Relevant bibliographies by topics / Electrooculograms

Academic literature on the topic 'Electrooculograms'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Electrooculograms"

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Becerra-García, Roberto A., Rodolfo García-Bermúdez, and Gonzalo Joya. "Differentiation of Saccadic Eye Movement Signals." Sensors 21, no. 15 (July 24, 2021): 5021. http://dx.doi.org/10.3390/s21155021.

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Saccadic electrooculograms are discrete biosignals that contain the instantaneous angular position of the human eyes as a response to saccadic visual stimuli. These signals are essential to monitor and evaluate several neurological diseases, such as Spinocerebellar Ataxia type 2 (SCA2). For this, biomarkers such as peak velocity, latency and duration are computed. To compute these biomarkers, we need to obtain the velocity profile of the signals using numerical differentiation methods. These methods are affected by the noise present in the electrooculograms, specially in subjects that suffer neurological diseases. This noise complicates the comparison of the differentiation methods using real saccadic signals because of the impossibility of establishing exact saccadic onset and offset points. In this work, we evaluate 16 differentiation methods by the design of an experiment that uses synthetic saccadic electrooculograms generated from parametric models of both healthy subjects and subjects suffering from Spinocerebellar Ataxia type 2 (SCA2). For these synthetic electrooculograms the exact velocity profile is known, hence we can use them as a reference for comparison and error computing for the tasks of saccade identification and saccade biomarker computing. Finally, we identify the best fitting method or methods for each evaluated task.
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Chang, Won-Du. "Electrooculograms for Human–Computer Interaction: A Review." Sensors 19, no. 12 (June 14, 2019): 2690. http://dx.doi.org/10.3390/s19122690.

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Eye movements generate electric signals, which a user can employ to control his/her environment and communicate with others. This paper presents a review of previous studies on such electric signals, that is, electrooculograms (EOGs), from the perspective of human–computer interaction (HCI). EOGs represent one of the easiest means to estimate eye movements by using a low-cost device, and have been often considered and utilized for HCI applications, such as to facilitate typing on a virtual keyboard, moving a mouse, or controlling a wheelchair. The objective of this study is to summarize the experimental procedures of previous studies and provide a guide for researchers interested in this field. In this work the basic characteristics of EOGs, associated measurements, and signal processing and pattern recognition algorithms are briefly reviewed, and various applications reported in the existing literature are listed. It is expected that EOGs will be a useful source of communication in virtual reality environments, and can act as a valuable communication tools for people with amyotrophic lateral sclerosis.
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Ishii, Chiharu, Shunsuke Murooka, and Minato Tajima. "Navigation of an Electric Wheelchair Using Electromyograms, Electrooculograms, and Electroencephalograms." International Journal of Mechanical Engineering and Robotics Research 7, no. 2 (2016): 143–49. http://dx.doi.org/10.18178/ijmerr.7.2.143-149.

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Ianov, Alexsandr Igorevitch, Hiroaki Kawamoto, and Yoshiyuki Sankai. "Development of Hybrid Resistive-Capacitive Electrodes for Electroencephalograms and Electrooculograms." IEEJ Transactions on Sensors and Micromachines 133, no. 3 (2013): 57–65. http://dx.doi.org/10.1541/ieejsmas.133.57.

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Dasgupta, Anirban, and Aurobinda Routray. "Piecewise empirical mode Bayesian estimation – A new method to denoise electrooculograms." Biomedical Signal Processing and Control 70 (September 2021): 102945. http://dx.doi.org/10.1016/j.bspc.2021.102945.

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Chang, Won-Du, Ho-Seung Cha, and Chang-Hwan Im. "Removing the Interdependency between Horizontal and Vertical Eye-Movement Components in Electrooculograms." Sensors 16, no. 2 (February 14, 2016): 227. http://dx.doi.org/10.3390/s16020227.

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Sasaki, Tatsuya, Kyouichi Suzuki, Masato Matsumoto, Taku Sato, Namio Kodama, and Keiko Yago. "Origin of surface potentials evoked by electrical stimulation of oculomotor nerves: are they related to electrooculographic or electromyographic events?" Journal of Neurosurgery 97, no. 4 (October 2002): 941–44. http://dx.doi.org/10.3171/jns.2002.97.4.0941.

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Object. Evoked potentials elicited by electrical stimulation of the oculomotor nerve and recorded from surface electrodes placed on the skin around the eyeball reportedly originate in the eye and are represented on electrooculograms. Because evoked potentials recorded from surface electrodes are extremely similar to those of extraocular muscles, which are represented on electromyograms, the authors investigated the true origin of these potentials. Methods. Evoked potentials elicited by electrical stimulation of the canine oculomotor nerve were recorded from surface electrodes placed on the skin around the eyeball. A thread sutured to the center of the cornea was pulled and the potentials that were evoked during the resultant eye movement were recorded. These potentials were confirmed to originate in the eye and to be represented on electrooculograms because their waveforms were unaffected by the administration of muscle relaxant. To eliminate the influence of this source, the retina, a main origin of standing potentials of the eyeball, was removed. This resulted in the disappearance of electrooculography (EOG) waves elicited by eye movement. Surface potentials elicited by oculomotor nerve stimulation were the same before and after removal of the retina. Again the oculomotor nerve was electrically stimulated and electromyography (EMG) response of the extraocular muscles was recorded at the same time that potentials were recorded from the surface electrodes. In their peak latencies, amplitudes, and waveforms, the evoked potentials obtained from surface electrodes were almost identical to EMG responses of extraocular muscles. Conclusions. Evoked potentials elicited by electrical stimulation of the oculomotor nerves and obtained from surface electrodes originated from EMG responses of extraocular muscles. These evoked potentials do not derive from the eye.
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Noguchi, Kazuhito, Koichi Haishi, and Daisuke Sato. "An Illusion of Velocity in Motion Perception." Perceptual and Motor Skills 78, no. 1 (February 1994): 112–14. http://dx.doi.org/10.2466/pms.1994.78.1.112.

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We report a phenomenon that seems to have potential to elucidate a role of eye movement in motion perception. When tracking a target controlled by a triangular wave, the viewer perceives movement of the target like a ball bouncing in between two walls. We measured eye movement with electrooculograms (EOGs) when the subject was tracking a target controlled by a triangular wave. Eye movement after passing the turning point and rapidly returning to the target with saccadic movement and then smoothly tracking the target was recorded for all 4 adults. It was considered that extraretinal information on eye position during saccade may mainly contribute to this illusion.
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Chakraborty, Suvodip, Anirban Dasgupta, and Aurobinda Routray. "Localization of eye Saccadic signatures in Electrooculograms using sparse representations with data driven dictionaries." Pattern Recognition Letters 139 (November 2020): 104–11. http://dx.doi.org/10.1016/j.patrec.2017.11.001.

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Li, Hao, Xia Mao, and Lijiang Chen. "An emotion classification method from electroencephalogram based on 1/f fluctuation theory." Measurement and Control 53, no. 5-6 (April 24, 2020): 824–32. http://dx.doi.org/10.1177/0020294020913893.

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Electroencephalogram data are easily affected by artifacts, and a drift may occur during the signal acquisition process. At present, most research focuses on the automatic detection and elimination of artifacts in electrooculograms, electromyograms and electrocardiograms. However, electroencephalogram drift data, which affect the real-time performance, are mainly manually calibrated and abandoned. An emotion classification method based on 1/f fluctuation theory is proposed to classify electroencephalogram data without removing artifacts and drift data. The results show that the proposed method can still achieve a great classification accuracy of 75% in cases in which artifacts and drift data exist when using the support vector machine classifier. In addition, the real-time performance of the proposed method is guaranteed.
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Dissertations / Theses on the topic "Electrooculograms"

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Coughlin, Michael J., and n/a. "Calibration of Two Dimensional Saccadic Electro-Oculograms Using Artificial Neural Networks." Griffith University. School of Applied Psychology, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030409.110949.

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The electro-oculogram (EOG) is the most widely used technique for recording eye movements in clinical settings. It is inexpensive, practical, and non-invasive. Use of EOG is usually restricted to horizontal recordings as vertical EOG contains eyelid artefact (Oster & Stern, 1980) and blinks. The ability to analyse two dimensional (2D) eye movements may provide additional diagnostic information on pathologies, and further insights into the nature of brain functioning. Simultaneous recording of both horizontal and vertical EOG also introduces other difficulties into calibration of the eye movements, such as different gains in the two signals, and misalignment of electrodes producing crosstalk. These transformations of the signals create problems in relating the two dimensional EOG to actual rotations of the eyes. The application of an artificial neural network (ANN) that could map 2D recordings into 2D eye positions would overcome this problem and improve the utility of EOG. To determine whether ANNs are capable of correctly calibrating the saccadic eye movement data from 2D EOG (i.e. performing the necessary inverse transformation), the ANNs were first tested on data generated from mathematical models of saccadic eye movements. Multi-layer perceptrons (MLPs) with non-linear activation functions and trained with back propagation proved to be capable of calibrating simulated EOG data to a mean accuracy of 0.33° of visual angle (SE = 0.01). Linear perceptrons (LPs) were only nearly half as accurate. For five subjects performing a saccadic eye movement task in the upper right quadrant of the visual field, the mean accuracy provided by the MLPs was 1.07° of visual angle (SE = 0.01) for EOG data, and 0.95° of visual angle (SE = 0.03) for infrared limbus reflection (IRIS®) data. MLPs enabled calibration of 2D saccadic EOG to an accuracy not significantly different to that obtained with the infrared limbus tracking data.
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Coughlin, Michael J. "Calibration of Two Dimensional Saccadic Electro-Oculograms Using Artificial Neural Networks." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/365854.

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The electro-oculogram (EOG) is the most widely used technique for recording eye movements in clinical settings. It is inexpensive, practical, and non-invasive. Use of EOG is usually restricted to horizontal recordings as vertical EOG contains eyelid artefact (Oster & Stern, 1980) and blinks. The ability to analyse two dimensional (2D) eye movements may provide additional diagnostic information on pathologies, and further insights into the nature of brain functioning. Simultaneous recording of both horizontal and vertical EOG also introduces other difficulties into calibration of the eye movements, such as different gains in the two signals, and misalignment of electrodes producing crosstalk. These transformations of the signals create problems in relating the two dimensional EOG to actual rotations of the eyes. The application of an artificial neural network (ANN) that could map 2D recordings into 2D eye positions would overcome this problem and improve the utility of EOG. To determine whether ANNs are capable of correctly calibrating the saccadic eye movement data from 2D EOG (i.e. performing the necessary inverse transformation), the ANNs were first tested on data generated from mathematical models of saccadic eye movements. Multi-layer perceptrons (MLPs) with non-linear activation functions and trained with back propagation proved to be capable of calibrating simulated EOG data to a mean accuracy of 0.33° of visual angle (SE = 0.01). Linear perceptrons (LPs) were only nearly half as accurate. For five subjects performing a saccadic eye movement task in the upper right quadrant of the visual field, the mean accuracy provided by the MLPs was 1.07° of visual angle (SE = 0.01) for EOG data, and 0.95° of visual angle (SE = 0.03) for infrared limbus reflection (IRIS®) data. MLPs enabled calibration of 2D saccadic EOG to an accuracy not significantly different to that obtained with the infrared limbus tracking data.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Applied Psychology
Griffith Health
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Ma, Jiaxin. "Research on Human-Machine Interfaces of Vigilance Estimation and Robot Control based on Biomedical Signals." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/199268.

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Young, Chieh-neng, and 楊傑能. "Electrooculogram Signals for the Detection of REM Sleep Via VQ Methods." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/h372fr.

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博士
國立中山大學
機械與機電工程學系研究所
95
One primary topic of sleep studies is the depth of sleep. According to definitions of R&K rules, human sleep can be roughly divided into three different stages: Awake, Non-rapid-eye-movement (NREM) Sleep, and Rapid-eye-movement (REM) Sleep. Moreover, sleep stages are scored mainly by EEG signals and complementally by EOG and EMG signals. Many researchers have indicated that diseases or disorders occur during sleep will affect life quality of patients. For example, REM sleep-related dyssomnia is highly correlated with neurodegenerative or mental disorders such as major depression. Furthermore, sleep apnea is one of the most common sleep disorders at present. Untreated sleep apnea can increase the risk of mental and cardiovascular diseases. This research proposes a detection method of REM sleep. Take into account the environment of homecare, we just extract and analyze EOG signals for the sake of convenience in comparison with EEG channels. By analyzing elementary waveforms of EOG signals based on VQ method, the proposed method performs a classification accuracy of 67.71% in a group application. The corresponding sensitivity and specificity are 73.38% and 68.95% respectively. In contrast, the average classification accuracy is 82.02% in personalized applications. And the corresponding average sensitivity and specificity are 83.05% and 81.62% respectively. Experimental results demonstrate the feasibility of detecting REM sleep via the proposed method, especially in personalized applications. This will be propitious to a long term tracing and research of personal sleep status.
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Chen, Hsiaw-Shuw, and 陳孝壽. "THE STUDY OF RELATIONSHIP BETWEEN ELECTROOCULOGRAM AND THE FEATURES OF CLOSE EYE VIDEO IMAGES." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/66898671229447341934.

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碩士
國立清華大學
產業研發碩士積體電路設計專班
96
The brain scientific research can be regarded as one of the contemporary popular studies in recent years. The integration of biology, medicine, physics, electrical and information engineering has resulted in a substantial development in brain related researches and applications. For example, there were breakthrough progresses in the researches on sleeping status, brain waves status, and excitatory zone of cortex, etc. In sleep studies, the majority of sleep measurements are conducted by using invasive sensing approaches which will more or less disturb the sleep. It’s natural to ask whether there exists a noninvasive approach that is not only cheaper and non-contact sensing, but also able to obtain the corresponding physiological signal. By looking at the physiological signals comprehensively, we discovered that most values of theirs strength are in μV or weaker if in a form of voltage signal; in addition, they are even weaker and difficult to measure if in the magnetic field signal form because of the difficulty of screening. However, the signal of Electrooculogram (EOG), with its stronger signal strength (in mV level), and related to the sleeping status, is frequently adopted along with other physiological measurements in the sleep study. If it is possible to use the remote sensing technique to acquire the EOG signal, a non-invasive and cheap approach of monitoring sleep may be obtained then. Therefore, this study is emphasized on the possibility of using the computer vision method to establish the function of EOG signal obtained from the traditional electrode. In order to develop the computer vision EOG, we have to seek out the correlation between the EOG and features of eye images obtained form computer vision. We thus utilized the digital image processing techniques to find out the image features of eye movement under close eye condition that related to the EOG. In pre-processing stage, we determined the position of eyelashes by examining the images from the video sequences taken of the close eye, and further to position the moveable range for eyes, named as the ROI (range of image). Then, we conducted the process of feature extraction to extract out 4 features: Spatial Domain Feature, Statistical Feature, Frequency Domain Feature, and Entropy Feature, respectively. Next, we investigate their correlations to the EOG by comparing these 4 features with the EOG signals obtained from the actual EOG measuring process. We then discovered a good correspondence between the Entropy Feature and EOG signal. As a result, the Entropy Feature may be a better approach of correspondence to develop the computer vision EOG.
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Books on the topic "Electrooculograms"

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Butkov, Nic. Polysomnography. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0007.

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This chapter provides an overview of the sleep recording process, including the application of electrodes and sensors to the patient, instrumentation, signal processing, digital polysomnography (PSG), and artifact recognition. Topics discussed include indications for PSG, standard recording parameters, patient preparation, electrode placement for recording the electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG), the use of respiratory transducers, oximetry, signal processing, filters, digital data display, electrical safety, and patient monitoring. This chapter also includes record samples of the various types of recording artifacts commonly found in sleep studies, with a detailed description of their causes, preventative measures, and recommended corrective actions.
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Book chapters on the topic "Electrooculograms"

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Zayit-Soudry, Shiri, and Ido Perlman. "Electrooculogram." In Encyclopedia of Ophthalmology, 1–3. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-35951-4_1033-1.

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Zayit-Soudry, Shiri, and Ido Perlman. "Electrooculogram." In Encyclopedia of Ophthalmology, 705–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-540-69000-9_1033.

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Yang, Fumeng, and Bin Xia. "Single Electrooculogram Channel-Based Sleep Stage Classification." In Advances in Cognitive Neurodynamics (V), 595–600. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0207-6_80.

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Banerjee, Anwesha, Shreyasi Datta, Amit Konar, D. N. Tibarewala, and Janarthanan Ramadoss. "Cognitive Activity Recognition Based on Electrooculogram Analysis." In Smart Innovation, Systems and Technologies, 637–44. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07353-8_73.

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Sandra, D’Souza, and N. Sriraam. "Feature Based Reading Skill Analysis Using Electrooculogram Signals." In Advanced Computing and Communication Technologies, 233–44. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1023-1_24.

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Medeiros, Romeu, Ana Cláudia S. Souza, and Gustavo F. Rodrigues. "Mouse Control Interface Using Electrooculogram and Genetic Programming." In XXVI Brazilian Congress on Biomedical Engineering, 335–39. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-2517-5_51.

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Goswami, Laxmi. "Human Computer Interface Using Electrooculogram as a Substitute." In International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing, 170–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92905-3_21.

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Borchardt, A. R., L. S. Schiavon, L. G. L. Silva, A. A. Souza Junior, and M. G. Lucas. "Acquisition and Comparison of Classification Algorithms in Electrooculogram Signals." In XXVII Brazilian Congress on Biomedical Engineering, 1999–2003. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-70601-2_292.

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Gondou, Kazuya, Hiroki Tamura, and Koichi Tanno. "A Study on Human Interface for Communication Using Electrooculogram Signals." In Advances in Intelligent Systems and Computing, 311–20. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23207-2_31.

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Ning, Bo, Ming-jie Li, Tong Liu, Hui-min Shen, Liang Hu, and Xin Fu. "Human Brain Control of Electric Wheelchair with Eye-Blink Electrooculogram Signal." In Intelligent Robotics and Applications, 579–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33509-9_58.

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Conference papers on the topic "Electrooculograms"

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Mishra, Saswat, Yongkuk Lee, Dong Sup Lee, and Woon-Hong Yeo. "Fractal-Structured, Wearable Soft Sensors for Control of a Robotic Wheelchair via Electrooculograms." In 2017 IEEE 67th Electronic Components and Technology Conference (ECTC). IEEE, 2017. http://dx.doi.org/10.1109/ectc.2017.68.

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Datta, Shreyasi, Anwesha Banerjee, Amit Konar, and D. N. Tibarewala. "Electrooculogram based cognitive context recognition." In 2014 International Conference on Electronics, Communication and Instrumentation (ICECI). IEEE, 2014. http://dx.doi.org/10.1109/iceci.2014.6767362.

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Brahmaiah, V. Priyanka, Y. Padma Sai, and M. N. Giri Prasad. "Data Acquisition System of Electrooculogram." In 2017 IEEE 7th International Advance Computing Conference (IACC). IEEE, 2017. http://dx.doi.org/10.1109/iacc.2017.0149.

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Atique, Md Moin Uddin, Sakhawat Hossen Rakib, and Khondkar Siddique-e-Rabbani. "An electrooculogram based control system." In 2016 International Conference on Informatics, Electronics and Vision (ICIEV). IEEE, 2016. http://dx.doi.org/10.1109/iciev.2016.7760113.

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Alquran, Hiam, Ali Mohammad Alqudah, Isam Abu Qasmieh, and Sami Almashaqbeh. "Gaussian Model of Electrooculogram Signals." In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). IEEE, 2019. http://dx.doi.org/10.1109/jeeit.2019.8717499.

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Trikha, Mrinal, Tapan Gandhi, Ayush Bhandari, and Vijay Khare. "Multiple Channel Electrooculogram Classification using Automata." In 2007 IEEE International Workshop on Medical Measurement and Applications. IEEE, 2007. http://dx.doi.org/10.1109/memea.2007.4285158.

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Malaekah, Emad, Chanakya Reddy Patti, and Dean Cvetkovic. "Automatic sleep-wake detection using electrooculogram signals." In 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 2014. http://dx.doi.org/10.1109/iecbes.2014.7047603.

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Rosa, Andrei, Virgı́nia Bordignon, Carla Becker, and Sergio Almeida. "A New Approach for Electrooculogram Recognition Algorithms." In XXXV Simpósio Brasileiro de Telecomunicações e Processamento de Sinais. Sociedade Brasileira de Telecomunicações, 2017. http://dx.doi.org/10.14209/sbrt.2017.40.

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Kim-Tien, Nguyen, and Nguyen Truong-Thinh. "Using Electrooculogram and Electromyogram for powered wheelchair." In 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2011. http://dx.doi.org/10.1109/robio.2011.6181515.

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Bardhan, Jayetri, P. Suma, and M. Jyothirmayi. "Motorized wheelchair control using electrooculogram and head gear." In 2016 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2016. http://dx.doi.org/10.1109/inventive.2016.7830190.

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