Electrooculography Signals Research Articles

Communication Prototype for Post-Stroke Patients Using Electrooculography (EOG)

Background this prototype is intended for post-stroke patients experiencing disabilities in their daily activities, particularly in communication with others. The challenge they face is difficulty in communication, leading to a diminished quality of life for post-stroke patients. The purpose of innovating this communication aid prototype is to facilitate communication between post-stroke patients and caregivers. The method employed in the post-stroke communication aid prototype utilizes Electrooculography (EOG) signals generated from eye muscle movements during eye gazes, captured by the MaM Sense sensor. The variation in Analog-to-Digital Converter (ADC) values in the MaM Sense sensor is exploited to produce various forms of EOG signals. The resultant command signals from this method are processed by a microcontroller and displayed on a 20 x 4 Character LCD. Testing was conducted on 9 healthy individuals, comprising 5 males and 4 females. To ensure the prototype's functionality, testing was also performed on 1 post-stroke patient. The success rate of MaM Sense sensor readings was 80.5% for the 4 communication modes employed, involving 4 eye gaze movements: right gaze, left gaze, upward gaze, and downward gaze. Thus, the post-stroke communication aid prototype proves effective in assisting communication for post-stroke patients and aiding caregivers in understanding the patients' desires. In the future, a wireless system may be implemented for the acquisition of EOG signals attached to the face to minimize the use of cables.

Retrospective validation of automatic sleep analysis with grey areas model for human-in-the-loop scoring approach.

State-of-the-art automatic sleep staging methods have demonstrated comparable reliability and superior time efficiency to manual sleep staging. However, fully automatic black-box solutions are difficult to adapt into clinical workflow due to the lack of transparency in decision-making processes. Transparency would be crucial for interaction between automatic methods and the work of sleep experts, i.e., in human-in-the-loop applications. To address these challenges, we propose an automatic sleep staging model (aSAGA) that effectively utilises both electroencephalography and electro-oculography channels while incorporating transparency of uncertainty in the decision-making process. We validated the model through extensive retrospective testing using a range of datasets, including open-access, clinical, and research-driven sources. Our channel-wise ensemble model, trained on both electroencephalography and electro-oculography signals, demonstrated robustness and the ability to generalise across various types of sleep recordings, including novel self-applied home polysomnography. Additionally, we compared model uncertainty with human uncertainty in sleep staging and studied various uncertainty mapping metrics to identify ambiguous regions, or "grey areas", that may require manual re-evaluation. The validation of this grey area concept revealed its potential to enhance sleep staging accuracy and to highlight regions in the recordings where sleep experts may struggle to reach a consensus. In conclusion, this study provides a technical basis and understanding of automatic sleep staging uncertainty. Our approach has the potential to improve the integration of automatic sleep staging into clinical practice; however, further studies are needed to test the model prospectively in real-world clinical settings and human-in-the-loop scoring applications.

Power and area efficient FIR filter using Radix- 2r multiplier for de-noise the electrooculography (EOG) signal

This article introduces a novel FIR filter using a Radix-2r multiplier combined with 4:2 and 3:2 compressors for denoising Electrooculography (EOG) signals. This approach replaces traditional ripple-carry adders with compressors to add the partial products generated by the Radix-2r multiplier, resulting in reduced delay and energy consumption. The FIR filter is implemented in gate-level Verilog HDL, verified using ModelSim and Altera DSP Builder, and synthesized with the Cadence RTL compiler. Compared to conventional designs, the proposed filter achieves a 71.48% reduction in area, 73.28% reduction in power, 51.84% reduction in delay, 86.22% reduction in the area-delay product (ADP), and 92.38% reduction in energy-per-operation (EPS), significantly outperforming the SOPOT filter.

Electrooculography Signal Acquisition and Processing for Real-Time Virtual Keyboard

Electrooculography Signal Acquisition and Processing for Real-Time Virtual Keyboard

Driving Reality vs. Simulator: Data Distinctions

As the automotive industry undergoes a phase of rapid transformation driven by technological advancements, the integration of driving simulators stands out as an important tool for research and development. The usage of such simulators offers a controlled environment for studying driver behavior; the alignment of data, however, remains a complex aspect that warrants a thorough investigation. This research investigates driver state classification using a dataset obtained from real-road and simulated conditions, recorded through JINS MEME ES_R smart glasses. The dataset encompasses electrooculography signals, with a focus on standardizing and processing the data for subsequent analysis. For this purpose, we used a recurrent neural network model, which yielded a high accuracy on the testing dataset (86.5%). The findings of this study indicate that the proposed methodology could be used in real scenarios and that it could be used for the development of intelligent transportation systems and driver monitoring technology.

EOG Based Text and Voice Controlled Remote Interpreter for Quadriplegic Patients

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.

Interhemispheric differences of electroencephalography signal characteristics in different sleep stages

ObjectiveThe current electroencephalography (EEG) measurement setup is complex, laborious to set up, and uncomfortable for patients. We hypothesize that differences in EEG signal characteristics for sleep staging between the left and right hemispheres are negligible; therefore, there is potential to simplify the current measurement setup. We aimed to investigate the technical hemispheric differences in EEG signal characteristics along with electrooculography (EOG) signals during different sleep stages. MethodsType II portable polysomnography (PSG) recordings of 50 patients were studied. Amplitudes and power spectral densities (PSDs) of the EEG and EOG signals were compared between the left (C3-M2, F3-M2, O1-M2, and E1-M2) and the right (C4-M1, F4-M1, O2-M1, and E2-M2) hemispheres. Regression analysis was performed to investigate the potential influence of sleep stages on the hemispheric differences in PSDs. Wilcoxon signed-rank tests were also employed to calculate the effect size of hemispheres across different frequency bands and sleep stages. ResultsThe results showed statistically significant differences in signal characteristics between hemispheres, but the absolute differences were minor. The median hemispheric differences in amplitudes were smaller than 3 μv with large interquartile ranges during all sleep stages. The absolute and relative PSD characteristics were highly similar between hemispheres in different sleep stages. Additionally, there were negligible differences in the effect size between hemispheres across all sleep stages. ConclusionsTechnical signal differences between hemispheres were minor across all sleep stages, indicating that both hemispheres contain similar information needed for sleep staging. A reduced measurement setup could be suitable for sleep staging without the loss of relevant information.

Driver Drowsiness Multi-Method Detection for Vehicles with Autonomous Driving Functions.

The article outlines various approaches to developing a fuzzy decision algorithm designed for monitoring and issuing warnings about driver drowsiness. This algorithm is based on analyzing EOG (electrooculography) signals and eye state images with the aim of preventing accidents. The drowsiness warning system comprises key components that learn about, analyze and make decisions regarding the driver's alertness status. The outcomes of this analysis can then trigger warnings if the driver is identified as being in a drowsy state. Driver drowsiness is characterized by a gradual decline in attention to the road and traffic, diminishing driving skills and an increase in reaction time, all contributing to a higher risk of accidents. In cases where the driver does not respond to the warnings, the ADAS (advanced driver assistance systems) system should intervene, assuming control of the vehicle's commands.

Communication Prototype for Post-Stroke Patients Using Electrooculography (EOG)

The background of this prototype is meant for post-stroke patients who have difficulties owing to trouble completing daily activities, particularly speaking with others. They have trouble communicating, which reduces their quality of life after a stroke. The goal of developing novel communication aids for post-stroke patients is to make it easier for post-stroke patients to communicate with caretakers. Electrooculography (EOG) signals generated by the movement of the eye muscles during eye glances collected by the MaM Sense sensor are employed in post-stroke communication aids. The microcontroller processes the method's command signal, which is subsequently displayed on a 20 x 4 Character LCD. For the four communications utilized, the MaM Sense sensor reading was successful 80.5% of the time. Thus, communication tools for post-stroke patients can help them communicate while also assisting caretakers in understanding the patient's wants. Improvements will be made in the design and wireless technologies for EOG signal recovery in the future.

The detection of alcohol intoxication using electrooculography signals from smart glasses and machine learning techniques

The operation of a motor vehicle under the influence of alcohol poses a significant risk to the safety of the driver, passengers, and other road users. Electrooculographic (EOG) signal analysis can be used to understand the movements and behavior of the eyes while driving. In our study, we used smart glasses to collect EOG data from nine participants who used a driving simulator. Their level of alcoholic intoxication was simulated by drunk vision goggles at three different levels of inebriation (0, 1, 2, and 3‰ blood alcohol content). We used machine learning algorithms (decision trees, support vector machines, nearest-neighbor classifiers, boosted trees, bagged trees, subspace discriminant classifier, subspace k nearest-neighbor classifier, and RUSBoosted Trees) to analyze the data. The Bagged Trees achieved the highest accuracy of 79%. The most important features to detect simulated alcohol intoxication were the blink rate and the velocity of the saccade, a rapid simultaneous movement of both eyes in the same direction. Our study shows the potential of using smart glasses and machine learning for the automated detection of alcohol intoxication, even when alcohol consumption is simulated.

A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography.

Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human-computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence of multiple sources of noise interference. These sources include electroencephalography, electromyography linked to facial and extraocular muscle activity, electrical noise, signal artifacts, skin-electrode drifts, impedance fluctuations over time, and a host of associated challenges. Traditional methods of addressing these issues, such as bandpass filtering, have been frequently utilized to overcome these challenges but have the associated drawback of altering the inherent characteristics of EOG signals, encompassing their shape, magnitude, peak velocity, and duration, all of which are pivotal parameters in research studies. In prior work, several model-based adaptive denoising strategies have been introduced, incorporating mechanical and electrical model-based state estimators. However, these approaches are really complex and rely on brain and neural control models that have difficulty processing EOG signals in real time. In this present investigation, we introduce a real-time denoising method grounded in a constant velocity model, adopting a physics-based model-oriented approach. This approach is underpinned by the assumption that there exists a consistent rate of change in the cornea-retinal potential during saccadic movements. Empirical findings reveal that this approach remarkably preserves EOG saccade signals, resulting in a substantial enhancement of up to 29% in signal preservation during the denoising process when compared to alternative techniques, such as bandpass filters, constant acceleration models, and model-based fusion methods.

A New Multi-resolution Analysis Method for Electrooculography Signals.

Electrooculography (EOG) signals indicate the degree and direction of eye movements. Hence, EOG signals have been useful in eye movement controlled rehabilitation systems. Denoising and accurate identification of the type of eye movement in EOG signals are the major challenges in their analysis. The state-of-the-art techniques for EOG signal analysis concerning denoising and eye movement extraction are based on multi-resolution analysis using wavelet bases, such as Haar or Daubechies. However, these wavelets are designed for general purpose signal processing applications and hence are not optimized for the EOG signal structures. In this paper, we propose a new multi-resolution basis specific to the analysis of EOG signals. The scaling and wavelet functions for the basis are derived from the signatures of blinks and saccades respectively, and hence we name them as blinklets and saclets accordingly, thereby forming a new multi-resolution basis. These descriptors are found to be more effective than standard wavelets for EOG signals, signal denoising, and for identifying the different eye movement signatures such as saccades, blinks, smooth pursuits, and fixations, as tested on the Physiosig and Centre for Biomedical Cybernetics Eye Movement (CBC-EM) EOG Databases.

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.

Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring.

Manual sleep staging (MSS) using polysomnography is a time-consuming task, requires significant training, and can lead to significant variability among scorers. STAGER is a software program based on machine learning algorithms that has been developed by Medibio Limited (Savage, MN, USA) to perform automatic sleep staging using only EEG signals from polysomnography. This study aimed to extensively investigate its agreement with MSS performed during clinical practice and by three additional expert sleep technicians. Forty consecutive polysomnographic recordings of patients referred to three US sleep clinics for sleep evaluation were retrospectively collected and analyzed. Three experienced technicians independently staged the recording using the electroencephalography, electromyography, and electrooculography signals according to the American Academy of Sleep Medicine guidelines. The staging initially performed during clinical practice was also considered. Several agreement statistics between the automatic sleep staging (ASS) and MSS, among the different MSSs, and their differences were calculated. Bootstrap resampling was used to calculate 95% confidence intervals and the statistical significance of the differences. STAGER's ASS was most comparable with, or statistically significantly better than the MSS, except for a partial reduction in the positive percent agreement in the wake stage. These promising results indicate that STAGER software can perform ASS of inpatient polysomnographic recordings accurately in comparison with MSS.

Effects of lying posture and task type on muscle fatigue, visual fatigue, and discomfort while using a smartphone on the bed.

Neck-shoulder and arm musculoskeletal disorders in smartphone use have attracted increasing attention. This study aimed to examine the effect of different lying postures and task types by comparing the electrooculography (EOG) responses, perceived discomfort, and the activity of neck-shoulder and arm muscles while using a smartphone on the bed. Twenty participants were recruited to perform 30-min tasks (texting/ watching video) while holding the smartphone in three different lying postures (half-lying/lying/side-lying). Subjective discomfort, muscular activities including the abductor pollicis brevis (APB), upper trapezius (UT), and extensor carpi radialis (ECR), and EOG signals were simultaneously monitored. The results showed that the perceived discomfort of measured body parts increased significantly over time in all postures. Watching video significantly increases muscle activities of APB and UT and decreases blink frequency and blink duration. Compared to lying or half-lying, the side-lying posture has the highest blink frequency, blink duration, and muscle activities in ECR and UT. The findings of this study indicate that side-lying posture should be avoided while using a smartphone on the bed. It provides an understanding of the association between the postures of using smartphones on the bed and fatigue, which can help prevent muscle and visual fatigue in smartphone use.

Identification of full-night sleep parameters using morphological features of ECG signals: A practical alternative to EEG and EOG signals

Electroencephalogram (EEG) signals, which are among the most important recordings used in Polysomnography for sleep staging, are more challenging and demanding than electrocardiography (ECG) signals, both in terms of acquisition and interpretation. When examining the studies of other researchers on sleep parameters in the literature, it is evident that EEG signals are predominantly used for determining arousal (AR), K-complex (Kc), and sleep spindle (Ss) parameters. Furthermore, it is understood that electrooculography (EOG) signals are employed for detecting slow eye movements (SEM) and rapid eye movements (REM) parameters.This study is a continuation of our previous research, where we used only EEG signals for Kc and Ss detection. In this study, an approach that includes ECG signals in the determination of sleep parameters to bring practicality to sleep staging studies was adopted. For this purpose, firstly, 16 morphological features were extracted from ECG recordings taken from a total of 24 subjects after various preprocessing steps. Subsequently, these data were used to work on the detection of five different sleep parameters: AR, Kc, Ss, SEM, and REM, using the Random Subspace (RaSE) ensemble learning algorithm. The results were calculated according to various statistical criteria and a classification accuracy of over 78 % was obtained in all parameters. As a result, the sleep parameters that could be determined most successfully using the ECG signal were SEM and arousal, respectively. In addition, feature elimination was performed for these datasets using Symmetric Uncertainty (SU) ranking. As a result of the reclassification process using 9 and 12 features, the effectiveness of which was determined for both datasets, respectively, significant increases were observed in the performance outputs. Experimental results have shown that ECG signals can be used as an alternative to EEG and EOG signals in the determination of full-night sleep parameters.

A hybrid BCI combining SSVEP and EOG and its application for continuous wheelchair control

Brain-computer interfaces (BCIs) have emerged as one of the most promising techniques for individuals with motor disabilities to control external devices directly without peripheral pathways. While the discrete control strategy is widely adopted in BCI systems, the continuous control strategy provides intuitive and fluent control of robotic devices. However, the steady-state visual evoked potential (SSVEP)-based BCI is still far from practical applications of continuous control. One of the existing challenges is the long erroneous transition time, i.e., the duration from when the subject switches towards a new target to when the BCI system recognizes this new target. Therefore, this study proposed a hybrid BCI combining SSVEP and electrooculography (EOG). First, the prior probability distribution of the intended target was obtained by the SSVEP detection method. Different from directly picking the one with the maximum probability as the conventional SSVEP-based BCI did, the captured saccade event in the EOG signal served as an additional event to optimize this probability distribution, and the target was then extrapolated and outputted. An offline experiment simulating the target switching process and an online experiment of virtual wheelchair control were designed and conducted. The proposed hybrid BCI significantly outperformed the conventional SSVEP-based BCI in both the offline experiment (continuous accuracy:64.99 ± 3.44 % vs. 55.76 ± 3.67 %, transition time: 1.19 ± 0.073 s vs. 1.65 ± 0.08 s) and the online wheelchair control experiment (success rate: 81.67 ± 5.75 % vs. 16.67 %± 3.76 %; collision times with the dynamic obstacle: 1.50 ± 0.50 vs. 8.25 ± 0.37; minimum distance to dynamic obstacle: 1.41 ± 0.13 m vs. 0.31 ± 0.08 m). The results revealed that by combining the SSVEP and EOG signals, the proposed hybrid BCI can significantly diminish the transition time in SSVEP-based BCI and achieve continuous, fluent control. The present study provides a novel framework for integrating the SSVEP and EOG, and promotes the potential applications of continuous brain-actuated robotic devices.

A review for filtering techniques of the Electrooculography (EOG) signals

Electrooculography (EOG) is a non-invasive method employed for the measurement of the electrical potential produced by ocular movements. EOG signals frequently encounter contamination from diverse forms of interference, thereby impeding precise analysis and interpretation. In order to address these obstacles, numerous filtering methodologies have been devised to ameliorate the quality of EOG signals. The objective of this paper is to examine the filtering techniques commonly employed for EOG signals, elucidating their respective benefits and limitations.

Fourier Synchrosqueezing Transform-ICA-EMD Framework Based EOG-Biometric Sustainable and Continuous Authentication via Voluntary Eye Blinking Activities.

In recent years, limited works on EOG (electrooculography)-based biometric authentication systems have been carried out with eye movements or eye blinking activities in the current literature. EOGs have permanent and unique traits that can separate one individual from another. In this work, we have investigated FSST (Fourier Synchrosqueezing Transform)-ICA (Independent Component Analysis)-EMD (Empirical Mode Decomposition) robust framework-based EOG-biometric authentication (one-versus-others verification) performances using ensembled RNN (Recurrent Neural Network) deep models voluntary eye blinkings movements. FSST is implemented to provide accurate and dense temporal-spatial properties of EOGs on the state-of-the-art time-frequency matrix. ICA is a powerful statistical tool to decompose multiple recording electrodes. Finally, EMD is deployed to isolate EOG signals from the EEGs collected from the scalp. As our best knowledge, this is the first research attempt to explore the success of the FSST-ICA-EMD framework on EOG-biometric authentication generated via voluntary eye blinking activities in the limited EOG-related biometric literature. According to the promising results, improved and high recognition accuracies (ACC/Accuracy: ≥99.99% and AUC/Area under the Curve: 0.99) have been achieved in addition to the high TAR (true acceptance rate) scores (≥98%) and low FAR (false acceptance rate) scores (≤3.33%) in seven individuals. On the other hand, authentication and monitoring for online users/students are becoming essential and important tasks due to the increase of the digital world (e-learning, e-banking, or e-government systems) and the COVID-19 pandemic. Especially in order to ensure reliable access, a highly scalable and affordable approach for authenticating the examinee without cheating or monitoring high-data-size video streaming is required in e-learning platforms and online education strategies. Hence, this work may present an approach that offers a sustainable, continuous, and reliable EOG-biometric authentication of digital applications, including e-learning platforms for users/students.

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

Objective:This work aims to propose a novel method to estimate the gaze from electrooculography (EOG) signals while compensating for the baseline drift, and which intrinsically detects fixations, saccades and blinks. In contrast to existing baseline drift mitigation techniques, the proposed framework is real-time-implementable, and does not require the average point of gaze to lie at the primary gaze position, nor does it disrupt the overall ocular pose-induced EOG signal DC characteristics. The EOG data used to validate the proposed method is also being made publicly available. Methods:The proposed method is based on the dual Kalman filter, which estimates the gaze angles (GAs) and the baseline concurrently, taking into consideration the EOG signal’s non-stationary and temporally-multimodal characteristics. In fact, it is a multiple-model technique based on a battery model of the eye wherein fixations, saccades and blinks are modelled separately. Results:When applied to short EOG data segments, a horizontal and vertical GA estimation error of 1.64 ± 0.82°and 1.97 ± 0.34°, respectively, was obtained, which compared well with the corresponding results obtained using the state-of-the-art linear regression models. Conversely, for longer data segments, the proposed framework yielded superior GA estimation performance when compared to the state-of-the-art techniques. Eye movement detection and labelling F-scores exceeding 90% were achieved. Conclusion:The proposed method yields reliable gaze estimation performance, and accurately detects fixations, saccades and blinks. Significance:This work proposes an integrated method to simultaneously estimate the GAs and address the baseline drift issue without the limitations of existing baseline drift mitigation techniques.