Electrooculography Signals Research Articles

Robust EOG-based saccade recognition using multi-channel blind source deconvolution.

Human activity recognition (HAR) is a research hotspot in the field of artificial intelligence and pattern recognition. The electrooculography (EOG)-based HAR system has attracted much attention due to its good realizability and great application potential. Focusing on the signal processing method of the EOG-HAR system, we propose a robust EOG-based saccade recognition using the multi-channel convolutional independent component analysis (ICA) method. To establish frequency-domain observation vectors, short-time Fourier transform (STFT) is used to process time-domain EOG signals by applying the sliding window technique. Subsequently, we apply the joint approximative diagonalization of eigenmatrix (JADE) algorithm to separate the mixed signals and choose the "clean" saccadic source to extract features. To address the problem of permutation ambiguity in a case with a six-channel condition, we developed a constraint direction of arrival (DOA) algorithm that can automatically adjust the order of eye movement sources according to the constraint angle. Recognition experiments of four different saccadic EOG signals (i.e. up, down, left and right) were conducted in a laboratory environment. The average recognition ratios over 13 subjects were 95.66% and 97.33% under the between-subjects test and the within-subjects test, respectively. Compared with "bandpass filtering", "wavelet denoising", "extended infomax algorithm", "frequency-domain JADE algorithm" and "time-domain JADE algorithm, the recognition ratios obtained relative increments of 4.6%, 3.49%, 2.85%, 2.81% and 2.91% (within-subjects test) and 4.91%, 3.43%, 2.21%, 2.24% and 2.28% (between-subjects test), respectively. The experimental results revealed that the proposed algorithm presents robust classification performance in saccadic EOG signal recognition.

Development of an EOG-based system to control a serious game

Eye movements are the least affected by disabilities of the central motor system. This means that eye movements have great potential in the development of applications adapted to people with motor disabilities. This article describes the development of an electrooculography (EOG) system to control a serious computer game. The goal is to allow people with motor disabilities to play a serious game and improve control over their oculomotor systems. The developed system consists of three subsystems: a low-cost hardware device to acquire the EOG signals, methods to process them digitally, and a serious game controlled exclusively by eye movements. Fourteen able-bodied volunteers of different ages and genders tested the computer game. The evaluation of the system’s interaction capacity with the computer game was based on three metrics: score obtained, number of successful interactions, and number of errors committed.

A Bypass-Switching SAR ADC With a Dynamic Proximity Comparator for Biomedical Applications

A bypass-switching successive approximation (BSSA) register analog-to-digital converter (ADC) employing a newly proposed dynamic proximity comparator is presented in this paper. By exploiting the current characteristics of a current correlator, the proposed comparator generates the bypass signal directly along with the polarity comparison result. The bypass window size can be adjusted to optimize the power reduction for different sensing applications with low-voltage sensitivity. A BSSA logic circuit is adopted with reduced number of logic gates as well as switching power. A prototyped chip including a proposed ADC has been designed and fabricated in a 0.18- $\mu \text{m}$ CMOS process occupying an area of 0.041 mm2. With a supply voltage of 0.6 V and at a sampling rate of 50 kS/s, the measured signal-to-noise-distortion ratio (SNDR) and spurious free dynamic range (SFDR) are 56.9 and 68.7 dB, respectively, achieving an effective number of bits (ENoB) of 9.16. The ADC consumes power of 114 nW while digitizing sinusoidal inputs. With a bypass window size of ±32 LSBs, the designed ADC consumes only 76 nW when quantizing full-scale electrocardiography signals that are generated from a certified commercial simulator, exhibiting a figure-of-merit (FoM) of 2.66 fJ/conversion-step. Besides ECG signals, the ADC is also demonstrated by digitizing electromyography and electrooculography signals from human bodies.

A dual model approach to EOG-based human activity recognition

Abstract Ongoing eyeball activities can be recorded as Electrooculography (EOG) to discover the links between human activities and eye movements. In the present work, we propose a dual model to achieve human activity recognition (HAR) under a specific task background. Specifically, the “EOG Signals Recognition (ESR)” model is used to recognize basic eye movement unit signals collected under different activities; the “Activities Relationship (AR)” model is utilized to describe the contextual relationship among different activities. Furthermore, we introduce a confidence parameter to comprehensively analyze and judge outputs of the above two models. To evaluate the performance of the proposed algorithm, the experiments have been performed under an office scene over 8 subjects. The average recognition accuracy achieves 88.15% according to 3 types of activities (i.e., reading, writing, and resting). Experimental results reveal that the EOG-based dual model HAR algorithm presents an excellent classification performance.

Implementation of a Cost-Effective Didactic Prototype for the Acquisition of Biomedical Signals

This paper presents the implementation of a cost-effective didactic prototype, which was designed as a tool for theoretical and practical learning in the biomedical instrumentation area for engineering students. The prototype provides integrated hardware and software components that allow online acquisition, processing, and visualization of electrocardiographic (ECG), electroencephalographic (EEG), electromyographic (EMG), and electrooculographic (EOG) signals, as well as measurements of bio-impedance from the skin. A control system using an Arduino Uno board and the PIC16F877A and PIC18F2550 microcontrollers was implemented. This control system allows selecting the type of module; the lead to be used in the ECG module; the input channel for the EEG, EMG, and EOG modules; and controlling the signal generator for the bioimpedance module. In addition, a graphical interface was developed in LabVIEW, in which all the acquired biomedical signals can be visualized in real time. It is highlighted as a novelty the modular implementation of the prototype, the incorporation of five modules in a single device and the graphical user-friendly interface. The final result is a low-cost device capable of processing and visualizing bioelectric signals through an interface in LabVIEW, which also allows the user to interact with each of the stages.

An EOG-Based Human-Machine Interface to Control a Smart Home Environment for Patients With Severe Spinal Cord Injuries.

This paper presents an asyn-chronous electrooculography (EOG)-based human-machine interface (HMI) for smart home environmental control with the purpose of providing daily assistance for severe spinal cord injury (SCI) patients. The proposed HMI allows users to interact with a smart home environment through eye blinking. Specifically, several buttons, each corresponding to a control command, randomly flash on a graphical user interface. Each flash of the buttons functions as a visual cue for the user to blink. To issue a control command, the user can blink synchronously with the flashes of the corresponding button. Through detecting blinks based on the recorded EOG signal, the target button and its corresponding control command are determined. Seven SCI patients participated in an online experiment, during which the patients were required to control a smart home environment including household electrical appliances, an intelligent wheelchair, as well as a nursing bed via the proposed HMI. The average false operation ratio in the control state was 4.1%, whereas during the idle state, no false operations occurred. All SCI patients were able to control the smart home environment using the proposed EOG-based HMI with satisfactory performance in terms of the false operation ratio in both the control and the idle states. The proposed HMI offers a simple and effective approach for patients with severe SCIs to control a smart home environment. Therefore, it is promising to assist severe SCI patients in their daily lives.

An ICA-based spatial filtering approach to saccadic EOG signal recognition

Abstract To establish a stable electrooculography (EOG)-based communication way for the patients with motor diseases, we proposed a saccadic signal recognition algorithm using independent component analysis (ICA) in this paper. According to the mapping pattern of independent components (ICs)-to-electrode, we designed an optimum ICA-based spatial filter. On this basis, we extracted feature parameters of four types of saccadic signals (i.e., up, down, left, and right) by linearly projecting pre-processed EOG signals to the spatial filter. In order to determine saccade related independent components (SRICs) and improve the recognition accuracy, we also developed an automatic SRICs detection algorithm and sample optimization strategy. Under lab environment, we adopted the support vector model (SVM) as the classifier. The average recognition accuracy of unit saccadic signals achieved 99.0% (before sample optimization) and 99.57% (sample optimized) over 10 participants, which reveals that the proposed algorithm presents an excellent classification performance in saccadic signals recognition.

Comparison of ANN and SVM for classification of eye movements in EOG signals

Nowadays, electrooculogram is regarded as one of the most important biomedical signal in measuring and analyzing eye movement patterns. Thus, it is helpful in designing EOG-based Human Computer Interface (HCI). In this research, electrooculography (EOG) data was obtained from five volunteers. The (EOG) data was then preprocessed before feature extraction methods were employed to further reduce the dimensionality of data. Three feature extraction approaches were put forward, namely statistical parameters, autoregressive (AR) coefficients using Burg method, and power spectral density (PSD) using Yule-Walker method. These features would then become input to both artificial neural network (ANN) and support vector machine (SVM). The performance of the combination of different feature extraction methods and classifiers was presented and analyzed. It was found that statistical parameters + SVM achieved the highest classification accuracy of 69.75%.

Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems

Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although this input method makes it easy for patients to get started because of their familiarity with handwriting, existing eye-writing systems suffer from slow input rates because they require a pause between input characters to simplify the automatic recognition process. In this paper, we propose a continuous eye-writing recognition system that achieves a rapid input rate because it accepts characters eye-written continuously, with no pauses. For recognition purposes, the proposed system first detects eye movements using electrooculography (EOG), and then a hidden Markov model (HMM) is applied to model the EOG signals and recognize the eye-written characters. Additionally, this paper investigates an EOG adaptation that uses a deep neural network (DNN)-based HMM. Experiments with six participants showed an average input speed of 27.9 character/min using Japanese Katakana as the input target characters. A Katakana character-recognition error rate of only 5.0% was achieved using 13.8 minutes of adaptation data.

Real-time estimation of horizontal gaze angle by saccade integration using in-ear electrooculography.

The manuscript proposes and evaluates a real-time algorithm for estimating eye gaze angle based solely on single-channel electrooculography (EOG), which can be obtained directly from the ear canal using conductive ear moulds. In contrast to conventional high-pass filtering, we used an algorithm that calculates absolute eye gaze angle via statistical analysis of detected saccades. The estimated eye positions of the new algorithm were still noisy. However, the performance in terms of Pearson product-moment correlation coefficients was significantly better than the conventional approach in some instances. The results suggest that in-ear EOG signals captured with conductive ear moulds could serve as a basis for light-weight and portable horizontal eye gaze angle estimation suitable for a broad range of applications. For instance, for hearing aids to steer the directivity of microphones in the direction of the user’s eye gaze.

Novel approach for sleep disorder monitoring using a finite‐state machine for localities lacking specialist physicians

This study proposes a novel method for sleep disorder monitoring based on a finite-state machine (FSM) from various bio-signals, namely electroencephalography (EEG), electro-oculography (EOG) and electromyography (EMG) signals. The sleep signals have been obtained from physionet sleep repository, which includes horizontal EOG, submental-EMG and EEG sampled at 100 Hz sampling frequency. Inputs given to the FSM-based module are the processed signals from EMG, EEG and EOG signals. The FSM module for sleep analysis is composed of different states and the conditions to flow from one state to another state. In this study, two FSM modules are designed, one for sleep wave and another for sleep stage identification. Based on the outputs obtained from the above two FSM modules, the sleep disorder can be monitored. The accuracy of the proposed method has been calculated with percentage accuracy, false acceptance rate and false rejection ratio. The average classification accuracy of the finite-state automaton (FSA)-based method is up to 99% for all the tested fault cases. The proposed FSA method suggests a novel method and can be put to effective use in the rural areas for primary analysis.

A EOG-based switch and its application for “start/stop” control of a wheelchair

Abstract Biological signals, including electroencephalography (EEG) and electrooculography (EOG), are often used to develop switches, which represent a class of typical asynchronous human–computer interfaces (HCIs) in which control and idle states need to be distinguished based on a criterion. Determining a satisfactory criterion for rapid and accurate discrimination between control and idle states remains a challenging issue, as EEG signals are highly noisy and nonstationary, and EOG signals are highly affected by unintended/spontaneous eye movements. Therefore, most existing EEG- or EOG-based switches are characterized by disadvantages of long response times (RTs) or high false positive rates (FPRs). The primary contribution of this work is the development of a novel EOG-based switch design, in which a visual trigger mechanism is introduced to guide the users’ blinks and to assist in detecting blinks. Specifically, the graphical user interface (GUI) includes a switch button that flashes once per 1.2 s. The user is instructed to blink synchronously with the flashes of the switch button to issue an on/off command while a single-channel EOG signal is collected. A waveform detection algorithm is applied to the ongoing EOG signal, which discriminates the intended and unintended blinks mainly based on the synchrony between the blink and the switch buttons flash. Once an intended blink, i.e., a blink corresponding to a button’s flash, is detected, the system issues an on/off command. As one application, the proposed EOG-based switch is used to produce start/stop commands for a wheelchair. Several online experiments were conducted with ten healthy subjects. An average accuracy of 99.5%, an RT of 1.3 s for issuing a switch command in the control state, and an average FPR of 0.10/min in the idle state were achieved. The experimental results therefore demonstrate the effectiveness of the single-channel EOG-based switch.

Design and implementation of an eye gesture perception system based on electrooculography

People with motor diseases have suffered from deprivation of both verbal and non-verbal communication abilities. Fortunately, some of them still retain coordination of brain and eye-motor. To establish a stable communication way for these disabled people, this paper presents an eye gesture perception system based on Electrooculography (EOG). In order to implement a high-accuracy of unit saccadic EOG signals recognition, we propose a new feature extraction algorithm based on Common Spatial Pattern (CSP). We first establish a CSP spatial filter bank corresponding to 8 saccadic tasks (i.e., up, down, left, right, right-up, left-up, right-down, and left-down), then use it to linearly project raw EOG signals and treat the outputs as feature parameters. Furthermore, eye gestures recognition has been carried out by identifying and merging unit saccadic segments in terms of pre-defined time sequences. Experiential results over 10 subjects show that the recognition precision of unit saccadic EOG and eye gesture are 96.8% and 95.0% respectively, which reveal the proposed system has a good performance of eye gestures perception.

Testing of features for fatigue detection inEOG.

The article deals with the testing of features for fatigue detection in electrooculography (EOG) records. An optimal methodology for EOG signal acquisition is described; the Biopac data acquisition system was used. EOG signals were being recorded while 10 volunteers were watching prepared scenes. Three scenes were created for this purpose - a rotating ball, a video of driving a car, and a cross. Recorded EOG signals were processed and 20 features were extracted. The features involved blinks, slow eye movement (SEM), rapid eye movement (REM), eye instability, magnitude, and periodicity. These features were statistically tested and discussed in terms of fatigue detection ability. Some of the features were compared with published results. Finally, the best features - fatigue indicators - were selected.

A Single-Channel EOG-Based Speller.

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.

AN ASSISTIVE DEVICE FOR QUADRIPLEGIC PATIENTS USING NI-MyRIO

One of the primary neural disorders is Paralysis, which causes loss of motion in one or more parts of the body muscles. Quadriplegia is a condition, where both the arms and the legs are paralyzed; as a result, the person is entirely dependent on the caretakers. To help them overcome their difficulties, we proposed a system which can control the house appliances, to communicate, monitor and alert the caregivers or the doctors in the case of an emergency by using his/her eye movements. These eye movements can be recorded using Electrooculography (EOG) that can be used to perform these simple tasks. EOG is the measurement of the corneo-retinal standing potential of the eye. This paper describes how the acquisition of EOG signals is made which can be used to control the home appliances. Also, a communication method for the quadriplegic patients to interact with the caregivers and the designing of a monitoring system which can be used for monitoring the quadriplegic patients and an alarm with pop-up message that can be used to alert the caretakers. The proposed work also describes the electrode placements so as to reduce the artifacts, the types of electrodes used, simple circuits and the programming that are used to control the house appliances using EOG signals. The picked up EOG signal from the subject is given to the processing block which suitably provides an output which can be utilized as an input to the NI MyRIO where the programming is done to control the household appliances and for communication.

Drowsiness Detection With Electrooculography Signal Using a System Dynamics Approach

The electrooculography (EOG) signal is considered most suitable for drowsiness detection. Besides its simplicity and low cost, EOG signals are not affected by environmental factors such as light intensity and driver movement. However, existing EOG-based drowsiness detection techniques employ arbitrarily chosen features for classifier training, leading to results that are less robust against changes in the measurement method, noise level, and individual subject variability. In this study, we propose a system dynamics-based approach to drowsiness detection. The EOG signal is treated as a neurophysiological response of the oculomotor system. Each blink action is considered as a result of a series of neuron firing impulses entering the system. Blink signatures are thus extracted to identify the system transfer function, from which system poles are computed to characterize the drowsiness state of the subject. It was found that the location of system poles on the pole–zero map for blink signatures from an alert state was distinctly different from those from a drowsy state. A simple criterion was subsequently developed for drowsiness detection by counting the ratio of real and complex poles of the system over any given period of time. The proposed methodology is a systematic approach and does not require extensive classifier training. It is robust against variations in the subject condition, sensor placement, noise level, and blink rate.

2D/3D-Display Auto-Adjustment Switch System.

Recently, 2-D/3-D switchable displays have become the mainstream in 3-D display technologies, and people can now watch 3-D movies with a naked 2-D/3-D switchable display at home. However, some studies have indicated that people might encounter visual fatigue after enjoying a 3-D film in the theater. Although 2-D/3-D switchable technologies have been widely developed, 3-D display technologies are still lacking in ergonomic and human-care factors such as reducing visual fatigue. This study proposes a novel 2-D/3-D display autoadjustment switch system to provide biofeedback functions to reduce users' visual fatigue. In addition, the relationship between the blink rate and the visual fatigue state while watching 3-D films was investigated and quantified. In this study, liquid crystal barrier technology was used to develop a 2-D/3-D switchable display, and a wearable EOG acquisition device was also designed to monitor electro-oculography signals to estimate the blink rate. Here, the 2-D/3-D display autoadjustment criterion of the proposed system was designed according to the change in the visual fatigue state as estimated from the blink rate. Finally, the experimental results show that the proposed system could effectively reduce users' visual fatigue while watching 3-D films.

Electrooculography: Analysis on device control by signal processing

This paper covers a new technology by placing electrodes on forehead of person around the eyes for recording the eye movements, this technique is called Electrooculography (EOG). It is basically focused on the concept of recording down the polarization potential which is also called corneal-retinal potential (CRP), this is resting potential between retina and cornea. The potential is called electrooculogram. This is a tiny electrical potential that can be found out using electrodes that are comparative to eye displacement. Electrooculography is a means of control for allowing the handicapped, mainly people with eye-motor coordination, which allow them live independent lives. This is a very good assistive system for disabled people with less money. This total command control EOG lets users to guide it with a high intensity of comfort ability. Systems which are becoming more popular day by day are HCI (Human Computer Interaction) that are Eye tracking based. To such systems Electrooculogram (EOG) functions as basic source of input. This integral process of analysis of electrooculogram is called Electrooculography. The paper we are presenting gives basic idea on Electrooculography and its use and applications in different HCI systems. In this work we have also included detection of eye blinks from EOG signal. Conclusively it throws light on various issues concerned with Electrooculography. Keywords: Electrooculography (EOG), eye tracking, eye blinks, Human Computer Interaction (HCI).

Brain–Computer Interface–Based Communication in the Completely Locked-In State

Despite partial success, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a state called complete locked-in state (CLIS). Based on a motor learning theoretical context and on the failure of neuroelectric brain–computer interface (BCI) communication attempts in CLIS, we here report BCI communication using functional near-infrared spectroscopy (fNIRS) and an implicit attentional processing procedure. Four patients suffering from advanced amyotrophic lateral sclerosis (ALS)—two of them in permanent CLIS and two entering the CLIS without reliable means of communication—learned to answer personal questions with known answers and open questions all requiring a “yes” or “no” thought using frontocentral oxygenation changes measured with fNIRS. Three patients completed more than 46 sessions spread over several weeks, and one patient (patient W) completed 20 sessions. Online fNIRS classification of personal questions with known answers and open questions using linear support vector machine (SVM) resulted in an above-chance-level correct response rate over 70%. Electroencephalographic oscillations and electrooculographic signals did not exceed the chance-level threshold for correct communication despite occasional differences between the physiological signals representing a “yes” or “no” response. However, electroencephalogram (EEG) changes in the theta-frequency band correlated with inferior communication performance, probably because of decreased vigilance and attention. If replicated with ALS patients in CLIS, these positive results could indicate the first step towards abolition of complete locked-in states, at least for ALS.