A Segmental Approach with SWT Technique for Denoising the EOG Signal

The aim of this study was to present electrooculogram (EOG) signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from amyotrophic lateral sclerosis (ALS) or other diseases that prevent correct limb and facial muscular responses. Using EOG signals, it is possible to improve the communication abilities of those patients who can move their eyes. Investigating the possible usage of the EOG for human---computer interface, a relation between sight angle and EOG is determined. In other methodology, most famous approaches involve the use of a camera to visually track the eye. However, this method has problems that the eyes of user must always be open. In this paper, we propose the mouse cursor control system for ALS patients using EOG and electroencephalograph (EEG) signals. We introduced the algorithm using alternating current and direct current of EOG corresponding to the drift. Therefore, EOG measurement system we proposed improved the problems of artifacts caused by eye blinking which was not accepted for other systems, the displacement of electrode positions and the drift. In addition, we introduced the EEG measurement to examine whether the subject could control their eye movement consciously. The EEG signals were not used to control the mouse movement, but to determine the subject's control state. In order to test whether our system works well, we prepared a questionnaire and asked the subjects to operate our system, and answer with YES or NO by moving the mouse cursor. During the task, we also recorded the subjects' EEG by MYNDPLAY [7] and checked their conscious level. Three subjects participated in this experiment, and they had never operated this system before. In this experiment, we measured 30 states of EEG signals while EOG was also measuring for one eye movement and analyzed the EEG signals. The results of analysis of the EEG signal changes and the answers to questions indicated that at 26 of 30 states, the subjects' conscious level while they were moving the cursor by EOG signals was correctly determined from the EEG signals. From these results, we could know that the EEG signals can be used to adjust the EOG system whether it works according to patients' mind or just a misjudgment.

In this paper, we present a comparison Support Vector Machine (SVM) and Artificial Neural Network (ANN) for classification of electrooculogram (EOG) signals acquired under specific eye movements. These methods that are required for an eye controlled system are compared by means of their accuracy and response time. Acquired EOG signals consist of 5 different eye movements - being horizontal (right and left), vertical (up and down) and blink. EOG signal acquisition was achieved from 20 different subjects by using two EOG channels (vertical and horizontal) and 3 element feature vectors were extracted. The first two elements of the feature vectors are the peak amplitudes of two channels whereas the third element, being our proposed parameter, is the kurtosis value of the active channel. 10 of 20 randomly selected feature vectors were used for training of the classifiers whereas the rest was used for performance tests. Offline tests yield 100 % success rate for both of the classifiers. The response times of both methods make them suitable for real-time usage.

Human-machine interfaces (HMIs) based on Electro-oculogram (EOG) signals have been widely explored. However, due to the individual variability, it is still challenging for an EOG-based eye movement recognition model to achieve favorable results among cross-subjects. The classical transfer learning methods such as CORrelation Alignment (CORAL), Transfer Component Analysis (TCA), and Joint Distribution Adaptation (JDA) are mainly based on feature transformation and distribution alignment, which do not consider similarities/dissimilarities between target subject and source subjects. In this paper, the Kullback-Leibler (KL) divergence of the log-Power Spectral Density (log-PSD) features of horizontal EOG (HEOG) between the target subject and each source subject is calculated for adaptively selecting partial subjects that suppose to have similar distribution with target subject for further training. It not only consider the similarity but also reduce computational consumption. The results show that the proposed approach is superior to the baseline and classical transfer learning methods, and significantly improves the performance of target subjects who have poor performance with the primary classifiers. The best improvement of Support Vector Machines (SVM) classifier has improved by 13.1% for subject 31 compared with baseline result. The preliminary results of this study demonstrate the effectiveness of the proposed transfer framework and provide a promising tool for implementing cross-subject eye movement recognition models in real-life scenarios.

To observe the clinical efficacy of transcutaneous electrical acupoint stimulation (TEAS) at Changqiang (GV 1) based on the modulation of electro-oculogram (EOG) signal for children with mental retardation, and explore the evaluation effect of the goal attainment scale (GAS) in children with mental retardation. Sixty children with mental retardation were randomly divided into a treatment group and a control group, with 30 cases in each one. The children in the control group were treated with conventional rehabilitation, 5 times a week. On the basis of the control group, TEAS at Changqiang (GV 1) under the modulation of EOG signal was adopted in the treatment group. When the similarity between the collected EOG signal and the template was within the range of EOG threshold, one electric stimulation was triggered at Changqiang (GV 1) for 20 s (continuous wave, 70-100 Hz in frequency, 0.1-0.2 ms in pulse width), lasting 30 min in each treatment, the intervention was given twice a week. One course of treatment was composed of 4 weeks, and 3 courses were required in total in the two groups. The infant-junior high school student's social living ability scale (S-M) and GAS were scored and compared before and after treatment in the two groups. After treatment, the scores of self-living ability in the treatment group and communication ability in the control group were higher than those before treatment (P<0.01, P<0.05). The scores of collective activity and motor ability in the treatment group were higher than those in the control group (P<0.05). After treatment, GAS scores were higher than before treatment in both groups (P<0.001), and the score in the treatment group was higher than the control group (P<0.05). TEAS under the modulation of EOG signal is conductive to improving the collective, motor and self-living abilities of the children with mental retardation and promoting children's individual goals. Compared with the standard score of S-M, the T value of GAS can better reflect the subtle progress of individual.

A meal assistance orthosis is developed for elderly and/or disable persons who have partly or completely lost the ability of moving their upper limbs by themselves independently. The meal assistance orthosis supports the disabled persons and brings foods from dishes to their mouth by their own forearms. Human intention, i.e., which foods they want to eat, can be extracted from electrooculogram (EOG) signals which are measured by electrodes on the face. Position control is adopted for control strategy of the meal assistance orthosis, and forcefree control which can realize flexible motion of the meal assistance orthosis is adopted in order to avoid the meal assistance orthosis injure the subject. The effectiveness of the developed meal assistance orthosis is assured by experimental works on normal persons

Assistive robots play an increasingly important role in the lives of people with disabilities. More and more research efforts are being focused on using Electrooculogram (EOG) signals in driving assistive robots. This paper presents a new shared control architecture based on EOG signal and global vision for assistive robots. This paper firstly introduces the shared control concept with detailed block diagrams and then presents the work flow process of EOG signal acquisition and angle recognition. A state machine design is also presented that makes the control command transfer sequentially. Finally, control strategies of intent conflict and intent consistency, respectively, are also presented for different intent expression scenarios. Experiment results demonstrate that the shared control architecture proposed in this paper is effective that can reduce the user's operational load significantly.

Electrooculogram (EOG) is an important bio-electricity signal generated by physical activities of eyeballs. Information about gaze position can be obtained by analyzing EOG signals, which can lay a foundation for a novel way of human-computer interaction (HCI) based on EOG signals. In this paper, we propose a gaze estimation method based on EOG signals. After filtering and amplifying the acquired EOG signals, framing and normalization is done to make the signals easier to classify. The wavelet coefficients are extracted as the feature vectors, and BP classifier is trained to classify EOG signals of different directions. The results show that this method works well with different people and the average accuracy achieves 96%.

This paper investigates a wavelet based denoising of the electroencephalogram (EEG) signal to correct for the presence of the ocular artifact (OA). The. proposed technique is based on an over-complete wavelet expansion of the EEG as follows: i) a stationary wavelet transform (SWT) is applied to the corrupted EEG; ii) the thresholding of the coefficients in the lower frequency bands is performed; iii) the de-noised signal is reconstructed. This paper demonstrates the potential of the proposed technique for successful OA correction. The advantage over conventional methods is that there is no need for the recording of the electrooculogram (EOG) signal itself. The approach works both for eye blinks and eye movements. Hence, there is no need to discriminate between different artifacts. To allow for a proper comparison, the contaminated EEG signals as well as the corrected signals are presented together with their corresponding power spectra.

In this study, Electrooculogram (EOG) based Human-Machine Interface (HMI) application is proposed for partial or completely paralyzed and physically limited people as Amyotrophic Lateral Sclerosis (ALS) patients. In the designed system, EOG signals consisted of vertical and horizontal eye movements are detected by using 6 Ag-AgCl electrodes which placed around the eye. Then, by applying amplifying and filtering processes to the detected signals, EOG signals at 0-+5V amplitude levels are obtained at the output of analog stage. In consequence of digital processing of analog EOG data with microcontroller unit, control signals for HMI applications are acquired. At signal processing stage, after preprocessing step for eye movements (vertical, horizontal and blink) maximum and minimum voltage amplitude values are detected. These values are directly determine the performance of the classification process. K-Nearest Neighbor (k-NN) classifier and Support Vector Machines (SVM) are used for classification. According to the results, k-NN and SVM perform the classification task with %90.3 and %92.6 accuracy, respectively. Simulation results show that with the proposed EOG based HMI system, physically limited patients can communicate with their environments in a successful manner.

The Cornea-Retinal Potential (CRP) is the main source of the Electrooculogram (EOG) signals. These potential are in small microvolt and required to be amplified upto the applicable and usable level. The place of electrode attachment, the eyeball rotation, the motion of head, the ambient light as well as the conductivity of skin can also vary this signal. These numerous artifacts with power-line interference and other unwanted noise had made the EOG signal quite unattractive for any real biomedical applications. This work thus mainly focuses on the elimination of the above major artifacts while striving to maintain signal linearity. The hardware design part also considers the cost effectiveness without negotiating accuracy. The circuit is then rigorously tested and checked over the varied sample of people. It is found to give an efficient and significantly consistent result with minimum noise level. The development and design of a low cost EOG signal amplifier and acquisition system that counters all the major problems is investigated and achieved making it suitable for both theoretical research analysis as well as for real practical applications.

Objectives:&nbsp; To develop an image enhancement technique that includes both resolution and contrast enhancement. Resolution is the ability of an image to show its details. &nbsp;In this method, Stationary Wavelet Transform (SWT) is used in combination with Lifting Wavelet Transform (LWT) for resolution enhancement and SWT with the combination of Contrast limited adaptive Histogram Equalization (CLAHE) for contrast enhancement. SWT is used in combination with LWT improves the resolution and also minimizes the execution time drastically. SWT is combined with CLAHE to enhance the Contrast and mitigate the noise effects than existing methods. The proposed method gives superior results, and it is proved with Peak Signal to Noise Ratio (PSNR), Noise Estimation and Root Mean Square Error (RMSE), and pictorial results.

One of the most common factors of road accidents is the drowsiness of drivers. The project's key purpose is to define the strategies for detecting and alerting driver somnolence, thus growing transportation safety. Via uses several physiological variables, such as pulse rate, EEG, ECG, eyelid closure, skin conductance. This scenario indicates that the driver is not in reasonable driving shape. EOG signals are used in this suggested framework. The Electrooculogram (EOG) is an electrical activity indicator related to eye motions. Other signal origins, called objects, may contaminate the electrooculography signal. Eye wink is one of the key causes of disturbance in the EOG signals among the various artefact sources. Blink attributes are determined from these signals, and they are evaluated using Fuzzy logic.

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.

In this paper, a novel method for removing white gaussian noise from raw ECG signals based on Stationary Wavelet Transform and Interval Thresholding is proposed. Unlike the classical existing denoising methods in the wavelet domains, that apply a simple thresholding to details coefficients, we considers the extremums of each zero-crossings interval in the wavelet domain a whole to perform a thresholding function, which can highlight preserve the most clinical information about the region of the QRS complex in the reconstructed ECG signal. The performance of the proposed SWT-IT (Stationary wavelet transform-Interval Thresholding) method is evaluated in terms of Signal-to-Noise Ratio (SNR), Root Mean Square Error (RMSE), and Percent Root Mean Square Difference (PRD) using real ECG signals acquired from the MIT-BIH Arrhythmia Database. The simulations results show that the proposed method demonstrate superior performance compared with conventional ECG denoising approaches based on the wavelet filtering.

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 MAT LAB 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.