It's in your eyes

Investigating neural correlates of locomotion transition via temporal relation of EEG and EOG-recorded eye movements

Wavelet transform is one of the favoured tool for analyzing the biomedical signals. In this paper, we describe the identification of Electro oculography (EOG) signals of eye movement potentials by using wavelet transform which gives more feature informations than FFT. The capability of wavelet transform is to distribute the signal energy over the change of time in different frequency bands. The EOG signals are captured using electrodes placed on the forehead around the eyes to record the eye movements using the Neuro fax EEG-9200. The wavelet features are used to determine the characteristic of eye movement waveform. The recorded data is composed of an eye movement toward vertical, upward and downward and horizontal, left and right movements. From the result, it is proved that different EOG signals exhibit differences in signals energy, in which their corresponding dominant energy are: level 6 (8-16Hz) for left eye movement, level 7 (4-8Hz) for upward, level 8 (2-4Hz) for right and level 9 (1-2Hz) for downward.

An efficient wavelet-based architectures are designed and presented during this research paper which may be used to denoising of Electrooculography (EOG) signals. Denoising of electrooculography (EOG) signals is a challenging task as the noise and signal share the same level of frequency spectrum. Electrooculography (EOG) plays a crucial role within the event of ophthalmological diagnosis and human computer interface (HCI) applications. Firstly, EOG signals are obtained from physio net fantasia database. Reducing the noises involved within the EOG signals is difficult because of sharing of same bandwidth for both signal and noise. For this reduction designing the methods of wavelet architectures like db4 in MATLAB software for denoising, as well as Signal to noise (SNR) and Mean Square Error (MSE) that also display the qualities for assessment and also compare with existing proposed methods like db3, db5. After simulation in MATLAB adopt Intel Quartus Standard Edition for Digital Signal Processing (DSP), which is used for engineering modelling and production of the proposed denoising approach with hardware consumption results. For hardware consumption, de-soc cyclone V FPGA series (5CSEMA5F31C6) is used by implementation of designed proposed model.

In this paper, a user independent human computer interface system using eye movement and blink feature detection is introduced. A hands free interface between computer and human can potentially replace the traditional human computer interface devices like mouse, keyboard etc. This technology is intended to give functionality to peoples with severe motor disabilities to control a computer by just moving their eyes. This paper describes a method of controlling the mouse cursor on a computer screen using the electrical potentials developed by eye movements known as Electrooculography. Electrooculography (EOG) signal is used to detect eye movement and blink features. The EOG signal is recorded from electrodes placed at appropriate positions around the eyes. The captured EOG signal is then analyzed to detect and classify eye movement features of interest. The detected features were then used to generate control signals to control a mouse cursor. The cursor control application is implemented offline. The detection accuracy for blinks, horizontal and vertical saccades are 100%, 97% and 93% respectively.

In this paper, we propose a real-time human-robot interface (HRI) system, where Electrooculography (EOG) and Electromyography (EMG) signals were decoded to perform reach-to-grasp movements. For that, five different eye movements (up, down, left, right and rest) were classified in real-time and translated into commands to steer an industrial robot (UR-10) to one of the four approximate target directions. Thereafter, EMG signals were decoded to perform the grasping task using an attached gripper to the UR-10 robot arm. The proposed system was tested offline on three different healthy subjects, and mean validation accuracy of 93.62% and 99.50% were obtained across the three subjects for EOG and EMG decoding, respectively. Furthermore, the system was successfully tested in real-time with one subject, and mean online accuracy of 91.66% and 100% were achieved for EOG and EMG decoding, respectively. Our results obtained by combining real-time decoding of EOG and EMG signals for robot control show overall the potential of this approach to develop powerful and less complex HRI systems. Overall, this work provides a proof-of-concept for successful real-time control of robot arms using EMG and EOG signals, paving the way for the development of more dexterous and human-controlled assistive devices.

EOG biofeedback protocol based on selecting distinctive features to treat or reduce ADHD symptoms

Gaming control becomes popular based on the development of human-computer interface (HCI). Among many kinds of physiological signals, Electrooculography (EOG) signal is more stable which can be used to control HCI systems based on eye movement detection and signal processing methods. However, there are currently no effective multi-directional classification methods for monitoring eye movements. In addition, many EOG-based HCI systems have been developed with traditional wet electrodes. Those traditional electrodes require conductive gel and skin preparation on some users. Here, we describe a signal processing method used in a wireless EOG-based HCI system with dry electrodes for detecting eye movements to have 9 options. This system includes wireless EOG signal acquisition device, dry electrodes and an EOG signal processing algorithm. The EOG signal processing algorithm is based on 9 options of eye movement and blink signals. The results demonstrated an application of baseball game control using the proposed wireless HCI system. This system provides an effective and convenient method for eye movement detection.

Electrooculography (EOG) signals acquire different types of eye movements, which can be employed for human-machine interfaces (HMI) and also for diagnostic purposes. In realistic circumstances, EOG signals tend to be contaminated with noise due to unconstrained head movements. This noise degrades the signal quality as well as increases the misclassification rate of eye movement detection. General filtering and preprocessing techniques are unable to remove this noise. This paper presents a novel approach of head-movement noise removal from EOG signals by employing a biorthogonal wavelet transform to extract the level-4 approximation coefficients, which are also exploited as features classified by k- nearest neighbor (kNN) classifier. This approach enhances the classification performance remarkably. Even when this wavelet based technique is applied as denoising technique and features to the prior arts, it improves the performance of those existing techniques too. Moreover, the proposed technique is suitable for real time applications.

Sleep is a natural periodic state of rest for the body, in which the eyes usually close and consciousness is completely or partially lost. Consequently, there is a decrease in bodily movements and responsiveness to external stimuli. Slow wave sleep is of immense interest as it is the most restorative sleep stage during which the body recovers from weariness. During this sleep stage, electroencephalographic (EEG) and electro-oculographic (EOG) signals interfere with each other and they share a temporal similarity. In this investigation we used the EEG and EOG signals acquired from 10 patients undergoing overnight polysomnography with their sleep stages determined by certified sleep specialists based on RK rules. In this pilot study, we performed spectral estimation of EEG signals by Autoregressive (AR) modeling, and then used Itakura Distance to measure the degree of similarity between EEG and EOG signals. We finally calculated the statistics of the results and displayed them in an easy to visualize fashion to observe tendencies for each sleep stage. We found that Itakura Distance is the smallest for sleep stages 3 and 4. We intend to deploy this feature as an important element in automatic classification of sleep stages.

Sleepiness at the wheel is an important factor contributing to road traffic accidents. Based on the characteristic changes in Electroencephalography (EEG) and Electrooculography (EOG) signals, a dozing state is refined into three sub-states: the onset, duration, and end state. Each state is characterized by different physiological indicators such as the EEG alpha waves, the rising edge, and falling edge waveforms in EOG signals. To enable real-time detection of these physiological indicators, we propose a framework integrating three Convolutional Neural Network-Long Short-Term Memory-Self-Attention (CLSA) models, which combine CNN-based local feature extraction with self-attention mechanism for global context capture. The framework is evaluated for performance on continuous test data from 12 subjects. Our results demonstrate that by detecting alpha waves and the rising edge waveform, the alpha wave epoch (AWE) at the onset of the dozing state can be identified with high accuracy and precision. Thus, the onset sub-state is calculated as the period from the start time of the rising edge waveform to the time when the AWE is valid. Subsequently, the duration sub-state corresponds to the sustained presence of alpha waves. Furthermore, the falling edge waveform is detected with high accuracy, enabling the classification of the end state into two distinct phenomena: alpha blocking phenomenon or alpha wave attenuation-disappearance phenomenon, representing the sleepiness level-relaxed wakefulness or sleep onset, respectively. Utilizing three-channel signal processing, this framework provides a promising approach for real-time sleepiness detection in real-world driving scenarios.

People with disabilities suffer from inability to communicate with their surroundings, so Human-Computer Interaction (HCI) technologies are used to have a means of communication for people with disabilities with their surroundings. HCI is an emerging technology in the disciplines of Artificial Intelligence and Biomedical Engineering. To power an external device, HCI technology uses several basic signals such as ECG, EMG, and EEG. Electrooculography (EOG) is a technique for measuring the potential difference between the cornea and the retina located between the front and back of the human eye, and the main application of EOG is to determine the directions of different eye movements. This study aims to assess eye movement for communication by persons with disabilities using electrocardiogram (EOG) data. In this study, the Supporting Vector Machine (SVM) and Long- Short term memory (LSTM) classification techniques was used and two types of features (statistical and time domain features) were used. Classification accuracy was 90.7% and 93.9% when using SVM with statistical domain and time domain features, respectively ,whereas Classification accuracy was 90.1% when using LSTM .

Eye tracking is the process of measuring the movement of the eye and determining the direction where the user is looking (point of gaze). Using eye movement and gaze tracking for communication and control is especially beneficial for disabled people with minimal ability to perform voluntary motion. Our research is directed to development of an eye tracker based on Electro-oculography (EOG) technique allowing physically impaired patients to control their wheelchairs or home robots. This paper presents the design of a prototype of the Development Kit for recording and processing of EOG signals. The structure and functional blocks of the Development Kit together with the electrical schemas of analogue front-end and digital part are presented. For visualization and on-line processing of recorded EOG signals a special Toolbox is designed. Finally, the examples of recorded EOG signals for different gaze directions are presented.

Electro-encephalography (EEG) and electro-oculography (EOG) are methods of electrophysiological monitoring that have potentially fruitful applications in neuroscience, clinical exploration, the aeronautical industry, and other sectors. These methods are often the most straightforward way of evaluating brain oscillations and eye movements, as they use standard laboratory or mobile techniques. This review describes the potential of EEG and EOG systems and the application of these methods in aeronautics. For example, EEG and EOG signals can be used to design brain-computer interfaces (BCI) and to interpret brain activity, such as monitoring the mental state of a pilot in determining their workload. The main objectives of this review are to, (i) offer an in-depth review of literature on the basics of EEG and EOG and their application in aeronautics; (ii) to explore the methodology and trends of research in combined EEG-EOG studies over the last decade; and (iii) to provide methodological guidelines for beginners and experts when applying these methods in environments outside the laboratory, with a particular focus on human factors and aeronautics. The study used databases from scientific, clinical, and neural engineering fields. The review first introduces the characteristics and the application of both EEG and EOG in aeronautics, undertaking a large review of relevant literature, from early to more recent studies. We then built a novel taxonomy model that includes 150 combined EEG-EOG papers published in peer-reviewed scientific journals and conferences from January 2010 to March 2020. Several data elements were reviewed for each study (e.g., pre-processing, extracted features and performance metrics), which were then examined to uncover trends in aeronautics and summarize interesting methods from this important body of literature. Finally, the review considers the advantages and limitations of these methods as well as future challenges.

Electrooculography (EOG) is a cost-effective and non-invasive technique for measuring eye movements, widely used in clinical diagnostics and for eye-gaze estimation used to develop eye movement-based human-computer interface (HCI) systems. While EOG signals are often considered insensitive to illumination changes in the EOG-based HCI system domain, a standard clinical ophthalmic test indicates that the EOG signal amplitude for the same ocular displacement oscillates upon both light and dark onset. Hence, this study investigates the EOG sensitivity with the aim of clarifying any inconsistencies in the literature specifically for HCI applications. A recording protocol was designed to capture the behaviour of EOG signals under two typical indoor illumination levels (150 lx and 500 lx) for comparative analysis. Results obtained from four participants confirm that while steady-state sensitivity values are comparable between illumination levels, significant fluctuations after light onset may lead to over- or under-estimation of the user's gaze, adversely impacting the gaze estimation accuracy. These findings suggest that while EOG-based HCI systems are largely insensitive to illumination levels under steady-state conditions, lighting transitions necessitate frequent recalibration to maintain accuracy. This work thus highlights the need to model this oscillatory process, enabling real-time compensation and minimising the need for recalibration in real-world EOG applications, and lays the groundwork for future studies.

Comfortable and reliable wearable systems for home-based automatic sleep staging are crucial for remotely diagnosing sleep disorders. Among various categories of biosignals, electrooculography (EOG) has emerged as a promising modality due to its ease of acquisition and robustness to noise. However, research on EOG-based sleep staging remains limited, with most studies constrained to the standard 2-channel EOG configuration recommended by the American Academy of Sleep Medicine (AASM) scoring manual. Thus, it may lack higher spatial information, hindering fully capturing the tiny sleep-related eye movements. In this study, we developed a novel eye mask-based EOG acquisition system capable of collecting 14-channel spatially diverse EOG signals. Building upon this hardware innovation, we further proposed a dedicated deep learning model, MTNet, combining multi-scale feature extraction and temporal attention, to fully exploit the rich information from the collected EOG data for accurate sleep staging , which is capable of effectively capturing relevant sleep features from EOG signals. We also evaluated the importance of each EOG channel for sleep staging, providing recommendations for EOG-based data acquisition in-home sleep monitoring systems. Our study involved 24 participants, resulting in 48 overnight sleep recordings. In K-fold cross-validation, 14-channel EOG achieved an average accuracy of 83.2%, a Cohen’s kappa coefficient of 0.77, and an MF1 score of 77.7%. These results surpass the performance of the standard EOG, which yielded an accuracy of 82.3%, a Cohen’s kappa coefficient of 0.76, and an MF1 score of 76.1%. In particular, the 14-channel EOG demonstrated superior detection accuracy in the N1 and REM sleep stages compared to the standard setup. In conclusion, the proposed 14-channel EOG collection system—formed as a wearable eye mask—shows significant potential as a high-dimensional biosignal source for home-based sleep monitoring. By combining 14-channel EOG acquisition with the MTNet model, and investigating the importance of channels, this study marks a substantial step forward in an accurate and user-friendly sleep monitoring solution.