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Electrooculography (EOG) technology can be used for the interfacing of human-computer interface(HCI) systems for the control of appliances. The main objective of measuring and processing these signals is to help people succeed in dealing with the inconveniences in the physical world especially for the people who are immobile. EOG is the eye trackin...
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... number of active electrodes placed and the second number denotes the total number of electrodes including the reference electrode. The 4/5 electrode configuration is used here as it is a compromise between the other two configurations. The wires used in the 7/8 configuration are a disadvantage. The different measurement systems are as shown in Fig. 2. ...
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Wearable graphene textile embedded smart headband and its feasibility in electrooculography (EOG) applications is demonstrated by benchmarking against clinical Ag/AgCl wet electrodes; where the recorded biopotentials displayed excellent correlation of 91.3% over durations up to hundred seconds. Automatic eye movement (EM) detection is implemented a...
The development of mobile technology over the last years and the consequent boom of available apps has enabled users to migrate a wide range of activities that were traditionally performed on computers to their smartphones. Despite this new freedom to work ubiquitously, there are circumstances in which operating the device becomes difficult, e.g.,...
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... There have recently been more chances for research in this field. For instance, eye motions for using and managing appliances were once more observed [9]. Wheelchairs with EOG control receive the greatest attention. ...
Electrooculography is considered as one of the significant electro-physiological signals. These signals carry data of eye movements which can be employed in human-computer interface (HCL) as a control signal. This project focuses on creating a text and voice-based interpreter for quadriplegic patients using electrooculography (EOG) signals. EOG is a technique that measures the electrical activity of the eye muscles responsible for eye movements and can be used to track changes in eye location to reveal information about human eye activities. The EOG signal is commonly used in human-computer interface (HCI) systems as an alternative input for patients suffering from quadriplegia, ALS, and locked-in syndrome. The BioAmp EXG Pill Sensor is used to acquire EOG signals of left and right eye movement, as well as up and down eye movement. The signals are processed using an ESP32 microcontroller and Arduino IDE, and an algorithm is created to analyze the observed ranges and generate text and voice-based outputs. The accuracy of the system was tested by asking 10 healthy participants to perform each of the four types of motions ten times, and the results showed an overall accuracy of 81.04%. The system involves detecting EOG signals using sensors that are placed around the patient's eyes, and the text-based output is displayed on an LCD screen, while the voice-based output is played on an MP3 player. The output is then displayed on an application enabling communication with the patient remotely, potentially improving the quality of care and increasing the patient’s sense of security. Future developments could include increasing the degrees of motion and addition of an eye-blink sensor for more convenient user experience. This project provides a valuable solution for quadriplegic patients, enabling them to communicate effectively and empowering them with a sense of independence. However, further research and testing are needed to fully evaluate the efficacy of the system on actual quadriplegic patients.
... It works by placing electrodes and sensing the corneo-retinal potential (the resting potential between the cornea and the retina of the eye) which is proportional to the eye-movement. Finally, since the obtained signal has low voltage, it is amplified, filtered and processed to remove involuntary blinks, noises and other artefacts [4][5][6]. Differently, Infrared-oculography (IROG) is a non-invasive method to validate the time of foveation, an indirect measure of VA: an infrared light illuminates the eye and the sclera reflects it; the difference between the input and the output of infrared light from the eye describes the eye positioning. Both EOG and IROG are still considered good methodology for measuring eye-movement and for eye-tracking, as testified by Singh and Singh in their review [7]. ...
During the first months of life, babies can be affected by congenital nystagmus, an ocular-motor disease making visual acuity decrease. Electrooculography (EOG) and Infrared-oculography are utilized in order to perform eye-tracking of patients, giving the possibility to extract from the signals several useful features. In the past years, different algorithms were used to perform the detection of events on these features and many researchers studied the relationships between the features and physiological values such as visual acuity and variability of eye-positioning. In this paper, machine learning techniques were used to predict visual acuity and the variability of eye positioning using features extracted from EOG. The EOG of 20 patients was acquired, signals underwent a pre-processing, and some parameters were extracted through a custom-made software. Frequency, amplitude, intensity, nystagmus foveation periods and both amplitude and frequency of baseline oscillation were the features used as input for the algorithms. Knime analytics platform was employed to perform a predictive analysis using Random Forests, Logistic Regression Tree, Gradient boosted tree, K nearest neighbour, Multilayer Perceptron and Support Vector Machine. Finally, some evaluation metrics were computed employing a leave one out cross validation. Considering the coefficient of determination, visual acuity achieved values between 0.67 and 0.85 while variability of eye positioning ranged from 0.62 to 0.79. These results were compared with past analysis with the exact same aims and dataset, obtaining a greater value as regards the variability of eye positioning and comparable results exploiting all the features related to nystagmus as regards the visual acuity. This paper showed the feasibility of a regression analysis performed through machine learning algorithms in detecting relationships among variables related to congenital nystagmus.
... The instrumentation amplifier circuit based on figure 4 serves as an initial gain from the Horizontal Channel Electrode and Vertical Channel inputs to get the shape of the EOG signal. The shape of the EOG signal requires amplitude amplification [11][12][13]. The R3 resistance is a resistor gain (Rg) which is used to regulate its gain. ...
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.
... In this paper [20] author describes common spatial pattern (CSP) to the classification of electrooculography (EOG) signals with four distinct classes, namely, eye blinks (EB), eye rotation clockwise (ERC), vertical eye movement (VEM) and horizontal eye movement (HEM). The author in this paper [21] implemented the various techniques used for eye movement detection are Infrared oculography (IROG), where a light source is focused. The amount of light reflected to the detector differs with respect to the position of the eye balls. ...
This paper covers a new technology by placing electrodes on forehead of person around the eyes for recording the eye movements, this technique is called Electrooculography (EOG). It is basically focused on the concept of recording down the polarization potential which is also called corneal-retinal potential (CRP), this is resting potential between retina and cornea. The potential is called electrooculogram. This is a tiny electrical potential that can be found out using electrodes that are comparative to eye displacement. Electrooculography is a means of control for allowing the handicapped, mainly people with eye-motor coordination, which allow them live independent lives. This is a very good assistive system for disabled people with less money. This total command control EOG lets users to guide it with a high intensity of comfort ability. Systems which are becoming more popular day by day are HCI (Human Computer Interaction) that are Eye tracking based. To such systems Electrooculogram (EOG) functions as basic source of input. This integral process of analysis of electrooculogram is called Electrooculography. The paper we are presenting gives basic idea on Electrooculography and its use and applications in different HCI systems. In this work we have also included detection of eye blinks from EOG signal. Conclusively it throws light on various issues concerned with Electrooculography.
... V What's more SureshR. Norman [6] recommend an alternate EOG estimation frameworks for the interfacing what's more control appliances. Veena g. ...
... The range of eye movement is 100 o for vertical and 120 o for horizontal eye movements as shown in Fig. 3 showing good pattern repetition, the average frequency plot varies between (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)Hz and during EOG rising, the frequency of the EOG signal is decreased and approached to less than three Hz, while the power in band shows two peak values. The measured EOG VD in Fig5 shows higher peak values in power band, which can be used in pattern recognition to discriminate between EOG VU and EOG VD. ...
This paper present a novel way of modeling EOG signal to use in a robot arm control system, two procedures implemented, offline procedure to measure and modeling EOG for building a pattern reference model ,and online procedure used to Control the robot arm. By comparing online measured EOG and the EOG pattern in reference model suitable manipulation instruction generated by the micro controller. The double exponential smoothing method used for building the pattern reference model, the accuracy of the reference model tested with main squire error (MSE) and main absolute error (MAPE) measures. Auto correlation analysis applied to study the existing pattern and linearity of EOG signal with eye movements. EOG signal measurement for this research classified in to five kinds: EOG horizontal (left and right) Vertical (up and down), and blinking. The EOG signal models of this research saved and used as a reference model file to classify the eye movements. a measurement and robot arm control system constructed by using arduino olimix 328, olimix sensor shield, and robot arm driving circuit, arduino C used as a programming environment, Minitab software used to build the model and correlation analysis ,Brain Bay software used to control and signal processing.
... However, the papers are mainly focused on acquiring EOG signals with a greater quality. For example, eye gestures were analyzed again for the interface and control of appliances [25] and the solution to the drift problem [26] was also revised. Other research can be found where a novel encoding paradigm to convey users' intentions is presented as a preliminary step to develop an HCI [27]. ...
The development of a novel computer writing system based on eye movements is introduced herein. A system of these characteristics requires the consideration of three subsystems: (1) A hardware device for the acquisition and transmission of the signals generated by eye movement to the computer; (2) A software application that allows, among other functions, data processing in order to minimize noise and classify signals; and (3) A graphical interface that allows the user to write text easily on the computer screen using eye movements only. This work analyzes these three subsystems and proposes innovative and low cost solutions for each one of them. This computer writing system was tested with 20 users and its efficiency was compared to a traditional virtual keyboard. The results have shown an important reduction in the time spent on writing, which can be very useful, especially for people with severe motor disorders.
Eye movement information provides an alternative way for patients with muscular and neurological disorders to communicate with other people or devices. Electrooculography (EOG) is an effective eye movement recording method that has been widely applied to design human–computer interaction systems. In this paper, we propose a simplified Chinese eye-writing system based on EOG (EsCew) by identifying the basic strokes of Chinese characters. Specifically, we first use a bandpass digital filter to preprocess the raw EOG signals to suppress noise interference. Then, we determine the effective eye movement segments according to different strokes by detecting blink signals using the sliding window technique. On this basis, we establish basic stroke templates in terms of the handwriting characteristics of Chinese characters. To reduce the computational complexity, the DTW algorithm is adopted to classify the EOG segments. Finally, we match the stroke sequence with the encoded Chinese characters to obtain the final recognition results. In the lab environment, the recognition experiments are performed on 10 most representative Chinese characters, i.e., . The average accuracies of the basic strokes and Chinese characters are 93.998% and 94.52%, respectively. The experimental results validate the feasibility of the proposed EsCew system.
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Electrooculography (EOG) is beneficial in many human interface systems when the signals are measured, amplified and filtered to produce a signal from movement of eyes by which appliances can be controlled. Some patients who are disabled by paralysis can only control their eyes; therefore, these EOG and electromyography (EMG) signals become one of the few ways they can interact with the surrounding environment. The signals gathered from blinking intentionally and unintentionally, need to be distinguished in order for the system to work correctly. By integrating duration and amplitude data from both EOG and EMG signals simultaneously from three electrodes, an efficient algorithm is proposed to distinguish the two classes of blinking: intentional and unintentional.
Human activity recognition (HAR) is a research hotspot in the field of artificial intelligence and pattern recognition. The electrooculography (EOG)-based HAR system has attracted much attention due to its good realizability and great application potential. Focusing on the signal processing method of the EOG-HAR system, we propose a robust EOG-based saccade recognition using the multi-channel convolutional independent component analysis (ICA) method. To establish frequency-domain observation vectors, short-time Fourier transform (STFT) is used to process time-domain EOG signals by applying the sliding window technique. Subsequently, we apply the joint approximative diagonalization of eigenmatrix (JADE) algorithm to separate the mixed signals and choose the "clean" saccadic source to extract features. To address the problem of permutation ambiguity in a case with a six-channel condition, we developed a constraint direction of arrival (DOA) algorithm that can automatically adjust the order of eye movement sources according to the constraint angle. Recognition experiments of four different saccadic EOG signals (i.e. up, down, left and right) were conducted in a laboratory environment. The average recognition ratios over 13 subjects were 95.66% and 97.33% under the between-subjects test and the within-subjects test, respectively. Compared with "bandpass filtering", "wavelet denoising", "extended infomax algorithm", "frequency-domain JADE algorithm" and "time-domain JADE algorithm, the recognition ratios obtained relative increments of 4.6%, 3.49%, 2.85%, 2.81% and 2.91% (within-subjects test) and 4.91%, 3.43%, 2.21%, 2.24% and 2.28% (between-subjects test), respectively. The experimental results revealed that the proposed algorithm presents robust classification performance in saccadic EOG signal recognition.
