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Bibliografías temáticas / Electrooculography / Artículos de revistas

Artículos de revistas sobre el tema "Electrooculography"

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Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 20 de febrero de 2023

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1

Oner, Ayse, Sarper Karakucuk, Ertugrul Mirza y Kuddusi Erkilic. "Electrooculography After Photodynamic Therapy". Documenta Ophthalmologica 111, n.º 2 (septiembre de 2005): 83–86. http://dx.doi.org/10.1007/s10633-005-4410-4.

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2

Wang, Yuqi, Lijun Zhang y Zhen Fang. "Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography". Algorithms 15, n.º 3 (3 de marzo de 2022): 84. http://dx.doi.org/10.3390/a15030084.

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Nowadays, eye fatigue is becoming more common globally. However, there was no objective and effective method for eye fatigue detection except the sample survey questionnaire. An eye fatigue detection method by machine learning based on the Single-Channel Electrooculography-based System is proposed. Subjects are required to finish the industry-standard questionnaires of eye fatigue; the results are used as data labels. Then, we collect their electrooculography signals through a single-channel device. From the electrooculography signals, the five most relevant feature values of eye fatigue are extracted. A machine learning model that uses the five feature values as its input is designed for eye fatigue detection. Experimental results show that there is an objective link between electrooculography and eye fatigue. This method could be used in daily eye fatigue detection and it is promised in the future.

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3

Mulam, Harikrishna y Malini Mudigonda. "Empirical mean curve decomposition with multiwavelet transformation for eye movements recognition using electrooculogram signals". Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 234, n.º 8 (3 de julio de 2020): 794–811. http://dx.doi.org/10.1177/0954411920924496.

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Many research works are in progress in classification of the eye movements using the electrooculography signals and employing them to control the human–computer interface systems. This article introduces a new model for recognizing various eye movements using electrooculography signals with the help of empirical mean curve decomposition and multiwavelet transformation. Furthermore, this article also adopts a principal component analysis algorithm to reduce the dimension of electrooculography signals. Accordingly, the dimensionally reduced decomposed signal is provided to the neural network classifier for classifying the electrooculography signals, along with this, the weight of the neural network is fine-tuned with the assistance of the Levenberg–Marquardt algorithm. Finally, the proposed method is compared with the existing methods and it is observed that the proposed methodology gives the better performance in correspondence with accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F1 score, and Mathews correlation coefficient.

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4

KROGH, ERIK. "NORMAL VALUES IN CLINICAL ELECTROOCULOGRAPHY". Acta Ophthalmologica 53, n.º 4 (27 de mayo de 2009): 563–75. http://dx.doi.org/10.1111/j.1755-3768.1975.tb01775.x.

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5

KROGH, ERIK. "NORMAL VALUES IN CLINICAL ELECTROOCULOGRAPHY". Acta Ophthalmologica 54, n.º 4 (27 de mayo de 2009): 389–400. http://dx.doi.org/10.1111/j.1755-3768.1976.tb01270.x.

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6

KROGH, ERIK. "NORMAL VALUES IN CLINICAL ELECTROOCULOGRAPHY". Acta Ophthalmologica 55, n.º 2 (27 de mayo de 2009): 227–36. http://dx.doi.org/10.1111/j.1755-3768.1977.tb01304.x.

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7

KROGH, ERIK. "NORMAL VALUES IN CLINICAL ELECTROOCULOGRAPHY". Acta Ophthalmologica 55, n.º 6 (27 de mayo de 2009): 986–93. http://dx.doi.org/10.1111/j.1755-3768.1977.tb05680.x.

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8

KROGH, ERIK. "NORMAL VALUES IN CLINICAL ELECTROOCULOGRAPHY:". Acta Ophthalmologica 55, n.º 5 (27 de mayo de 2009): 739–49. http://dx.doi.org/10.1111/j.1755-3768.1977.tb08272.x.

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9

Golparvar, Ata Jedari y Murat Kaya Yapici. "Electrooculography by Wearable Graphene Textiles". IEEE Sensors Journal 18, n.º 21 (1 de noviembre de 2018): 8971–78. http://dx.doi.org/10.1109/jsen.2018.2868879.

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10

THORBURN, WILLIAM. "ELECTROOCULOGRAPHY IN THE GLAUCOMATOUS AMAUROTIC EYE". Acta Ophthalmologica 56, n.º 6 (27 de mayo de 2009): 953–56. http://dx.doi.org/10.1111/j.1755-3768.1978.tb03814.x.

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11

Tamaki, Daisuke, Hiromi Fujimori y Hisaya Tanaka. "An Interface using Electrooculography with Closed Eyes". International Symposium on Affective Science and Engineering ISASE2019 (2019): 1–4. http://dx.doi.org/10.5057/isase.2019-c000030.

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12

MacNeil, Raymond R., P. D. S. H. Gunawardane, Jamie Dunkle, Leo Zhao, Mu Chiao, Clarence W. de Silva y James T. Enns. "Using electrooculography to track closed-eye movements." Journal of Vision 21, n.º 9 (27 de septiembre de 2021): 1898. http://dx.doi.org/10.1167/jov.21.9.1898.

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13

Paul, Sananda, Anwesha Banerjee y D. N. Tibarewala. "Emotional eye movement analysis using electrooculography signal". International Journal of Biomedical Engineering and Technology 23, n.º 1 (2017): 59. http://dx.doi.org/10.1504/ijbet.2017.082224.

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14

Tibarewala, D. N., Anwesha Banerjee y Sananda Paul. "Emotional eye movement analysis using electrooculography signal". International Journal of Biomedical Engineering and Technology 23, n.º 1 (2017): 59. http://dx.doi.org/10.1504/ijbet.2017.10003041.

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15

Mirdehghan, M. S., S. M. Shushtarian, Sh Malekzadeh y N. Laal. "Recording of Electrooculography in photo phobia patients". Biophysical Journal 96, n.º 3 (febrero de 2009): 527a. http://dx.doi.org/10.1016/j.bpj.2008.12.2721.

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16

Kubacki, Arkadiusz y Arkadiusz Jakubowski. "Controlling the industrial robot model with the hybrid BCI based on EOG and eye tracking". MATEC Web of Conferences 252 (2019): 06004. http://dx.doi.org/10.1051/matecconf/201925206004.

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The article describes the design process of building a hybrid brain-computer interface based on Electrooculography (EOG) and centre eye tracking. In the first paragraph authors presented theoretical information about Electroencephalography (EEG), Electrooculography (EOG), and Eye. Authors prepared an overview of the literature concerning hybrid BCIs. The interface was built with use of bioactive sensors mounted on the head. Movement of industrial robot model was triggered by a signal from eyes movement by EOG and eye tracking. The built interface has been tested. Three experiments were carried out. In all experiments, three people aged 25-35 were involved. 30 attempts per scenario were recorded. Between each attempt, a respondent had a 1-minute break. The investigators attempted to move cube from one table to the other.

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17

Sasaki, Minoru, Muhammad Syaiful Amri Bin Suhaimi, Kojiro Matsushita, Satoshi Ito y Muhammad Ilhamdi Rusydi. "Robot Control System Based on Electrooculography and Electromyogram". Journal of Computer and Communications 03, n.º 11 (2015): 113–20. http://dx.doi.org/10.4236/jcc.2015.311018.

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18

Lei Sun, Sunan Wang, Jinhua Zhang y Xiaohu Li. "Research on Electrooculography Classification Based on Multiple Features". International Journal of Digital Content Technology and its Applications 6, n.º 10 (30 de junio de 2012): 35–42. http://dx.doi.org/10.4156/jdcta.vol6.issue10.5.

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19

Sheremet, N. L., I. A. Ronzina, V. V. Strel'nikov, A. A. Mikaelyan, I. G. Grushke, N. V. Zhorzholadze y S. L. Kiselev. "Electrooculography facilities in the retina functional state evaluation". POINT OF VIEW. EAST – WEST, n.º 2 (30 de mayo de 2019): 71–75. http://dx.doi.org/10.25276/2410-1257-2019-2-71-75.

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20

Bulling, Andreas, Jamie A. Ward, Hans Gellersen y Gerhard Tröster. "Eye Movement Analysis for Activity Recognition Using Electrooculography". IEEE Transactions on Pattern Analysis and Machine Intelligence 33, n.º 4 (abril de 2011): 741–53. http://dx.doi.org/10.1109/tpami.2010.86.

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21

Dasgupta, Anirban, Suvodip Chakraborty y Aurobinda Routray. "A two-stage framework for denoising electrooculography signals". Biomedical Signal Processing and Control 31 (enero de 2017): 231–37. http://dx.doi.org/10.1016/j.bspc.2016.08.012.

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22

C, Nupur. "Electrooculography in the Field of Assistive Interaction Communication". International Journal on Recent and Innovation Trends in Computing and Communication 3, n.º 3 (2015): 1153–56. http://dx.doi.org/10.17762/ijritcc2321-8169.150358.

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23

Yusupov, Azamat, Mukhiddin Ziyoviddinov, Shavkat Mukhanov y O. O. Sobirov. "FUNCTIONAL METHODS OF EARLY DIAGNOSTICS OF AGE MACULAR DEGENERATION.(LITERATURE REVIEW)". UZBEK MEDICAL JOURNAL Special issue, n.º 2 (30 de mayo de 2021): 61–67. http://dx.doi.org/10.26739/2181-0664-2021-si-2-10.

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This article provides an unsystematic literature review devoted to analyzing the currently existing methods of functional diagnostics for age-related macular degeneration.The essence, advantages and disadvantages, and literature data on the use of such methods asphotostresstest, electrooculography, dark adaptation study, contrast sensitivity function assessment, light and color sensitivity study,electroretinographyand critical flicker fusion frequencyare described.Based on the analysis of literature data, itis shownthat currently, there is a need to searchfor informative and accessible methods of functional diagnostics in age-related macular degeneration, especially for its early diagnosis. The analysis has shown that the existing methods are mainly aimed either at fixing secondary morphological changes in the layer of pigment epithelial cells, at identifying the pathology of the pigment epithelium in conjunction with determining the function of photoreceptor elements, or at a comprehensive assessment of the structures of several layers of the retina. Keywords:age-related macular degeneration; methods of functional diagnostics; photostress test; electrooculography; contrast sensitivity

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24

Banerjee, Anwesha, Shreyasi Datta, Monalisa Pal, D. N. Tibarewala y Amit Konar. "ELECTROOCULOGRAPHY BASED READING SPEED ANALYSIS AS AN ASSISTIVE TOOL". Biomedical Engineering: Applications, Basis and Communications 27, n.º 02 (17 de marzo de 2015): 1550016. http://dx.doi.org/10.4015/s1016237215500167.

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Dyslexia is a well-known reading disorder that involves difficulty in fluent reading, decoding and processing of words despite adequate intelligence. It is common that the reading speed of dyslexic patients is lower than their normal counterparts, because of slow letter and word processing. Eye movements in dyslexic patients are significantly different from that of normal individuals, in terms of the presence of frequent fixations and stares in the former. This work proposes a Human Computer Interactive system to assist individuals having low reading speed to increase their reading speed by the analysis of eye movements. Eye movement data for different reading speeds is recorded using a laboratory developed Electrooculogram acquisition system. From the data, Adaptive Autoregressive (AAR) parameters, Band Power Estimates and Wavelet Coefficients are extracted as signal features. Reading speeds are classified using different pattern classifiers from which an average accuracy of 94.67% over all classes and participants is obtained using Radial Basis Function (RBF) Support Vector Machine (SVM) Tree classifier and AAR Parameters as features. Friedman test is done to select the best classifier. The trained classifier is used to recognize the reading speeds of a set of new normal individuals. If the reading speeds are less than a preset threshold, that individual is trained repeatedly for 10 days for improvement. An improvement of reading speed is observed by the decrease in the misclassification rate from 45.1% to 9.92% in 10 days for the fastest speed (1 sentence/2 s) over all the subjects. This work is carried out on healthy individuals. However, the results reveal that the proposed system may also be used for training and assisting children with dyslexia or other similar reading disabilities children.

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25

Stula, Tomas, Antonino Proto, Jan Kubicek, Lukas Peter, Martin Cerny y Marek Penhaker. "A MATLAB-BASED GUI FOR REMOTE ELECTROOCULOGRAPHY VISUAL EXAMINATION". Lékař a technika - Clinician and Technology 50, n.º 3 (19 de marzo de 2021): 101–13. http://dx.doi.org/10.14311/ctj.2020.3.04.

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In this work, a MATLAB-based graphical user interface is proposed for the visual examination of several eye movements. The proposed solution is algorithm-based, which localizes the area of the eye movement, removes artifacts, and calculates the view trajectory in terms of direction and orb deviation. To compute the algorithm, a five-electrode configuration is needed. The goodness of the proposed MATLAB-based graphical user interface has been validated, at the Clinic of Child Neurology of University Hospital of Ostrava, through the EEG Wave Program, which was considered as “gold standard” test. The proposed solution can help physicians on studying cerebral diseases, or to be used for the development of human-machine interfaces useful for the improvement of the digital era that surrounds us today.

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26

Rusydi, Muhammad Ilhamdi, Takeo Okamoto, Satoshi Ito y Minoru Sasaki. "Controlling 3-D Movement of Robot Manipulator using Electrooculography". International Journal on Electrical Engineering and Informatics 10, n.º 1 (31 de marzo de 2018): 170–85. http://dx.doi.org/10.15676/ijeei.2018.10.1.12.

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27

Exaudi, Kemahyanto, Rendyansyah Rendyansyah y Aditya Putra Perdana Prasetyo. "Kontrol Robot Menggunakan Gerakan Mata Berbasis Sinyal Electrooculography (EOG)". Jurnal ELTIKOM 5, n.º 2 (10 de septiembre de 2021): 100–109. http://dx.doi.org/10.31961/eltikom.v5i2.464.

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Biomedical technology has now been widely adopted as a means of monitoring the human body in real-time. For example, to detect eye movement. In the medical world, eye movement can be used to determine the type of disease. With the application of human-machine interface (HMI) technology, eyeball movement can be developed in the robotics industry as robot navigation. For example, by moving the eyeball left and right, the robot can interpret the eye signal to move left and right. The interaction between the eyeball movement and the robot is of particular concern in this study. This study aimed to design a measuring instrument for eye movement detection using Electrooculography (EOG) techniques to move a wheeled robot. The EOG measuring instrument consisting of an instrument differential amplifier, a low pass filter, and a high pass filter has been applied in this research. The signal generator technique on EOG is carried out by placing electrodes on three sides of the face, namely forehead (G), left horizontal (H-), right horizontal (H +). The experimental results showed a significant difference between the left and right eye movement amplitude signals. This amplitude is used to classify the movement of the robot wheel towards the left and right. The process of sending robot signals and EOG measuring instruments uses Bluetooth HC-05 serial communication. Based on the research results, it is proven that the robot manages to move left and right according to the eyeball movement.

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28

SAITO, Hirotsugu, Ryoichi KONISHI y Chiharu ISHII. "2A1-H09 Control of an electric wheelchair using Electrooculography". Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2015 (2015): _2A1—H09_1—_2A1—H09_4. http://dx.doi.org/10.1299/jsmermd.2015._2a1-h09_1.

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29

Sun, Lei, Sun an Wang, Jin hua Zhang y Xiao hu Li. "Robust electrooculography endpoint detection based on autoregressive spectral entropy". International Journal of Biomedical Engineering and Technology 10, n.º 3 (2012): 239. http://dx.doi.org/10.1504/ijbet.2012.050292.

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30

Choudhari, Ajit M., Prasanna Porwal, Venkatesh Jonnalagedda y Fabrice Mériaudeau. "An Electrooculography based Human Machine Interface for wheelchair control". Biocybernetics and Biomedical Engineering 39, n.º 3 (julio de 2019): 673–85. http://dx.doi.org/10.1016/j.bbe.2019.04.002.

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31

Khan, Kamron N., Farrah Islam, Graham E. Holder, Anthony Robson, Andrew R. Webster, Anthony T. Moore y Michel Michaelides. "NORMAL ELECTROOCULOGRAPHY IN BEST DISEASE AND AUTOSOMAL RECESSIVE BESTROPHINOPATHY". Retina 38, n.º 2 (febrero de 2018): 379–86. http://dx.doi.org/10.1097/iae.0000000000001523.

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32

Zargari Marandi, Ramtin y S. H. Sabzpoushan. "Qualitative modeling of the decision-making process using electrooculography". Behavior Research Methods 47, n.º 4 (17 de diciembre de 2014): 1404–12. http://dx.doi.org/10.3758/s13428-014-0549-9.

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33

Lledó, L. D., A. Úbeda, E. Iáñez y J. M. Azorín. "Internet browsing application based on electrooculography for disabled people". Expert Systems with Applications 40, n.º 7 (junio de 2013): 2640–48. http://dx.doi.org/10.1016/j.eswa.2012.11.012.

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34

Lagodzinski, P., K. Shirahama y M. Grzegorzek. "Codebook-based electrooculography data analysis towards cognitive activity recognition". Computers in Biology and Medicine 95 (abril de 2018): 277–87. http://dx.doi.org/10.1016/j.compbiomed.2017.10.026.

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35

Barber, C., G. Brimlow, N. R. Galloway, P. Toghill y R. P. Walt. "Dark adaptation compared with electrooculography in primary biliary cirrhosis". Documenta Ophthalmologica 71, n.º 4 (abril de 1989): 397–402. http://dx.doi.org/10.1007/bf00152766.

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36

Phukpattaranont, Pornchai, Siriwadee Aungsakul, Angkoon Phinyomark y Chusak Limsakul. "Efficient feature for classification of eye movements using electrooculography signals". Thermal Science 20, suppl. 2 (2016): 563–72. http://dx.doi.org/10.2298/tsci151005038p.

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Electrooculography (EOG) signal is widely and successfully used to detect activities of human eye. The advantages of the EOG-based interface over other conventional interfaces have been presented in the last two decades; however, due to a lot of information in EOG signals, the extraction of useful features should be done before the classification task. In this study, an efficient feature extracted from two directional EOG signals: vertical and horizontal signals has been presented and evaluated. There are the maximum peak and valley amplitude values, the maximum peak and valley position values, and slope, which are derived from both vertical and horizontal signals. In the experiments, EOG signals obtained from five healthy subjects with ten directional eye movements were employed: up, down, right, left, up-right, up-left, down-right down-left clockwise and counterclockwise. The mean feature values and their standard deviations have been reported. The difference between the mean values of the proposed feature from different eye movements can be clearly seen. Using the scatter plot, the differences in features can be also clearly observed. Results show that classification accuracy can approach 100% with a simple distinction feature rule. The proposed features can be useful for various advanced human-computer interface applications in future researches.

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37

A. A. AlHaddad, R. Sudirman y C. Omar. "Approaches to Guide Powered Wheelchair Autonomously Based On Electrooculography Signals". International Journal of Engineering and Industries 3, n.º 4 (31 de diciembre de 2012): 65–74. http://dx.doi.org/10.4156/ijei.vol3.issue4.8.

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38

V, Karthik Raj. "Study of electrooculography signal acquisition sites for assistive device applications". International Journal of Advanced Technology and Engineering Exploration 8, n.º 81 (31 de agosto de 2021): 989–1004. http://dx.doi.org/10.19101/ijatee.2021.874221.

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39

Joo, Jin-Ho, Jeong Han Choi y Sang Woong Moon. "Usefulness of Fast Oscillation Electrooculography in Chronic Central Serous Chorioretinopathy". Journal of Retina 5, n.º 2 (30 de noviembre de 2020): 149–56. http://dx.doi.org/10.21561/jor.2020.5.2.149.

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40

Bukhari. "Identification of Electrooculography Signals Frequency Energy Distribution Using Wavelet Algorithm". Journal of Computer Science 7, n.º 11 (1 de noviembre de 2011): 1619–25. http://dx.doi.org/10.3844/jcssp.2011.1619.1625.

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41

Barea, R., L. Boquete, M. Mazo y E. Lopez. "System for assisted mobility using eye movements based on electrooculography". IEEE Transactions on Neural Systems and Rehabilitation Engineering 10, n.º 4 (diciembre de 2002): 209–18. http://dx.doi.org/10.1109/tnsre.2002.806829.

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42

Makati, Ravie, Diana Shechtman, Eulogio Besada y Joseph J. Pizzimenti. "Electrooculography and Optical Coherence Tomography Reveal Late-Onset Best Disease". Optometry and Vision Science 91, n.º 11 (noviembre de 2014): e274-e277. http://dx.doi.org/10.1097/opx.0000000000000403.

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43

Gallicchio, Germano, Andrew Cooke y Christopher Ring. "Assessing ocular activity during performance of motor skills using electrooculography". Psychophysiology 55, n.º 7 (9 de febrero de 2018): e13070. http://dx.doi.org/10.1111/psyp.13070.

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44

Liang, Timothy, Mark I. Boulos, Brian J. Murray, Sridhar Krishnan, Hans Katzberg y Karthikeyan Umapathy. "Analysis of electrooculography signals for the detection of Myasthenia Gravis". Clinical Neurophysiology 130, n.º 11 (noviembre de 2019): 2105–13. http://dx.doi.org/10.1016/j.clinph.2019.08.008.

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45

Hu, Shuyan, Björn Peters y Gangtie Zheng. "Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal". IET Intelligent Transport Systems 7, n.º 1 (1 de marzo de 2013): 105–13. http://dx.doi.org/10.1049/iet-its.2012.0045.

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46

Skotte, J. H., J. K. Nøjgaard, L. V. Jørgensen, K. B. Christensen y G. Sjøgaard. "Eye blink frequency during different computer tasks quantified by electrooculography". European Journal of Applied Physiology 99, n.º 2 (7 de noviembre de 2006): 113–19. http://dx.doi.org/10.1007/s00421-006-0322-6.

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47

Martinek, Radek, Martina Ladrova, Michaela Sidikova, Rene Jaros, Khosrow Behbehani, Radana Kahankova y Aleksandra Kawala-Sterniuk. "Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach—Part III: Other Biosignals". Sensors 21, n.º 18 (10 de septiembre de 2021): 6064. http://dx.doi.org/10.3390/s21186064.

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Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).

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48

López, Alberto, Francisco Ferrero y Octavian Postolache. "An Affordable Method for Evaluation of Ataxic Disorders Based on Electrooculography". Sensors 19, n.º 17 (30 de agosto de 2019): 3756. http://dx.doi.org/10.3390/s19173756.

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Ataxias are a group of neurodegenerative disorders characterized by cerebellar dysfunction that cause irregularities in the rate, rhythm, amplitude, and force of voluntary movements. The electrooculogram (EOG) may provide clues about ataxic disorders because most of these patients have difficulty with visual tracking and fixing their gaze. Using electrodes, EOG records the biopotentials generated by eye movements. In this paper, three surface electrodes are placed around the eye socket, and the biopotentials generated by eye movements are acquired using a commercial bioamplifier device. Next, the signals are sent to the computer to be digitally processed to extract the rate of saccades as well as the delay and deviation of the gaze in response to a stimulus. These features are analysed in a novel software application designed to help physicians in evaluating ataxia. After applying several tests to both healthy and ataxia-affected patients, differences in EOG results were found. The evaluation of the reliability of the designed software application is made according to three metrics: sensitivity, specificity, and accuracy. The results indicate the proposed system’s viability as an affordable method for evaluation of ataxic disorders.

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49

Barea, Rafael, Luciano Boquete, Jose Manuel Rodriguez-Ascariz, Sergio Ortega y Elena López. "Sensory System for Implementing a Human—Computer Interface Based on Electrooculography". Sensors 11, n.º 1 (29 de diciembre de 2010): 310–28. http://dx.doi.org/10.3390/s110100310.

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Iáñez, Eduardo, José M. Azorín, Eduardo Fernández y Andrés Úbeda. "Interface based on electrooculography for velocity control of a robot arm". Applied Bionics and Biomechanics 7, n.º 3 (3 de septiembre de 2010): 199–207. http://dx.doi.org/10.1080/11762322.2010.503107.

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