Wavelet based head movement artifact removal from electrooculography signals

Electrooculography (EOG) signals, which can be used to infer the intentions of a user based on eye movements, are widely used in human-computer interface (HCI) systems. Most existing EOG-based HCI systems incorporate a limited number of commands because they generally associate different commands with a few different types of eye movements, such as looking up, down, left, or right. This paper presents a novel single-channel EOG-based HCI that allows users to spell asynchronously by only blinking. Forty buttons corresponding to 40 characters displayed to the user via a graphical user interface are intensified in a random order. To select a button, the user must blink his/her eyes in synchrony as the target button is flashed. Two data processing procedures, specifically support vector machine (SVM) classification and waveform detection, are combined to detect eye blinks. During detection, we simultaneously feed the feature vectors extracted from the ongoing EOG signal into the SVM classification and waveform detection modules. Decisions are made based on the results of the SVM classification and waveform detection. Three online experiments were conducted with eight healthy subjects. We achieved an average accuracy of 94.4% and a response time of 4.14 s for selecting a character in synchronous mode, as well as an average accuracy of 93.43% and a false positive rate of 0.03/min in the idle state in asynchronous mode. The experimental results, therefore, demonstrated the effectiveness of this single-channel EOG-based speller.

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

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.

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

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.

Multiply-innervated muscle fibers (MIFs) are peculiar to the extraocular muscles as they are non-twitch but produce a slow build up in tension on repetitive stimulation. The motoneurons innervating MIFs establish en grappe terminals along the entire length of the fiber, instead of the typical en plaque terminals that singly-innervated muscle fibers (SIFs) motoneurons establish around the muscle belly. MIF motoneurons have been proposed to participate only in gaze holding and slow eye movements. We aimed to discern the function of MIF motoneurons by recording medial rectus motoneurons of the oculomotor nucleus. Single-unit recordings in awake cats demonstrated that electrophysiologically-identified medial rectus MIF motoneurons participated in different types of eye movements, including fixations, rapid eye movements or saccades, convergences, and the slow and fast phases of the vestibulo-ocular nystagmus, the same as SIF motoneurons did. However, MIF medial rectus motoneurons presented lower firing frequencies, were recruited earlier and showed lower eye position (EP) and eye velocity (EV) sensitivities than SIF motoneurons. MIF medial rectus motoneurons were also smaller, had longer antidromic latencies and a lower synaptic coverage than SIF motoneurons. Peristimulus time histograms (PSTHs) revealed that electrical stimulation to the myotendinous junction, where palisade endings are located, did not recurrently affect the firing probability of medial rectus motoneurons. Therefore, we conclude there is no division of labor between MIF and SIF motoneurons based on the type of eye movement they subserve.SIGNIFICANCE STATEMENT In addition to the common singly-innervated muscle fiber (SIF), extraocular muscles also contain multiply-innervated muscle fibers (MIFs), which are non-twitch and slow in contraction. MIF motoneurons have been proposed to participate only in gaze holding and slow eye movements. In the present work, by single-unit extracellular recordings in awake cats, we demonstrate, however, that both SIF and MIF motoneurons, electrophysiologically-identified, participate in the different types of eye movements. However, MIF motoneurons showed lower firing rates (FRs), recruitment thresholds, and eye-related sensitivities, and could thus contribute to the fine adjustment of eye movements. Electrical stimulation of the myotendinous junction activates antidromically MIF motoneurons but neither MIF nor SIF motoneurons receive a synaptic reafferentation that modifies their discharge probability.

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.

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.

Video-oculography eye tracking towards clinical applications: A review

Vestibulo-ocular reflexes (VORs) are the dominating contributors to gaze stabilization in all vertebrates. During horizontal head movements, abducens motoneurons form the final element of the reflex arc that integrates visuovestibular inputs into temporally precise motor commands for the lateral rectus eye muscle. Here, we studied a possible differentiation of abducens motoneurons into subtypes by evaluating their morphology, discharge properties, and synaptic pharmacology in semi-intact in vitro preparations of larval Xenopus laevis Extracellular nerve recordings during sinusoidal head motion revealed a continuum of resting rates and activation thresholds during vestibular stimulation. Differences in the sensitivity to changing stimulus frequencies and velocities allowed subdividing abducens motoneurons into two subgroups, one encoding the frequency and velocity of head motion (Group I), and the other precisely encoding angular velocity independent of stimulus frequency (Group II). Computational modeling indicated that Group II motoneurons are the major contributor to actual eye movements over the tested stimulus range. The segregation into two functional subgroups coincides with a differential activation of glutamate receptor subtypes. Vestibular excitatory inputs in Group I motoneurons are mediated predominantly by NMDA receptors and to a lesser extent by AMPA receptors, whereas an AMPA receptor-mediated excitation prevails in Group II motoneurons. Furthermore, glycinergic ipsilateral vestibular inhibitory inputs are activated during the horizontal VOR, whereas the tonic GABAergic inhibition is presumably of extravestibular origin. These findings support the presence of physiologically and pharmacologically distinct functional subgroups of extraocular motoneurons that act in concert to mediate the large dynamic range of extraocular motor commands during gaze stabilization.SIGNIFICANCE STATEMENT Outward-directed gaze-stabilizing eye movements are commanded by abducens motoneurons that combine different sensory inputs including signals from the vestibular system about ongoing head movements (vestibulo-ocular reflex). Using an amphibian model, this study investigates whether different types of abducens motoneurons exist that become active during different types of eye movements. The outcome of this study demonstrates the presence of specific motoneuronal populations with pharmacological profiles that match their response dynamics. The evolutionary conservation of the vestibulo-ocular circuitry makes it likely that a similar motoneuronal organization is also implemented in other vertebrates. Accordingly, the physiological and pharmacological understanding of specific motoneuronal contributions to eye movements might help in designing drug therapies for human eye movement dysfunctions such as abducens nerve palsy.

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.

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) 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.

People with severe disabilities require assistance to perform their routine activities; a Human–Machine Interface (HMI) will allow them to activate devices that respond according to their needs. In this work, an HMI based on electrooculography (EOG) is presented, the instrumentation is placed on portable glasses that have the task of acquiring both horizontal and vertical EOG signals. The registration of each eye movement is identified by a class and categorized using the one hot encoding technique to test precision and sensitivity of different machine learning classification algorithms capable of identifying new data from the eye registration; the algorithm allows to discriminate blinks in order not to disturb the acquisition of the eyeball position commands. The implementation of the classifier consists of the control of a three-wheeled omnidirectional robot to validate the response of the interface. This work proposes the classification of signals in real time and the customization of the interface, minimizing the user’s learning curve. Preliminary results showed that it is possible to generate trajectories to control an omnidirectional robot to implement in the future assistance system to control position through gaze orientation.