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We have successfully designed and built a human computer interface system to interface with the computer by human eye EOG signal (Electrooculography). The EOG signal has very low voltage and very sensitive to interference. This paper introduces the software we developed for processing the EOG signal which lead to a reliable human computer interface...
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... Measurement, Design, Experimentation, Human Factors Human-Computer Interaction, EOG (Electrooculography), eye movement tracking There is no magic now to control a machine by pushing a button by a finger. Human-machine interaction has never been so easy. Are there even easier way to control a machine without even pushing a button? Our answer is yes. For example, one can control a machine by only looking at it. As the advance of computer technology, to control any machinery that is equipped with a computer has become a common fact. The lacking element of controlling a machine by eyes becomes how to control a computer by eyes. Many studies have been done towards this goal such as [5][6][8][9][10], among others. Towards this goal, we have studied the characteristics of the EOG signal and its possibility to be an accurate human machine interface. We successfully built an experimental system that can study the bio-physiological signal to control the computer only by eye movements [1]. The system recorded the EOG signal[2][3][4] from user eye movement and positions the cursor on the monitor to control the computer as a normal cursor does. See Figure 1 of the system modules. For the novelty of the system, please refer to [1] for more details. In this paper, we will introduce a key element in the system which is the software subsystem for the experiments on the EOG signal. It serves for the processing and analysis of the signal for cursor position control. This is the base for the software module in Figure 1. The development has two stages. The first stage is the development of the software subsystem for the experiments. The second stage is a modification on this experimental subsystem to form the software module in the global system. Important technical issues in this subsystem will be presented with testing results. The software subsystem will be presented first. The subsequent sections will describe its elements one by one. The objective of building the software subsystem in the experimental user interface is to use an inverse engineering approach to generate ideal cursor movement patterns for the user eyes to follow. The EOG signal from the user eye movement following these patterns are recorded and compared with ideal signals. These helped us to identify all the important issues in building the final software module to control the cursor by eyes. The basic functions of the software subsystem are designed according to the need of the experiments. The basic functions are: configuration of the system parameters, recording the detected EOG signal, comparison of the recorded signal and the perfect produced signals, and to process the signal. The components of the software subsystem for the experimental system can be seen in the Figure 2. The configuration module is for configure all the equipments in the system such as the digitalization card for signal acquisition and the signal visualization, the filtering parameters, the creation of file folders for the recorded EOG signals. This module is in charge of generating the experimental stimuli signals and recording the EOG signal from the user eyes. It will record both generated standard signals and the signals from the user eyes. The basic algorithm of this module can be seen in the following: begin Exit: = false; Read (Exit); while Exit = false do begin Read parameters of the experiment; Configure acquisition parameters; Open log file for record; Generate point matrix of cursor movement; Move cursor to start position; Initialize audio signal; Wait time of initiation; While there are cursor movement points do begin Move cursor to next position; Wait for the next multiple of the ISI; Get time; Read EOG signal; Save time, cursor position and EOG in matrix; End; Compute statistics; Save record file; Close record file; End; Read(Exit); End; The first part of the algorithm is for setting all the experimental configuration parameters such as: generated movement pattern, time between each cursor movement ( ISI : InterStimuli Interval ), iteration time, lateral margin, number of generated cursor movement points. The second step is to configure the digitalization card. The matrix of cursor movement points is generated according to the test pattern, number of points selected along x direction. Along with the ISI and the monitor size the cursor movement velocity can be decided. The experiment begins with the cursor in the initial position. An audio signal will be generated as well to inform the user that the generation of test pattern will begin. The user can then follow the test pattern by eye movement. We calculate the statistics during the EOG signal recording. We calculate the arithmetic mean, standard deviation and the maximum and minimum values of ISI. These values provide the information needed to validate the recording. The algorithm ends with the saving and closing of the record file. This module is for comparing the recorded signal with the respective generated movement pattern to establish the scaling and offset factors to adjust the signal on both dimensions. We apply a parameterizable moving average smoothing filter to the signal to better visualizing it. It is also possible to measure the differences between the levels of different points to determine the drift value in a specific time interval. In Figure 3, you can see the result of comparing the x component of a generated cursor movement pattern with the EOG signal. The EOG signal has been applied a scaling factor of -2.135, and a displacement of -0.297, and a median filter over a window of 21 points. One of the tasks, in addition to comparing the EOG signals with the generated one, is to have a simple method for the detection of voluntary and involuntary eye blinks in the EOG signals. The filtering module is for adjusting the frequency of a high-pass filter to isolate the blinking. It is also used for adjusting the low-pass filters frequencies. The module starts by selecting a record file and the filter type. Then it selects the cutoff frequency and calculates the result of applying the filter on the EOG signal. As shown in Figure 4, the right part is the original EOG signal and the left part is the result of applying the high-pass filter with cutoff frequency at 8 Hz. In the first and third channel on the left one can see the pulse signal corresponding to the eye blinking. The purpose of this module is to approximate the user's eye position from the EOG signals of one or more record files using the cursor movement patterns. It is assumed that the user pursuits the foveal cursor movement during recording without moving the head. Before processing the signal files, it is possible to apply a moving average filter to smooth the EOG signals. The module also allows the calculation on a selected range of the signal. The filter generates a new sequence of points from each channel of EOG input by calculating the arithmetic mean over a window of length specified by the parameter range. If x i is the value of the filtered signal at time moment i , m the number of samples of the signal and r is the value range, the effect of filtering for each sample can be expressed ...
Citations
... For HCI applications, this EOG signal can be used to transfer information between humans and machines. Many strategies have already been introduced to implement the HCI system based on EOG signal (Estrany et al. 2009). ...
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.
... To avoid the discomfort of placing sensors directly on the skin, some approaches use goggles or face shield based artifacts [21][22][23]46] or a head-band [48]. ...
... Outside this range, a smaller EOG voltage/angle increase is found. A similar result is obtained in [21][22][23]. However, in [36] the authors claim that there is a nonlinear relationship between the EOG and the eye angle, and this non-linearity is used to establish an automatic drift calibration technique. ...
The basic principles and techniques used in Electrooculography (EOG) are presented. The main objective of this work is to present a state of art of Electrooculography (EOG) in Human computer Interface (HCI) to help researchers interested in the field.
... Developed a signal processing algorithm with two inputs to detect eye movements. Another article was written by Estrany, B et al. (2009) [3]. designed and built a human-computer interface system using EOG signal. ...
In the electrical transmission system, maneuvers or intervention should be done quickly in case of a possible malfunction. In order for the intervention to be carried out correctly, the switchyards must be properly illuminated. Insufficient lighting increases the risk of possible work accidents during night maneuvers or repair work. For this reason, it is necessary to determine the lighting to be used in energy transmission facilities. This ensures that malfunctions or maneuvers that may occur in the interconnected system are managed in a healthy way. Considering the electric field exposed in high voltage switchyards, the lighting technology must be resistant to this. In this study, first of all, the structures of the switchyards will be examined. It will be mentioned which lightings are used in the switchyards in the transmission system. The electric field and corona discharge values that these lighting systems are exposed to when under high voltage will be examined. In the last part of the research, the status of lighting systems used in switchyards in energy management will be evaluated. Information will be given about which lighting types would be appropriate to use against the electric field and corona discharges to which the lightings are exposed.
... The difference is thought to be due to the high metabolic rate in the retina. EOG is the process of recording the potential difference between the front and back of the eye, this potential difference is obtained with special electrodes that are placed appropriately around the eye [15]. Figure 4 shows EOG's electrical response as a function of time when looking left and right. ...
... These potential can be used to make predictions about health, fitness and can be used to treat physiological, psychological diseases and disorders.Recent advancements in computing and signal processing techniques have made it possible to develop a mind-machine interface (MMI). The use of EEG (electroencephalogram), EMG (electro-myogram), EOG (electro-oculogram), ECG (electro-cardiogram) or brain waves signals generated in human body are used to communicate with a computer [1] [2]. These electrical signals are generated as a result of a thought to prompt a change or to prompt a muscle to move in a human body. ...
This paper presents advancement towards making an efficient and viable wheel chair control system based on brain computer interface via electro-oculogram (EOG) signals. The system utilizes the movement of eye as the element of purpose for controlling the movement of the wheel chair. Skin-surface electrodes are placed over skin for the purpose of acquiring the electro-oculogram signal and with the help of differential amplifier the bio-potential is measured between the reference and the point of interest, afterwards these obtained low voltage pulses are amplified, then passed through a sallen-key filter for noise removal and smoothening. These pulses are then collected on to the micro-controller; based on these pulses motor is switched to move in either right or left direction. A prototype system was developed and tested. The system showed promising results. The test conducted showed 99.5% efficiency of movement in correct direction.
... The differential potential in humans lie between 0.05-3.5mV of amplitude and a frequency range of 0.1-20Hz [4]. Eye movement will respectively generate voltage up to 16μV and 14μV per 1° in horizontal and vertical way [5]. The electrooculogram (EOG) is an ectroencephalographic record of the voltage changes obtained while the subject, without moving the head, moves the eyes from one fixation point to another within the visual field. ...
HCI system with the help of bio-signals such as
EEG, EOG, EMG, etc. stand out to be the peaking subject of
research in the recent years. This paper presents the design
system for the cursor movement through eyes using
Electrooculogram (EOG) signal for a physically disabled people
to assist them in operating the basic home appliances. In this
work, a Home Automation System using Arduino UNO has been
developed, so that it can function by the movement and click of
the cursor when positioned to the particular location of the
switches. Eye movement have been detected by extracting and
recording the EOG signal using a BIOPAC System. The
interfacing of this system design has been developed in the
platform of MATLAB Software. Operation of home appliances
such as switching on a light or a fan by the cursor movement and
click through eyes on screen for physically impaired people is
the outcome of this research.
... io signal like EOG has attracted the attention of many researchers recently because it has potential to help disabled people in controlling wheel chair and in using their computer (Estrany, Fuster, Garcia, & Luo , 2009). In order to measure Bio signals generated by eye movements, Electrooculography has been used to measure the bio-potential generated by the changes in the positioning of the eye. ...
The aim of this research is to develop a signal processing method to detect four eye movements, such as looking up, down, left, right and blinking. This new method has a couple of features in comparison with the recent eye movement detection algorithms. Most of the recent algorithms require a De-noising stage, which is not required in this work. In addition, the suggested algorithm can be considered simple and robust in noisy environment in contrast with other algorithms. In this paper, short-time averaging method is proposed to process and to extract parameters from EOG signals. Moreover, adaptive threshold is applied to classify EOG pulses. The purpose of the adaptive threshold is to enhance the performance of the algorithm in a noisy background. Simulation results are based on real-life EOG signals, where these signals were recorded using an Electrooculography system. The results show that the proposed algorithm has a stable performance HR=100 % and FR=0% with SNR greater than 2dB. The average performance with SNR=0.5 dB is about HR= 90.21% and FR= 4.88%.
... The detection of eye blinking and yawn in these researches majorly employs Bio-electrical measurement and computer vision technology. Bio-electrical measurement relies on measuring change of Electrooculogram (EOG) or Electromyography (EMG) caused by motions of eye blinking and yawn by bio-electrodes placed on surrounds of related tissues [6,7]. However, in this approach, metallic electrodes commonly bring discomforts for human body, and the measurements might easily be disturbed by muscular movement or electrostatic noise. ...
... Thus, the system was able to detect movement angles but could not improve the 2 Computational Intelligence and Neuroscience accuracy of the decoded EOG trajectory. However, a linear model was designed and built using eye saccade angles and EOG based on linear fitting by He et al. [15], while Estrany et al. established a similar model using multiple linear regression [16,17]. In the research of Estrany et al., the adjusting of proportion and excursion parameters, multichannel EOG could be transformed into cursor positions [18]. ...
The aim of this study is to build a linear decoding model that reveals the relationship between the movement information and the EOG (electrooculogram) data to online control a cursor continuously with blinks and eye pursuit movements. First of all, a blink detection method is proposed to reject a voluntary single eye blink or double-blink information from EOG. Then, a linear decoding model of time series is developed to predict the position of gaze, and the model parameters are calibrated by the RLS (Recursive Least Square) algorithm; besides, the assessment of decoding accuracy is assessed through cross-validation procedure. Additionally, the subsection processing, increment control, and online calibration are presented to realize the online control. Finally, the technology is applied to the volitional and online control of a cursor to hit the multiple predefined targets. Experimental results show that the blink detection algorithm performs well with the voluntary blink detection rate over 95% . Through combining the merits of blinks and smooth pursuit movements, the movement information of eyes can be decoded in good conformity with the average Pearson correlation coefficient which is up to 0.9592 , and all signal-to-noise ratios are greater than 0 . The novel system allows people to successfully and economically control a cursor online with a hit rate of 98% .
... Classifying activities are based on pattern detection, as in reading which involves small eye movements from the beginning of a text line and a big shift at the end [7]. Event activities are mainly based on go-and-back movement (GBM) from eyeball center to an extreme: for example, a virtual keyboard [8], a mouse pointer [9], or a wheelchair [10]. ...
... Bell-shaped waveforms, such as blinks and overshoots, are unwanted elements in some control interfaces or activity classifiers [7][8][9][10]18], because they reduce their effectiveness. The offline and online versions of the technique proposed in this paper achieved high and stable levels of elimination with low response delays (EFS-WN, mean filter, finding local minimums and interpolation are linear operations), with their amplitudes decreasing by more than 97%, which was far better than the 300 ms-length MF. ...
... Saccade movements cause a rapid signal variation, whereas fixations maintain the electric level from saccades [1,17]. They can both be modeled with a sigmoid function (9). This is defined for all real numbers, its rank is (0, 1), with two horizontal asymptotes in 0 and 1 (Figure 16). ...
Background
Eye movements have been used in control interfaces and as indicators of somnolence, workload and concentration. Different techniques can be used to detect them: we focus on the electrooculogram (EOG) in which two kinds of interference occur: blinks and overshoots. While they both draw bell-shaped waveforms, blinks are caused by the eyelid, whereas overshoots occur due to target localization error and are placed on saccade. They need to be extracted from the EOG to increase processing effectiveness.
Methods
This paper describes off- and online processing implementations based on lower envelope for removing bell-shaped noise; they are compared with a 300-ms-median filter. Techniques were analyzed using two kinds of EOG data: those modeled from our own design, and real signals. Using a model signal allowed to compare filtered outputs with ideal data, so that it was possible to quantify processing precision to remove noise caused by blinks, overshoots, and general interferences. We analyzed the ability to delete blinks and overshoots, and waveform preservation.
Results
Our technique had a high capacity for reducing interference amplitudes (>97%), even exceeding median filter (MF) results. However, the MF obtained better waveform preservation, with a smaller dependence on fixation width.
Conclusions
The proposed technique is better at deleting blinks and overshoots than the MF in model and real EOG signals.
