EOG-based Human Computer Interface Research Articles

Development of an Electrooculogram (EOG) and Surface Electromyogram (sEMG)-Based Human Computer Interface (HCI) Using a Bone Conduction Headphone Integrated Bio-Signal Acquisition System

Human–computer interface (HCI) methods based on the electrooculogram (EOG) signals generated from eye movement have been continuously studied because they can transmit the commands to a computer or machine without using both arms. However, usability and appearance are the big obstacles to practical applications since conventional EOG-based HCI methods require skin electrodes outside the eye near the lateral and medial canthus. To solve these problems, in this paper, we report development of an HCI method that can simultaneously acquire EOG and surface-electromyogram (sEMG) signals through electrodes integrated into bone conduction headphones and transmit the commands through the horizontal eye movements and various biting movements. The developed system can classify the position of the eyes by dividing the 80-degree range (from −40 degrees to the left to +40 degrees to the right) into 20-degree sections and can also recognize the three biting movements based on the bio-signals obtained from the three electrodes, so a total of 11 commands can be delivered to a computer or machine. The experimental results showed the interface has accuracy of 92.04% and 96.10% for EOG signal-based commands and sEMG signal-based commands, respectively. As for the results of virtual keyboard interface application, the accuracy was 97.19%, the precision was 90.51%, and the typing speed was 5.75–18.97 letters/min. The proposed interface system can be applied to various HCI and HMI fields as well as virtual keyboard applications.

An Electrooculogram-Based Interaction Method and Its Music-on-Demand Application in a Virtual Reality Environment

Recently, the devices commonly used for interaction with a virtual reality (VR) environment include game controllers, data gloves, and motion tracking systems. However, these devices may limit the immersive experience or cause inconvenience, especially for patients with limb paralysis. This paper proposes a new nonmanual human-computer interface (HCI) based on a single-channel electrooculogram (EOG) signal and enables real-time interactions with the VR environment. The graphical user interface of the EOG-based HCI in VR includes several buttons flashing in a random order. The user needs to blink in synchrony with the corresponding button's flashes to issue a command, while the algorithm detects the eye blinks from the EOG signal and determines the users' target button. Furthermore, with the EOG-based HCI, we developed a music-on-demand system in the VR environment. By blinking, users can search and select target songs in the music library, play the selected songs, switch to the next song, and delete songs from the list. Ten healthy subjects and five patients with spinal cord injuries (SCIs) participated in online experiments. The experimental results demonstrated the effectiveness of the EOG-based HCI as a new method for interacting with the VR environment. Furthermore, the music-on-demand system can be used for entertainment not only by healthy people but also by SCI patients with limb paralysis.

Development of EOG-Based Human Computer Interface (HCI) System Using Piecewise Linear Approximation (PLA) and Support Vector Regression (SVR)

Electrooculogram (EOG)-based human-computer interfaces (HCIs) are widely researched and considered to be a good HCI option for disabled people. However, conventional systems can only detect eye direction or blinking action. In this paper, we developed a bio-signal-based HCI that can quantitatively estimate the horizontal position of eyeball. A designed bio-signal acquisition system can measure EOG and temporalis electromyogram (EMG) signals simultaneously without additional electrodes. For real-time processing for practical application, modified sliding window algorithms are designed and applied for piecewise linear approximation (PLA). To find the eyeball position, support vector regression (SVR) is applied as a curve-fitting model. The average tracking error for target circle with a diameter of 3 cm showed only 1.4 cm difference on the screen with a width of 51 cm. A developed HCI system can perform operations similar to dragging and dropping used in a mouse interface in less than 5 s with only eyeball movement and bite action. Compare to conventional EOG-based HCI that detects the position of the eyeball only in 0 and 1 levels, a developed system can continuously track the eyeball position in less than 0.2 s. In addition, compared to conventional EOG-based HCI, the reduced number of electrodes can enhance the interface usability.

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%.

Offline Analysis for Designing Electrooculogram Based Human Computer Interface Control for Paralyzed Patients

Currently, majority of persons were immobilized and need aid from caretakers due to disability. To reduce and overcome such problem, there was a need for developing human–computer interface (HCI) with the help of biosignals. In this paper, we propose a two-channel elecctrooculograpy (EOG)-based HCI to encourage the contact ability as well as value of life for paralyzed persons who cannot speak or shift their extremity by using 20 subjects with the help of ADT26 Bio amplifier. EOG signals were collected for 11 tasks from both vertical and horizontal eye movement by using gold-platted electrodes. The extracted EOG signals were processed with convolution and Plancherel theorem to obtain the features. Layered recurrent neural network (LRNN) was implemented to analyze the extracted features and then converted into a sequence of commands to control the HCI. A graphical user interface was developed using MATLAB to help a user to convey their thoughts. This paper shows an average classification accuracy of 90.72% for convolution features and 91.28% for Plancherel features. Off-line single trail analysis was also performed to analyze the recognition accuracy of the proposed HCI system. The off-line analysis displayed that Plancherel features using LRNN were high compared to convolution features using LRNN. From this paper, we found that LRNN architecture using Plancherel features was more suitable for developing EOG-based HCI. Single trail analysis was conducted to identify the recognizing accuracy in offline. The off-line results indicated that in comparison with other EOG-based HCI systems, our system was user friendly and needs minimum training to operate.

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.

Controlling a Human–Computer Interface System With a Novel Classification Method that Uses Electrooculography Signals

Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.

Development of robust electrooculography (EOG)-based human-computer interface controlled by eight-directional eye movements

Electrooculography (EOG) signal is one of the useful electro-physiological signals. The EOG signals provide information about eye movements that can be used as a control signal in human-computer interface (HCI). Usually, eight-directional movements, including up, down, right, left, up-right, up-left, down-right and down-left, are proposed. Development of the EOG signal classification has been shown more increasing interest in the last decade; however, the effect of noises on classification system is a major problem to degrade the usefulness of EOG-based HCI. A robust classification algorithm of the eight movements is proposed, in which this technique can conduct the effect of noises in EOG signal, particularly for involuntary movements and eye-blink artifacts. The proposed algorithm was based on the onset analysis, feature extraction, the first derivative technique and threshold classification. Eight beneficial time domain features were proposed including the peak and the valley amplitude positions, and the upper and the lower wavelengths of two EOG channels, vertical and horizontal channels. Based on the optimal threshold values and conditions, the results showed that classification accuracy reached 100% for three-subject testing. In addition, the first derivative technique was additionally implemented in order to avoid the eye-blink artifact and other eight time domain features, that is, peak amplitude and area under curve, have been investigated for use in advanced HCI interfaces, notably, eye activity and eye writing recognitions. Key words: Electrooculography signal, eye motions, eye blink artifacts, feature extraction, interference, noises, non-pattern recognition, robustness, threshold analysis.

EOG-based visual navigation interface development

This paper presents an EOG-based (Electrooculography) interface for Human Computer Interface (HCI) purposes. The solution enables the filtering of the recorded signals and identification of characteristic peak amplitudes associated with eye saccades, blinks or winks by using a classifier based on a set of fuzzy logic rules and a deterministic finite automaton. The identified eye saccades were assigned to six low-level commands for navigation purposes. An experiment study was conducted in order to check the accuracy and the performances of the proposed interface compared with three traditional input control interfaces. Experimental results show that the developed interface has good performance and can be used for online communication and control in EOG-based HCI systems or even for first-person navigation metaphors in games industry.

Design of a Novel Efficient Human–Computer Interface: An Electrooculagram Based Virtual Keyboard

The aim of this paper is to present the design and application of an electrooculogram (EOG) based on an efficient human-computer interface (HCI). Establishing an alternative channel without speaking and hand movements is important in increasing the quality of life for the handicapped. EOG-based systems are more efficient than electroencephalogram (EEG)-based systems in some cases. By using a realized virtual keyboard, it is possible to notify in writing the needs of the patient in a relatively short time. Considering the biopotential measurement pitfalls, the novel EOG-based HCI system allows people to successfully communicate with their environment by using only eye movements. Classifying horizontal and vertical EOG channel signals in an efficient interface is realized in this study. The new system is microcontroller based, with a common-mode rejection ratio of 88 dB, an electronic noise of 0.6 μV (p-p), and a sampling rate of 176 Hz. The nearest neighborhood algorithm is used to classify the signals, and the classification performance is 95%. The novel EOG-based HCI system allows people to successfully and economically communicate with their environment by using only eye movements.

A novel eye movement detection algorithm for EOG driven human computer interface

Bio-based human computer interface (HCI) has attracted more and more attention of researchers all over the world in recent years. The paper is concerned with eye movement detection algorithm and the EOG-based human computer interface. The linear predictive coding cepstrum (LPCC) coefficients of EOG pulses are extracted as feature vectors, which are used for eye movement pattern matching. Dynamic time warping (DTW) is adopted to solve the discrepancy in EOG pulses duration among different trials. Furthermore, spectral entropy algorithm is used to detect the endpoints of EOG pulses, which can improve the robustness and increase the recognition rate in noisy background. Experimental and simulation results based on real-life EOG signals show that the proposed algorithm has stable performance and can be used for online controlling and communication in EOG based HCI system.

EOG-based Human–Computer Interface system development

Several Human–Machine/Computer Interfaces (HMI/HCI) had been developed in recent years. Some designs were specifically made for people with disabilities such as injured-vertebra, apoplexy or poliomyelitis, Amyotrophic Lateral Sclerosis (ALS), and Motor Neuron Disease, (MND). In this paper, we proposed an eye-movement tracking system. Based on Electro-Oculography (EOG) technology we detected the signal with different directions in eye-movements and then analyzed to understand what they represented about (e.g. horizontal direction or vertical direction). We converted the analog signal to digital signal and then used as the control signals for Human–Computer Interface (HCI). In order to make the system “robust”, several applications with EOG-based HCI had been designed. Our preliminary results revealed more than 90% accuracy rate for examining the eye-movement that may become a new useful human–machine user interface in the near future.