Relevant bibliographies by topics / Electrooculography

Academic literature on the topic 'Electrooculography'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Electrooculography"

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Oner, Ayse, Sarper Karakucuk, Ertugrul Mirza, and Kuddusi Erkilic. "Electrooculography After Photodynamic Therapy." Documenta Ophthalmologica 111, no. 2 (September 2005): 83–86. http://dx.doi.org/10.1007/s10633-005-4410-4.

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Wang, Yuqi, Lijun Zhang, and Zhen Fang. "Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography." Algorithms 15, no. 3 (March 3, 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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Mulam, Harikrishna, and 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, no. 8 (July 3, 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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KROGH, ERIK. "NORMAL VALUES IN CLINICAL ELECTROOCULOGRAPHY." Acta Ophthalmologica 53, no. 4 (May 27, 2009): 563–75. http://dx.doi.org/10.1111/j.1755-3768.1975.tb01775.x.

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KROGH, ERIK. "NORMAL VALUES IN CLINICAL ELECTROOCULOGRAPHY." Acta Ophthalmologica 54, no. 4 (May 27, 2009): 389–400. http://dx.doi.org/10.1111/j.1755-3768.1976.tb01270.x.

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KROGH, ERIK. "NORMAL VALUES IN CLINICAL ELECTROOCULOGRAPHY." Acta Ophthalmologica 55, no. 2 (May 27, 2009): 227–36. http://dx.doi.org/10.1111/j.1755-3768.1977.tb01304.x.

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KROGH, ERIK. "NORMAL VALUES IN CLINICAL ELECTROOCULOGRAPHY." Acta Ophthalmologica 55, no. 6 (May 27, 2009): 986–93. http://dx.doi.org/10.1111/j.1755-3768.1977.tb05680.x.

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KROGH, ERIK. "NORMAL VALUES IN CLINICAL ELECTROOCULOGRAPHY:." Acta Ophthalmologica 55, no. 5 (May 27, 2009): 739–49. http://dx.doi.org/10.1111/j.1755-3768.1977.tb08272.x.

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Golparvar, Ata Jedari, and Murat Kaya Yapici. "Electrooculography by Wearable Graphene Textiles." IEEE Sensors Journal 18, no. 21 (November 1, 2018): 8971–78. http://dx.doi.org/10.1109/jsen.2018.2868879.

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THORBURN, WILLIAM. "ELECTROOCULOGRAPHY IN THE GLAUCOMATOUS AMAUROTIC EYE." Acta Ophthalmologica 56, no. 6 (May 27, 2009): 953–56. http://dx.doi.org/10.1111/j.1755-3768.1978.tb03814.x.

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Dissertations / Theses on the topic "Electrooculography"

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Erkent, Ozgur. "An Eye Movement Analysis Of Chess Players Across Levels Of Expertise: An Electrooculography Study." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/2/12605407/index.pdf.

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The eye movements of expert and novice chess players were recorded by electrooculography (EOG) technique as they attempted to find the mate in fifteen tactically active simple chess positions. In the analysis part of the data gathered from EOG recordings, the effect of the drift, which is an important problem for EOG, was reduced. The processed data were converted into coordinates of the display on which the chess positions were demonstrated. In the test phase, the players were asked to fixate the white king which appeared on a different square on an empty chess board for one second. It was predicted on which square the subject fixated by the method developed. The predictions and the actual location of the white king were compared and the results revealed that EOG technique can be used reliably to track the eye movements of the chess players while they fixated on a chess board. It has been revealed that experts produced more fixations on the relevant squares than did the novices as the fixations were investigated. The difference between fixation duration was not significant across skill groups. The results provide evidence for an early perceptual encoding advantage for expert players attributable to chess experience.
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Ebrahim, Parisa [Verfasser], and Bin [Akademischer Betreuer] Yang. "Driver drowsiness monitoring using eye movement features derived from electrooculography / Parisa Ebrahim ; Betreuer: Bin Yang." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2016. http://d-nb.info/1118370554/34.

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Fejgl, Martin. "Fokusace očí na charakteristické prvky vizuálního vjemu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219214.

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This master’s thesis deals with muscles physiology of eye movement and questions of surface electromyography measurement. Physiology and analysis of eye movement are described here. Furthermore the paper deals with a variety of electrooculographic measuring techniques and also with resulting evaluation of measured signal.
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Němcová, Andrea. "Hodnocení únavy pomocí elektrookulografie." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220869.

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The master´s thesis deals with fatigue evaluation in electrooculography records (EOG). The theoretical part focuses on electrooculography itself, fatigue and methods used for fatigue detection from EOG. The practical part includes a plan of optimal methodology for fatigue evaluation using EOG. The EOG signals are recorded during the volunteers are watching prepared scenes. Those scenes are desribed here. There is also definition of signal processing methods with relevant block diagrams. Laboratory protocol describing EOG signals recording using Biopac data acquisition system is included. Ten volunteers were measured according to this protocol and the signal database was created. In brief questionnaire volunteers were supposed to evaluate fatigue and discomfort of the measurment. Recorded signals were processed and acquired parameters were statistically evaluated. Then the parameters were discussed in terms of fatigue detection ability. On the basis of that software application was created. This application detects fatigue in selected signal. The thesis includes detailed laboratory manual for students.
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Parks, Nathan Allen. "Neural mechanisms of short-term visual plasticity and cortical disinhbition." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28163.

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Thesis (M. S.)--Psychology, Georgia Institute of Technology, 2009.
Committee Chair: Dr. Paul Corballis, Ph.D.; Committee Member: Dr. Daniel Spieler, Ph.D.; Committee Member: Dr. Eric Schumacher, Ph.D.; Committee Member: Dr. Krish Sathian, M.D., Ph.D.; Committee Member: Dr. Randall Engle, Ph.D.
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Hasan, Md Mahmudul. "Biomedical signal based drowsiness detection using machine learning: Singular and hybrid signal approaches." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/211388/1/Md%20Mahmudul_Hasan_Thesis.pdf.

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Drowsiness is one of the main contributors to road crashes. This research program examines the utility of drowsiness detection based on singular and hybrid approaches using physiological signals of EEG, EOG, and ECG. Four supervised machine learning models were developed to detect drowsiness levels, using physiological features known to be associated with drowsiness and performance impairment. The ground truth was subjective sleepiness responses while performing a repetitive reaction time task. The outcome of the study indicates that the selected features provided higher performance in the hybrid approaches than the singular approaches, which could be useful for future research implications.
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Estepp, Justin Ronald. "An improved adaptive filtering approach for removing artifact from the electroencephalogram." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1433244703.

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Roulet, Christophe. "Etude et réalisation de systèmes de mesures et de traitements de l'activité oculo-motrice et gestuelle chez l'homme : application à l'activité physique et sportive et à l'ergonomie." Grenoble 1, 1987. http://www.theses.fr/1987GRE10175.

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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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Books on the topic "Electrooculography"

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M, Milad M., ed. Metody okulografii v issledovanii poznavatelʹnykh prot͡s︡essov i dei͡a︡telʹnosti. Moskva: Institut psikhologii RAN, 1994.

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Samuel, Sokol, ed. Electrophysiologic testing in disorders of the retina, optic nerve, and visual pathway. San Francisco, CA: American Academy of Ophthalmology, 1990.

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Electrophysiology of vision: Clinical testing and applications. Boca Raton: Taylor & Francis, 2005.

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Carr, Ronald E. Visual Electrodiagnostic Testing: A Practical Guide for the Clinician. 2nd ed. Williams & Wilkins, 1988.

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R, Heckenlively John, and Arden Geoffrey B, eds. Principles and practice of clinical electrophysiology of vision. St. Louis: Mosby Year Book, 1991.

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Birch, David G., Graham E. Holder, Mitchell G. Brigell, and Gerald Allen Fishman. Electrophysiologic Testing in Disorders of the Retina, Optic Nerve, and Visual Pathway (Ophthalmology Monographs). 2nd ed. Oxford University Press, USA, 2003.

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1943-, Fishman Gerald Allen, ed. Electrophysiologic testing in disorders of the retina, optic nerve, and visual pathway. 2nd ed. San Francisco, CA: Foundation of the American Academy of Ophthalmology, 2001.

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Lam, Byron L. Electrophysiology of Vision: Clinical Testing and Applications. Taylor & Francis Group, 2005.

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Lam, Byron L. Electrophysiology of Vision: Clinical Testing and Applications. Taylor & Francis Group, 2005.

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Miyake, Yozo. Electrodiagnosis of Retinal Disease. Springer, 2005.

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Book chapters on the topic "Electrooculography"

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Tsang, Stephen H., and Tarun Sharma. "Electrooculography." In Advances in Experimental Medicine and Biology, 21–22. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95046-4_6.

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Creel, Donnell, and Minzhong Yu. "Electrooculography." In Handbook of Clinical Electrophysiology of Vision, 29–33. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30417-1_3.

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Reda, Radwa, Manal Tantawi, Howida Shedeed, and Mohamed F. Tolba. "Eye Movements Recognition Using Electrooculography Signals." In Advances in Intelligent Systems and Computing, 490–500. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44289-7_46.

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Zhang, Jinhua, Baozeng Wang, Jun Hong, Ting Li, and Feng Guo. "Human Manipulator Shared Online Control Using Electrooculography." In Intelligent Robotics and Applications, 278–87. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13966-1_28.

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Reda, Radwa, Manal Tantawi, Howida shedeed, and Mohamed F. Tolba. "Analyzing Electrooculography (EOG) for Eye Movement Detection." In Advances in Intelligent Systems and Computing, 179–89. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14118-9_18.

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Malaekah, E., H. Abdullah, and D. Cvetkovic. "Automatic Sleep Stage Detection Based on Electrooculography." In IFMBE Proceedings, 193–97. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0266-3_40.

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Du, Ruo-Fei, Ren-Jie Liu, Tian-Xiang Wu, and Bao-Liang Lu. "Online Vigilance Analysis Combining Video and Electrooculography Features." In Neural Information Processing, 447–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34500-5_53.

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Trzepacz, Maja, Przemysław Łagodziński, and Marcin Grzegorzek. "Electrooculography Application in Vision Therapy Using Smart Glasses." In Advances in Intelligent Systems and Computing, 103–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23762-2_10.

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Le, Toan, Ngu Bui, and Trung Le. "Electrooculography Feature Extraction Techniques for Classification of Eye Movements." In IFMBE Proceedings, 537–41. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5859-3_93.

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Zhao, Huimin, Chao Chen, Abdelkader Nasreddine Belkacem, Jiaxin Zhang, Lin Lu, and Penghai Li. "An Improved Method for Removing the Artifacts of Electrooculography." In Proceedings of the 11th International Conference on Computer Engineering and Networks, 516–25. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6554-7_57.

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Conference papers on the topic "Electrooculography"

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Ka Hou, Hoi, and Smitha K.G. "Low-Cost Wireless Electrooculography Speller." In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018. http://dx.doi.org/10.1109/smc.2018.00032.

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Zheng-Ping Wei and Bao-Liang Lu. "Online vigilance analysis based on electrooculography." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252594.

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Tabkara, Musa, Fatma Latifoglu, Kubra Ceran, Seyit Hamza Cavga, Muhammed Yaprak, and Cigdem Guluzar Altintop. "Electrooculography based font and punto analysis." In 2017 Medical Technologies National Congress (TIPTEKNO). IEEE, 2017. http://dx.doi.org/10.1109/tiptekno.2017.8238082.

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Duque, Carlos, Marco A. Ribeiro, and Nuno Souto. "Multiclass electrooculography using common spatial pattern." In 2012 35th International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2012. http://dx.doi.org/10.1109/tsp.2012.6256367.

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Kuo, Chung-Hsien, Yi-Chang Chan, Hung-Chyun Chou, and Jia-Wun Siao. "Eyeglasses based electrooculography human-wheelchair interface." In 2009 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2009. http://dx.doi.org/10.1109/icsmc.2009.5346087.

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Kuntal, Konica, Indrajit Banerjee, and Prabha P. Lakshmi. "Design of Wheelchair based on Electrooculography." In 2020 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2020. http://dx.doi.org/10.1109/iccsp48568.2020.9182157.

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Vyalkina, A. A., O. V. Korobeynikova, and V. A. Morozov. "Usage of combined electrooculography system in optometry." In 2009 32nd International Spring Seminar on Electronics Technology (ISSE). IEEE, 2009. http://dx.doi.org/10.1109/isse.2009.5206965.

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Ma, Zheren, Brandon C. Li, and Zeyu Yan. "Wearable driver drowsiness detection using electrooculography signal." In 2016 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet). IEEE, 2016. http://dx.doi.org/10.1109/wisnet.2016.7444317.

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Peter, Lukas, Barbora Janoscova, Antonino Proto, and Martin Cerny. "Electrooculography as a Tool For Managing Application." In 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom). IEEE, 2018. http://dx.doi.org/10.1109/healthcom.2018.8531178.

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Zou, Jiadao, and Qingxue Zhang. "eyeSay: Eye Electrooculography Decoding with Deep Learning." In 2021 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2021. http://dx.doi.org/10.1109/icce50685.2021.9427710.

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Reports on the topic "Electrooculography"

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Stern, John A., Robert Goldstein, and Douglas N. Dunham. An Evaluation of Electrooculographic, Head Movement and Steady State Evoked Response Measures of Workload in Flight Simulation (U). Fort Belvoir, VA: Defense Technical Information Center, November 1988. http://dx.doi.org/10.21236/ada236505.

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