Explore the vast range of titles available.

Ataxias are a group of neurodegenerative disorders characterized by cerebellar dysfunction that cause irregularities in the rate, rhythm, amplitude, and force of voluntary movements. The electrooculogram (EOG) may provide clues about ataxic disorders because most of these patients have difficulty with visual tracking and fixing their gaze. Using electrodes, EOG records the biopotentials generated by eye movements. In this paper, three surface electrodes are placed around the eye socket, and the biopotentials generated by eye movements are acquired using a commercial bioamplifier device. Next, the signals are sent to the computer to be digitally processed to extract the rate of saccades as well as the delay and deviation of the gaze in response to a stimulus. These features are analysed in a novel software application designed to help physicians in evaluating ataxia. After applying several tests to both healthy and ataxia-affected patients, differences in EOG results were found. The evaluation of the reliability of the designed software application is made according to three metrics: sensitivity, specificity, and accuracy. The results indicate the proposed system’s viability as an affordable method for evaluation of ataxic disorders.

The clinical electro-oculogram (EOG) is an electrophysiological test of the outer retina and retinal pigment epithelium (RPE) in which changes in the electrical potential across the RPE are recorded during successive periods of dark and light adaptation. This document presents the 2017 EOG Standard from the International Society for Clinical Electrophysiology of Vision (ISCEV: www.iscev.org ). This standard has been reorganized and updated to include an explanation of the mechanism of the EOG, but without substantive changes to the testing protocol from the previous version published in 2011. It describes methods for recording the EOG in clinical applications and gives detailed guidance on technical requirements, practical issues and reporting of results with the main clinical measure (the Arden ratio) now termed the light peak:dark trough ratio. The standard is intended to promote consistent quality of testing and reporting within and between clinical centers.

Bioelectrical or electrophysiological signals generated by living cells or tissues during daily physiological activities are closely related to the state of the body and organ functions, and therefore are widely used in clinical diagnosis, health monitoring, intelligent control and human-computer interaction. Ag/AgCl electrodes with wet conductive gels are widely used to pick up these bioelectrical signals using electrodes and record them in the form of electroencephalograms, electrocardiograms, electromyography, electrooculograms, etc. However, the inconvenience, instability and infection problems resulting from the use of gel with Ag/AgCl wet electrodes can’t meet the needs of long-term signal acquisition, especially in wearable applications. Hence, focus has shifted toward the study of dry electrodes that can work without gels or adhesives. In this paper, a retrospective overview of the development of dry electrodes used for monitoring bioelectrical signals is provided, including the sensing principles, material selection, device preparation, and measurement performance. In addition, the challenges regarding the limitations of materials, fabrication technologies and wearable performance of dry electrodes are discussed. Finally, the development obstacles and application advantages of different dry electrodes are analyzed to make a comparison and reveal research directions for future studies.

This project introduces a low-cost system for classifying eye blinks and vertical movements based on Electrooculography (EOG) signals and machine learning. EOG signals were recorded using wet electrodes and a Bio Amp EXG Pill, filtered using MATLAB, and used to extract signals for key features (amplitude, FFT energy, and mean). A Random Forest Classifier trained on the data, achieving an accuracy of 96.49% in the classifications including blinks, up, down, and stationary. Unlike previous work, this system works with compact, real-time compatible signals and uses threshold-based post processing to provide a good ground truth for movement counting. The work indicates a strong potential for use in assistive interfaces and eye-controlled systems.

Portable electroencephalogram (EEG) systems are increasingly used in healthcare due to their user-friendly and wearable design. However, accurate diagnosis can be hindered by electrooculogram (EOG) artifacts-low-frequency, high-amplitude signals caused by eye blinks. These artifacts are especially problematic in single-channel (SCL) EEG systems, necessitating robust artifact removal techniques. This study proposes an automated method for eliminating EOG artifacts from EEG signals using a Fixed Frequency Empirical Wavelet Transform (FF-EWT) integrated with a finely tuned Generalized Moreau Envelope Total Variation (GMETV) filter. The approach effectively separates artifact sources from the single-channel EEG by identifying contaminated components at the decomposition stage using kurtosis (KS), dispersion entropy (DisEn), and power spectral density (PSD) metrics. These components are then removed using the GMETV filter. The method was validated on both synthetic and real EEG datasets, demonstrating its capability to suppress EOG artifacts while preserving essential low-frequency EEG information. Performance evaluation revealed substantial improvements using the FF-EWT+GMETV technique, with lower Relative Root Mean Square Error (RRMSE) and higher Correlation Coefficient (CC) on synthetic data, and improved Signal-to-Artifact Ratio (SAR) and Mean Absolute Error (MAE) on real EEG recordings. This advancement offers strong potential for brain signal analysis, serving as an effective preprocessing tool in both clinical and research settings.

To tackle work-related stress in the evolving landscape of Industry 5.0, organizations need to prioritize employee well-being through a comprehensive strategy. While electrocardiograms (ECGs) and electrodermal activity (EDA) are widely adopted physiological measures for monitoring work-related stress, electrooculography (EOG) remains underexplored in this context. Although less extensively studied, EOG shows significant promise for comparable applications. Furthermore, the realm of human factors and ergonomics lacks sufficient research on the integration of wearable sensors, particularly in the evaluation of human work. This article aims to bridge these gaps by examining the potential of EOG signals, captured through smart eyewear, as indicators of stress. The study involved twelve subjects in a controlled environment, engaging in four stress-inducing tasks interspersed with two-minute relaxation intervals. Emotional responses were categorized both into two classes (relaxed and stressed) and three classes (relaxed, slightly stressed, and stressed). Employing supervised machine learning (ML) algorithms—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN)—the analysis revealed accuracy rates exceeding 80%, with RF leading at 85.8% and 82.4% for two classes and three classes, respectively. The proposed wearable system shows promise in monitoring workers’ well-being, especially during visual activities.

Eye tracking technology is frequently utilized to diagnose eye and neurological disorders, assess sleep and fatigue, study human visual perception, and enable novel gaze-based interaction methods. However, traditional eye tracking methodologies are constrained by bespoke hardware that is often cumbersome to wear, complex to apply, and demands substantial computational resources. To overcome these limitations, we investigated the application of Electrooculography (EOG) eye tracking using 14 electrodes positioned around the ears, integrated into a custom-built headphone form factor device. In a controlled laboratory experiment, 16 participants tracked a series of on-screen stimuli designed to induce smooth pursuits and saccades. Data analysis identified the optimal electrode pairs for tracking vertical and horizontal eye movements, benchmarked against gold-standard EOG and camera-based eye tracking. The electrode montage closest to the eyes provided the best results for horizontal eye movements. One-dimensional smooth pursuit eye movements measured via earEOG exhibited a high correlation with the gold-standard for horizontal 1D pursuits spanning to visual angle for the best performing electrode pair ( ; ). Vertical 1D smooth pursuits were only weakly correlated for the best performing pair ( ; ). Voltage deflections of earEOG and gold-standard EOG for saccades from to in the four cardinal directions are highly correlated for horizontal eye movement ( ; ) but not for vertical eye movements ( ; ). A regression model was employed to predict absolute gaze angle changes of horizontal saccades using earEOG and gold-standard EOG. In the left and right directions, the earEOG model achieved a mean absolute angular error of , for saccades ranging from to . In comparison, gold-standard EOG attained mean absolute angular error of . Overall, horizontal earEOG demonstrated strong performance, indicating its potential effectiveness in our setup. On the other hand, vertical earEOG showed significantly poorer results, suggesting that it may not be feasible with our current configuration.

Abstract Study Objectives To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality. Methods Power spectra from PSGs of 8716 participants, including from the MrOS Sleep Study and the Sleep Heart Health Study, were analyzed in deep learning-based survival models. The best-performing model was further examined using SHapley Additive Explanation (SHAP) for data-driven sleep-stage specific definitions of power bands, which were evaluated in predicting mortality using Cox Proportional Hazards models. Results Survival analyses, adjusted for known covariates, identified multiple EEG frequency bands across all sleep stages predicting all-cause mortality. For EEG, we found an all-cause mortality hazard ratio (HR) of 0.90 (CI: 95% 0.85 to 0.96) for 12–15 Hz in N2, 0.86 (CI: 95% 0.82 to 0.91) for 0.75–1.5 Hz in N3, and 0.87 (CI: 95% 0.83 to 0.92) for 14.75–33.5 Hz in rapid-eye-movement sleep. For EOG, we found several low-frequency effects including an all-cause mortality HR of 1.19 (CI: 95% 1.11 to 1.28) for 0.25 Hz in N3, 1.11 (CI: 95% 1.03 to 1.21) for 0.75 Hz in N1, and 1.11 (CI: 95% 1.03 to 1.20) for 1.25–1.75 Hz in wake. The gain in the concordance index (C-index) for all-cause mortality is minimal, with only a 0.24% increase: The best single mortality predictor was EEG N3 (0–0.5 Hz) with a C-index of 77.78% compared to 77.54% for confounders alone. Conclusions Spectral power features, possibly reflecting abnormal sleep microstructure, are associated with mortality risk. These findings add to a growing literature suggesting that sleep contains incipient predictors of health and mortality. Graphical Abstract Graphical Abstract

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).

Locked-in Syndrome (LIS) is a neurological condition in which individuals remain conscious but experience complete paralysis of voluntary muscles, except for eye movements—highlighting the need for reliable assistive communication technologies. This study presents the design and evaluation of an Arabic electrooculogram (EOG)-based communication system with adaptive classification capabilities for LIS applications. A custom-designed EOG acquisition circuit incorporating filtering and amplification stages was implemented and compared with the OpenBCI Cyton board. The system employed a hybrid classification approach combining amplitude, temporal, and statistical features to distinguish between blinks and voluntary vertical eye movements. Testing with ten healthy subjects yielded a mean classification accuracy of 83.96% ± 4.59% and an information transfer rate of 10.43 letters per minute, corresponding to a 30.38% improvement over conventional approaches. The custom-designed circuit achieved a signal-to-noise ratio of 25.21 dB, outperforming the OpenBCI Cyton board by 8% while reducing system cost by 62%. The integration with a Morse code-based interface enabled Arabic letter composition, while the system incorporated auto-completion and text-to-speech functionalities to further enhance communication efficiency. This cost-effective solution addresses a critical gap in assistive technologies for Arabic-speaking individuals with LIS and shows strong potential for enhancing their communication abilities and overall quality of life.