern from EOG were determined by using the data of normal healthy subjects. The system worked almost perfectly for normal healthy subjects. Then, the developed system was operated by a subject with serious movement disability. Parts of the system operation were regarded as satisfactory level, and some miss-operation were also seen.

Diagnosis of attention-deficit hyperactivity disorder using EOG signals: a new approach.

A system based on objective data was developed in the diagnosis and follow-up of attention-deficit hyperactivity disorder (ADHD) in this study. First of all, an electronic circuit, with a two-channel instrumentation amplifier designed to detect eye movements in the horizontal and vertical directions via surface electrodes, was developed to obtain the electrooculogram (EOG) signals. In order to provide a controlled analysis of eye movements during the reception of the signal, an attention test with visual stimulus software was developed. Eight patients with ADHD and eight healthy subjects were asked to monitor the stimulus images on the screen in the reference directions of the test system while recording EOG signals. According to the results of the t-test, no significant difference was found (p=0.11) between the healthy group and the reference movement information, whereas a significant difference was found between patients and the reference motion information (p=0.049). According to these results, it was seen that the number of eye movements of healthy individuals was statistically significant. In addition, they were inconsistent with the reference movement information. The level of significance was found to be low in patients. In this study, a new method is presented to test and diagnose individuals who were attention deficit.