Electrooculogram Signals Research Articles

Quantifying attention in children with intellectual and developmental disabilities through multicenter electrooculogram signal analysis.

In a multicenter case-control investigation, we assessed the efficacy of the Electrooculogram Signal Analysis (EOG-SA) method, which integrates attention-related visual evocation, electrooculography, and nonlinear analysis, for distinguishing between intellectual and developmental disabilities (IDD) and typical development (TD) in children. Analyzing 127 participants (63 IDD, 64 TD), we applied nonlinear dynamics for feature extraction. Results indicated EOG-SA's capability to distinguish IDD, with higher template thresholds and Correlation Dimension values correlating with clinical severity. The template threshold proved a robust indicator, with higher values denoting severe IDD. Discriminative metrics showed areas under the curve of 0.91 (template threshold) and 0.85/0.91 (D2), with sensitivities and specificities of 77.6%/95.9% and 93.5%/71.0%, respectively. EOG-SA emerges as a promising tool, offering interpretable neural biomarkers for early and nuanced diagnosis of IDD.

Multiscale neural dynamics in sleep transition volatility across age scales: a multimodal EEG-EMG-EOG analysis of temazepam effects.

Recent advances in computational modeling techniques have facilitated a more nuanced understanding of sleep neural dynamics across the lifespan. In this study, we tensorize multiscale multimodal electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals and apply Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling to quantify interactions between age scales and the use of pharmacological sleep aids on sleep stage transitions. Our cohort consists of 22 subjects in a crossover design study, where each subject received both a sleep aid and a placebo in different sessions. To understand these effects across the lifespan, three evenly distributed age groups were formed: 18-29, 30-49, and 50-66years. The methodological framework implemented here employs tensor-based machine learning techniques to compute continuous wavelet transform time-frequency features and utilizes a GARCH model to quantify sleep signal volatility across age scales. Support Vector Machines are used for feature ranking, and our analysis captures interactions between signal entropy, age, and sleep aid status across frequency bands, sleep transitions, and sleep stages. GARCH model results reveal statistically significant volatility clustering in EEG, EMG, and EOG signals, particularly during transitions between REM and non-REM sleep. Notably, volatility was higher in the 50-66 age group compared to the 18-29 age group, with marked fluctuations during transitions from deep sleep to REM sleep (standard deviation of 0.35 in the older group vs. 0.30 in the 18-29 age group, p < 0.05). Statistical comparisons of volatility across frequency bands, age scales, and sleep stages highlight significant differences attributable to sleep aid use. Mean conditional volatility parameterization of the GARCH model reveals directional influences, with a causality index of 0.75 from frontal to occipital regions during REM sleep transition periods. Our methodological framework identifies distinct neural behavior patterns across age groups associated with each sleep stage and transition, offering insights into the development of targeted interventions for sleep regularity across the lifespan.

SleepGCN: A transition rule learning model based on Graph Convolutional Network for sleep staging

Background and Objective:Automatic sleep staging is essential for assessing and diagnosing sleep disorders, serving millions of people who suffer from them. Numerous sleep staging models have been proposed recently, but most of them have not fully explored the sleep transition rules that are essential for sleep experts to identify sleep stages. Therefore, one objective of this paper is to develop an automatic sleep staging model to capture the transition rules between sleep stages. Methods:In this paper, we propose a novel sleep staging model named SleepGCN. It utilizes the deep features of electroencephalogram (EEG) and electrooculogram (EOG) signals extracted by the sleep representation learning (SRL) module, in conjunction with the transition rules learned by the sleep transition rule learning (STRL) module to identify sleep stages. Specifically, the SRL module utilizes the residual network (ResNet) and Long Short Term Memory (LSTM) structure to capture the deep time-invariant features and temporal information of each sleep stage from the two-channel EEG-EOG, and then applies a feature enhancement block to obtain the refined features. The STRL module employs a Graph Convolutional Network (GCN) and a transition rule matrix to capture transition rules between sleep stages based on the sequence labels of the input signals. Results:We evaluate SleepGCN on five public datasets: SleepEDF-20, SleepEDF-78, SHHS, DOD-H and DOD-O. Overall, SleepGCN achieves an accuracy of 89.70%, 87.70%, 86.16%, 82.07%, and 81.20%, alongside a macro-average F1-score of 85.20%, 82.70%, 77.69%, 72.44%, and 72.93% across these datasets, respectively. Conclusions:The results achieved by our proposed model are much better than those of all other compared models. The ablation study validates the contributions of the SRL and STRL modules proposed in SleepGCN to the sleep staging tasks. Additionally, it shows that the sleep staging model using two-channel EEG-EOG outperforms the model using single-channel EEG or EOG. Overall, SleepGCN is an effective solution for sleep staging using two-channel EEG-EOG.

Improved motor imagery training for subject's self-modulation in EEG-based brain-computer interface.

For the electroencephalogram- (EEG-) based motor imagery (MI) brain-computer interface (BCI) system, more attention has been paid to the advanced machine learning algorithms rather than the effective MI training protocols over past two decades. However, it is crucial to assist the subjects in modulating their active brains to fulfill the endogenous MI tasks during the calibration process, which will facilitate signal processing using various machine learning algorithms. Therefore, we propose a trial-feedback paradigm to improve MI training and introduce a non-feedback paradigm for comparison. Each paradigm corresponds to one session. Two paradigms are applied to the calibration runs of corresponding sessions. And their effectiveness is verified in the subsequent testing runs of respective sessions. Different from the non-feedback paradigm, the trial-feedback paradigm presents a topographic map and its qualitative evaluation in real time after each MI training trial, so the subjects can timely realize whether the current trial successfully induces the event-related desynchronization/event-related synchronization (ERD/ERS) phenomenon, and then they can adjust their brain rhythm in the next MI trial. Moreover, after each calibration run of the trial-feedback session, a feature distribution is visualized and quantified to show the subjects' abilities to distinguish different MI tasks and promote their self-modulation in the next calibration run. Additionally, if the subjects feel distracted during the training processes of the non-feedback and trial-feedback sessions, they can execute the blinking movement which will be captured by the electrooculogram (EOG) signals, and the corresponding MI training trial will be abandoned. Ten healthy participants sequentially performed the non-feedback and trial-feedback sessions on the different days. The experiment results showed that the trial-feedback session had better spatial filter visualization, more beneficiaries, higher average off-line and on-line classification accuracies than the non-feedback session, suggesting the trial-feedback paradigm's usefulness in subject's self-modulation and good ability to perform MI tasks.

Carbon Quantum Dot-Functionalized Dermis-Derived Transparent Electronic Skin for Multimodal Human Motion Signal Monitoring and Construction of Self-Powered Triboelectric Nanogenerator.

Electronic skin (e-skin) is considered as a highly promising interface for human-computer interaction systems and wearable electronic devices. Through elaborate design and assembly of various materials, it possesses multiple characteristics similar to human skin, including remarkable flexibility, stretchability, sensitivity to temperature and humidity, biocompatibility, and efficient interfacial ion/electron transport capabilities. Here, we innovatively integrate multifunctional carbon quantum dots (CQDs), which exhibit conductivity, antibacterial properties, ultraviolet absorption, and fluorescence emission, with poly(acrylic acid) and glycerin (Gly) into a three-dimensional network structure of natural goatskin collagen fibers. Through a top-down design strategy enhanced by hydrogen bond reconstruction, we successfully fabricated a novel transparent e-skin (PAC-eSkin). This e-skin exhibited significant tensile properties (4.94 MPa of tensile strength and 263.42% of a maximum breaking elongation), while also possessing Young's modulus similar to human skin (2.32 MPa). It is noteworthy that the functionalized CQDs used was derived from discarded goat hair, and the addition of Gly gave PAC-eSkin excellent antifreezing and moisturizing properties. Due to the presence of ultrasmall CQDs, which creates efficient ion/electron transport channels within PAC-eSkin, it could rapidly sense human motion and physiological signals (with a gauge factor (GF) of 1.88). Furthermore, PAC-eSkin had the potential to replace traditional electrode patches for real-time monitoring of electrocardiogram, electromyogram, and electrooculogram signals, with a higher SNR (signal-to-noise ratio) of 25.1 dB. Additionally, the customizable size and shape of PAC-eSkin offer vast possibilities for the construction of single-electrode triboelectric nanogenerator systems. We have reason to believe that the design and development of this transparent e-skin based on CQDs-functionalized dermal collagen matrices can pave a new way for innovations in human-computer interaction interfaces and their sensing application in diverse scenarios.

Multimodal machine learning approach for emotion recognition using physiological signals

This study explores a novel approach to emotion recognition through machine learning, addressing the limitations of previous methods. While deep learning has shown promise in this field, it often requires significant computational resources and time. In response, we propose a multimodal approach utilizing Feature-level Fusion (FLF) and Decision-level Fusion (DLF) to enhance performance while reducing complexity. The study focuses on integrating electroencephalogram (EEG), electromyography (EMG), and electrooculogram (EOG) signals. Signal preprocessing involves extracting statistical features, power spectral density (PSD), and incremental entropy analysis. Recursive Feature Elimination (RFE) is employed as a feature selector, facilitating the fusion of different signal features. Three fusion strategies are explored: EEG with EOG, EEG with EMG, and a combination of EEG with EOG and EMG. For classification, the Bagging Classifier and K-Nearest Neighbors Algorithm are chosen. Results demonstrate promising accuracy rates, with 95.7% for arousal, 96.41% for valence in subject-dependent classification, 93.68% for arousal, and 93.23% for valence in subject-independent classification. This approach presents a viable alternative to deep learning methods, offering improved performance with reduced computational burden.

Design of a LabVIEW-Based Virtual Instrument for the Disabled

The eye is a prominent organ with complex functions that play a crucial role in our daily lives. The ability to perceive objects from varying distances and angles is vital for navigation, interaction, and understanding the world around us. The coordination of multiple muscles to move the eye allows us to focus on specific points of interest and gather visual information effectively. The concept of using the electrical signals generated by eye movements, known as the electrooculogram (EOG), has been studied in this research. EOG signals can be captured by placing electrodes around the eyes and measuring the potential differences generated as the eyes move. It was noted that a limited number of studies were carried out on EOG-based designs. However, continued research and development in this area have the potential to enhance the quality of life for individuals with disabilities and contribute to our understanding of both the visual system and human-computer interaction. This paper focuses on addressing the relatively underexplored areas of vertical and horizontal eye movements, as well as eye blinking, in the context of electrooculogram (EOG)-based designs. In this research, sharp EOG signals were observed in different eye movement patterns, and those signals were utilized to control assistive devices for individuals with mobility impairments with the LabVIEW software interface.

An auditory brain-computer interface based on selective attention to multiple tone streams.

In this study, we attempted to improve brain-computer interface (BCI) systems by means of auditory stream segregation in which alternately presented tones are perceived as sequences of various different tones (streams). A 3-class BCI using three tone sequences, which were perceived as three different tone streams, was investigated and evaluated. Each presented musical tone was generated by a software synthesizer. Eleven subjects took part in the experiment. Stimuli were presented to each user's right ear. Subjects were requested to attend to one of three streams and to count the number of target stimuli in the attended stream. In addition, 64-channel electroencephalogram (EEG) and two-channel electrooculogram (EOG) signals were recorded from participants with a sampling frequency of 1000 Hz. The measured EEG data were classified based on Riemannian geometry to detect the object of the subject's selective attention. P300 activity was elicited by the target stimuli in the segregated tone streams. In five out of eleven subjects, P300 activity was elicited only by the target stimuli included in the attended stream. In a 10-fold cross validation test, a classification accuracy over 80% for five subjects and over 75% for nine subjects was achieved. For subjects whose accuracy was lower than 75%, either the P300 was also elicited for nonattended streams or the amplitude of P300 was small. It was concluded that the number of selected BCI systems based on auditory stream segregation can be increased to three classes, and these classes can be detected by a single ear without the aid of any visual modality.

Multi-degree-of-freedom unmanned aerial vehicle control combining a hybrid brain-computer interface and visual obstacle avoidance

ObjectiveThe difficulty of unmanned aerial vehicle (UAV) control recently lies in multidirectional movement in 3-dimensional space, improving control accuracy and manipulation safety. To address these challenges, a UAV control system that incorporates a hybrid brain-computer interface (hBCI), gyroscope and visual obstacle avoidance based on monocular depth estimation is proposed. Approach. We propose an efficient steady-state visual evoked potential (SSVEP) classification network (CL-NET) featuring a one-dimensional convolutional neural network, a long short-term memory module and an attention module to identify the user's intention for UAV movement in the front, back, left and right directions. The take-off, landing and rising control of the UAV is realized by an electrooculogram (EOG) signal detection algorithm, a blink state detector. In addition, the UAV can fly in an oblique state and rotate according to the current head posture detected by a gyroscope. Furthermore, an improved monocular depth estimation network is employed to design the autonomous obstacle avoidance module of the UAV, ensuring the safety of the brain-controlled system in practice. Main results. The proposed CL-NET delivers an accuracy of 98.67% on the public dataset and an accuracy of 97.92% on the self-collected dataset, both of which surpass the performance of state-of-the-art models. Additionally, we set up a brain control group and a remote control group to conduct practical experiments in a realistic environment. In the experiments involving sixteen subjects, the proposed UAV control system reached an average information transfer rate (ITR) of 44.09 bits/min, and the brain control group had a lower collision rate than the remote control group. Significance. The hybrid control method ensures that the multi-degree-of-freedom (multi-DOF) UAV control system maintains outstanding performance while ensuring good safety.

The effect of considering eye movement time in evaluating the efficiency of attentional networks.

The attention network test (ANT) is a tool for assessing the executive, alerting, and orienting components of attention. However, conflicting findings exist regarding the nature and correlation between attention networks. This study aims to investigate the influence of eye movement time on the assessment of attention network efficiency. Forty male students, with an average age of 20.8 ± 1.3 years, participated in the study. The revised attention network test was conducted concurrently with the recording of the electrooculogram signal. The electrooculogram signal was used to estimate eye placement time on target stimuli. Considering eye movement time for calculating the score of each network was proposed as a novel method. The study explored the nature of attention networks and their relationships, and revealed significant effects for attention networks with and without considering the eye movement time. Additionally, a significant correlation is observed between the alerting and orienting networks. However, no significant correlation is found between attention networks using the proposed method. Considering eye movement time alters the assessment of attention network efficiency and modifies the correlation among attention networks.

Implementation of Tools for Lessening the Influence of Artifacts in EEG Signal Analysis

This manuscript describes an implementation of scripts of code aimed at reducing the influence of artifacts, specifically focused on ocular artifacts, in the measurement and processing of electroencephalogram (EEG) signals. This process is of importance because it benefits the analysis and study of long trial samples when the appearance of ocular artifacts cannot be avoided by simply discarding trials. The implementations provided to the reader illustrate, with slight modifications, previously proposed methods aimed at the partial or complete elimination of EEG channels or components obtained after independent component analysis (ICA) of EEG signals. These channels or components are those that resemble the electro-oculogram (EOG) signals in which artifacts are detected. In addition to the description of each of the provided functions, examples of utilization and illustrative figures will be included to show the expected results and processing pipeline.

Multimodal Emotion Recognition based on EEG and EOG Signals evoked by the Video-odor Stimuli.

Affective data is the basis of emotion recognition, which is mainly acquired through extrinsic elicitation. To investigate the enhancing effects of multi-sensory stimuli on emotion elicitation and emotion recognition, we designed an experimental paradigm involving visual, auditory, and olfactory senses. A multimodal emotional dataset (OVPD-II) that employed the video-only or video-odor patterns as the stimuli materials, and recorded the electroencephalogram (EEG) and electrooculogram (EOG) signals, was created. The feedback results reported by subjects after each trial demonstrated that the video-odor pattern outperformed the video-only pattern in evoking individuals' emotions. To further validate the efficiency of the video-odor pattern, the transformer was employed to perform the emotion recognition task, where the highest accuracy reached 86.65% (66.12%) for EEG (EOG) modality with the video-odor pattern, which improved by 1.42% (3.43%) compared with the video-only pattern. What's more, the hybrid fusion (HF) method combined with the transformer and joint training was developed to improve the performance of the emotion recognition task, which achieved classify accuracies of 89.50% and 88.47% for the video-odor and video-only patterns, respectively.

Application Specific Reconfigurable Processor for Eyeblink Detection from Dual-Channel EOG Signal

The electrooculogram (EOG) is one of the most significant signals carrying eye movement information, such as blinks and saccades. There are many human–computer interface (HCI) applications based on eye blinks. For example, the detection of eye blinks can be useful for paralyzed people in controlling wheelchairs. Eye blink features from EOG signals can be useful in drowsiness detection. In some applications of electroencephalograms (EEGs), eye blinks are considered noise. The accurate detection of eye blinks can help achieve denoised EEG signals. In this paper, we aimed to design an application-specific reconfigurable binary EOG signal processor to classify blinks and saccades. This work used dual-channel EOG signals containing horizontal and vertical EOG signals. At first, the EOG signals were preprocessed, and then, by extracting only two features, the root mean square (RMS) and standard deviation (STD), blink and saccades were classified. In the classification stage, 97.5% accuracy was obtained using a support vector machine (SVM) at the simulation level. Further, we implemented the system on Xilinx Zynq-7000 FPGAs by hardware/software co-design. The processing was entirely carried out using a hybrid serial–parallel technique for low-power hardware optimization. The overall hardware accuracy for detecting blinks was 95%. The on-chip power consumption for this design was 0.8 watts, whereas the dynamic power was 0.684 watts (86%), and the static power was 0.116 watts (14%).

An RNN-based Hybrid Model for Classification of Electrooculogram Signal for HCI

An RNN-based Hybrid Model for Classification of Electrooculogram Signal for HCI

EOG acquisition system based on ATmega AVR microcontroller

This paper presents a hardware and software of an electrooculogram (EOG) acquisition system based on ATmega AVR microcontroller for the acquisition of vertical and horizontal eye movements. The presented system is used to acquire a dataset of eye movements for volunteers. This system gives two channels representing vertical and horizontal EOG signals. The frequency range of the EOG signal is known to be 0.1 to 10 Hz, and hence this frequency range is isolated with a High-Pass Filter (HPF) with a cutoff frequency of 0.1 Hz followed by a Low-Pass Filter (LPF) with a cutoff frequency of 10 Hz. The EOG acquisition system is interfaced with an ATmega AVR microcontroller to acquire a dataset that can be used for controlling hardware such as Light Emitting Diodes (LEDs), wheelchair, and robot arm. The presented system is composed of EOG signal acquisition, Ag/AgCl electrodes, analog-to-digital converter through Arduino Mega 2560 board microcontroller unit, trainer board, laptop, keypad, and Liquid Crystal Display (LCD). The eye movement is detected by measuring the potential difference between cornea and retina using five Ag–Agcl disposable electrodes. Different volunteers of different ages at different times have been treated with the presented system to obtain data. Classified vertical and horizontal EOG signals and the basic eye movements e.g., open eye, left, right, up, and down can be used to control robots and wheelchairs for rehabilitation purposes.

A Comparative Investigation of Wavelet Families for Classification of EOG Signals Related to Healthy and ADHD Children.

Based on previous research, there are differences between eye movements of people with attention-deficit hyperactivity disorder (ADHD) and of healthy people, as a result, the existence of differences regarding the electrooculogram (EOG) signals of the 2 groups exists. Thus, this study aimed to examine the recorded EOG signals of 30 ADHD children and 30 healthy children while performing an attention-related task. For this purpose, the EOG signals of these 2 groups were decomposed utilizing various wavelet functions. Afterward, features, including mean, energy, and standard deviation (SD) of approximation and detail wavelet coefficients were calculated. The Davies-Bouldin (DB) index was used for the evaluation of the feature space quality. Finally, the 2 groups were classified using one-dimensional feature vector and support vector machine (SVM). The SD of detail coefficients (db4) was selected as the most effective feature for separating the 2 groups. Statistical analysis revealed that the values of energy and SD of EOG signals' detail coefficients were significantly lower in the ADHD group in comparison with the healthy group (P<.001). These results showed that the speed of the ADHD group's eye movements was slower due to the fact that the high-frequency band activity of EOG signals in the healthy group was higher. In addition, the EOG signals were classified with a detection accuracy of 83.42 ± 3.8%. The results of this study can be applied in designing an EOG biofeedback protocol to treat or mitigate the symptoms of ADHD patients.

Emotion Recognition Through Combining EEG and EOG Over Relevant Channels With Optimal Windowing

For dimensional emotion recognition, electroencephalography (EEG) signals and electrooculogram (EOG) signals are often combined to improve the performance of classifiers, as each of them provides complementary features to the other. In this article, we combine the EEG signal on the relevant channels with the EOG signal to boost the recognition accuracy. We first explore the mutual information (MI) of all EEG channels and only select emotion-related channels, i.e., channels with more MI are retained, since the emotion recognition performance can be degraded by the interference between uncorrelated channels, while the computational complexity is significant if all EEG channels are used for recognition. While the optimal lengths of EEG and EOG signals for emotion recognition are still uncertain, we systematically investigate the effects of time-window size on emotion recognition. This strategy not only increases the number of training samples, but also reduces the feature redundancy. At this stage, we not only extract multiple statistical features but also employ the increment entropy to find abrupt changes in EEG signals. The experimental results show that 13 out of 32 EEG channels were selected by the proposed channel selection algorithm, and these selected channels can already produce accurate emotion predictions. We found that using optimal time-windows to split EEG and EOG signals into several thin slices and then combine them can further enhance the emotion recognition performance, where the time-windows of 4, 5, 6, and 10 s allow the combined signals to achieve very high accuracy.

Development of electrooculogram based human computer interface system using deep learning

The patients with diseases that cause severe movement disabilities was noticeably increasing. These disabilities made patients unable to carry out their daily activities or interact with their external environment. However, the existence of human-computer interfaces (HCI) gave those patients a new hope to be able to interact once again. HCI enabled these patients to communicate with their environment by recognizing the movement of their eyes. Eye movements are recorded by an electro-oculogram (EOG) through some electrodes that are put vertically and horizontally on the eyes. In this paper, EOG vertical and horizontal signals were analyzed to detect six eye movements (up, down, right, left, double blinking, and center). Three deep learning models namely convolution neural network (CNN), visual geometry group (VGG), and inception had been examined on filtered EOG signals. The experimental results reveal the superiority of the inception model in providing the best average accuracy 96.4%. Accordingly, a writing system is presented based on the detected movements.

Development of electrooculogram based human computer interface system using deep learning

The patients with diseases that cause severe movement disabilities was noticeably increasing. These disabilities made patients unable to carry out their daily activities or interact with their external environment. However, the existence of human-computer interfaces (HCI) gave those patients a new hope to be able to interact once again. HCI enabled these patients to communicate with their environment by recognizing the movement of their eyes. Eye movements are recorded by an electro-oculogram (EOG) through some electrodes that are put vertically and horizontally on the eyes. In this paper, EOG vertical and horizontal signals were analyzed to detect six eye movements (up, down, right, left, double blinking, and center). Three deep learning models namely convolution neural network (CNN), visual geometry group (VGG), and inception had been examined on filtered EOG signals. The experimental results reveal the superiority of the inception model in providing the best average accuracy 96.4%. Accordingly, a writing system is presented based on the detected movements.

KL Divergence-based transfer learning for cross-subject eye movement recognition with EOG signals.

Human-machine interfaces (HMIs) based on Electro-oculogram (EOG) signals have been widely explored. However, due to the individual variability, it is still challenging for an EOG-based eye movement recognition model to achieve favorable results among cross-subjects. The classical transfer learning methods such as CORrelation Alignment (CORAL), Transfer Component Analysis (TCA), and Joint Distribution Adaptation (JDA) are mainly based on feature transformation and distribution alignment, which do not consider similarities/dissimilarities between target subject and source subjects. In this paper, the Kullback-Leibler (KL) divergence of the log-Power Spectral Density (log-PSD) features of horizontal EOG (HEOG) between the target subject and each source subject is calculated for adaptively selecting partial subjects that suppose to have similar distribution with target subject for further training. It not only consider the similarity but also reduce computational consumption. The results show that the proposed approach is superior to the baseline and classical transfer learning methods, and significantly improves the performance of target subjects who have poor performance with the primary classifiers. The best improvement of Support Vector Machines (SVM) classifier has improved by 13.1% for subject 31 compared with baseline result. The preliminary results of this study demonstrate the effectiveness of the proposed transfer framework and provide a promising tool for implementing cross-subject eye movement recognition models in real-life scenarios.