Relevant bibliographies by topics / Electro-oculogram

Academic literature on the topic 'Electro-oculogram'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2023

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Journal articles on the topic "Electro-oculogram"

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Arden, Geoffrey B., and Paul A. Constable. "The electro-oculogram." Progress in Retinal and Eye Research 25, no. 2 (March 2006): 207–48. http://dx.doi.org/10.1016/j.preteyeres.2005.11.001.

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FRANCOIS, J., A. ROUCK, C. JÖNSAS, and D. FERNANDEZ-SASSO. "THE RABBIT'S ELECTRO-OCULOGRAM." Acta Ophthalmologica 47, no. 4 (May 27, 2009): 984–90. http://dx.doi.org/10.1111/j.1755-3768.1969.tb03721.x.

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PINCKERS, A., and J. M. THIJSSEN. "FLAT TYPE ELECTRO-OCULOGRAM (EOG)." Acta Ophthalmologica 52, no. 4 (May 27, 2009): 429–40. http://dx.doi.org/10.1111/j.1755-3768.1974.tb01754.x.

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LITH, G. H. M., and J. BALIK. "VARIABILITY OF THE ELECTRO-OCULOGRAM (EOG)." Acta Ophthalmologica 48, no. 6 (May 27, 2009): 1091–96. http://dx.doi.org/10.1111/j.1755-3768.1970.tb06588.x.

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THIJSSEN, J. M., and A. PINCKERS. "CONTRALATERAL EFFECTS IN THE ELECTRO-OCULOGRAM." Acta Ophthalmologica 52, no. 4 (May 27, 2009): 441–54. http://dx.doi.org/10.1111/j.1755-3768.1974.tb01755.x.

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Lessel, Manfred R., Arnulf Thaler, Viktor Scheiber, and Peter Heilig. "Comparison of Electro-Oculogram Recording Methods." Ophthalmic Research 25, no. 4 (1993): 245–52. http://dx.doi.org/10.1159/000267320.

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Brink, H. M. A., A. J. L. G. Pinckers, and A. M. Verbeek. "The electro-oculogram in uveal melanoma." Documenta Ophthalmologica 75, no. 3-4 (October 1990): 329–34. http://dx.doi.org/10.1007/bf00164847.

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Gosbell, Andrew D., William R. Barry, Ian Favilla, and Guy Burkitt. "Computer-assisted analysis of the electro-oculogram." Australian and New Zealand Journal of Ophthalmology 19, no. 4 (November 1991): 335–41. http://dx.doi.org/10.1111/j.1442-9071.1991.tb00682.x.

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ADAMS, ALISTAIR. "THE NORMAL ELECTRO-OCULOGRAM (E. O. G.)." Acta Ophthalmologica 51, no. 4 (May 27, 2009): 551–61. http://dx.doi.org/10.1111/j.1755-3768.1973.tb06034.x.

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Neubauer, A. S. "Detecting chloroquine retinopathy: electro-oculogram versus colour vision." British Journal of Ophthalmology 87, no. 7 (July 1, 2003): 902–8. http://dx.doi.org/10.1136/bjo.87.7.902.

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Dissertations / Theses on the topic "Electro-oculogram"

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Corrales, Paucar L. A. "Generation of ASCII characters by eye movements." Thesis, University of Strathclyde, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382352.

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Constable, Paul Alexander. "Investigations into the underlying cellular mechanism of the alcohol-electro-oculogram." Thesis, City University London, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.441428.

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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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Lal, Saroj Kusum Lata. "The psychophysiology of driver fatigue/drowsiness : electroencephalography, electro-oculogram, electrocardiogram and psychological effects." 2001. http://hdl.handle.net/2100/1031.

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University of Technology, Sydney. Faculty of Science.
Driver fatigue is a major cause of road accidents and has implications for road safety. Investigating the psychophysiological links to fatigue can enhance our understanding and management of fatigue in the transport industry. A variety of psychophysiological parameters have been identified as indicators of fatigue, with electroencephalography (EEG) perhaps being the most promising. Therefore, monitoring EEG during driver fatigue may be a promising variable for use in fatigue countermeasure devices. However, most previous fatigue-based studies have suffered from methodological shortcomings such as insufficient sample numbers, lack of a controlled testing environment, inadequate study design and statistical analysis. Furthermore, a thorough psychophysiological assessment of fatigue was found to be lacking in the literature. Therefore, the aims of the present doctoral research were to: 1) Assess the EEG and electro-occulogram (EOG) changes during driver fatigue in a 'state of the art' experimentally controlled study. 2) Identify psychological associations with fatigue. 3) Assess the changes in autonomic nervous system activity during fatigue. 4) Investigate the differences in the physiological changes that occur during fatigue in professional versus non-professional drivers. 5) Identify the reproducibility of physiological changes that occur during fatigue. 6) Examine the changes in EEG coherence during fatigue. 7) Utilise the physiological findings in this research for the development of EEG based software to detect fatigue. The results showed significant increases in delta and theta during driver fatigue. The conventional high amplitude blinks during alertness was replaced with slow, low amplitude blinks during fatigue. Reduced Fatigue-Inertia and decreased VigourActivity (which are mood sub-scales) and increased anxiety levels were associated with fatigue. There was an increase in parasympathetic activity during fatigue. Nonprofessional drivers showed greater increases in the EEG of fatigue compared to professional drivers. The EEG changes associated with fatigue were shown to be reproducible. The changes in EEG coherence were not found to be significant during fatigue. The EEG changes during fatigue were used for the development of an algorithm for a fatigue-countermeasure device and was shown to reliably detect fatigue. In summary, this research has provided important information on the psychophysiology of driver fatigue clarifying some of the findings of prior research. Significant changes were found to occur in EEG, EOG and parasympathetic activity during fatigue. From this research it may also be suggested that psychological status of the driver may influence fatigue status. Furthermore, the EEG changes during fatigue are consistent and reliable, which can be utilised to detect fatigue in a EEG-based fatigue countermeasure device. The results are discussed in the light of direction for future driver fatigue studies and fatigue management.
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Book chapters on the topic "Electro-oculogram"

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Banerjee, Anwesha, Pratyusha Das, Shounak Datta, Amit Konar, R. Janarthanan, and D. N. Tibarewala. "Real Time Electro-Oculogram Driven Rehabilitation Aid." In Advances in Intelligent Systems and Computing, 435–40. New Delhi: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-0740-5_53.

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Bhandari, Ayush, Vijay Khare, Jayashree Santhosh, and Sneh Anand. "Wavelet based compression technique of Electro-oculogram signals." In 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006, 440–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-68017-8_111.

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Banerjee, Anwesha, Amit Konar, R. Janarthana, and D. N. Tibarewala. "Electro-oculogram Based Classification of Eye Movement Direction." In Advances in Computing and Information Technology, 151–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31600-5_15.

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Rasheed, Tahir, Myung Ho In, Young-Koo Lee, Sungyoung Lee, Soo Yeol Lee, and Tae-Seong Kim. "Constrained ICA Based Ballistocardiogram and Electro-Oculogram Artifacts Removal from Visual Evoked Potential EEG Signals Measured Inside MRI." In Neural Information Processing, 1088–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893028_121.

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"Electro-oculogram." In Electrophysiology of Vision, 105–21. CRC Press, 2005. http://dx.doi.org/10.1201/b14209-5.

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Pandey, Suresh, and Brighu Swamy. "Electroretinogram and Electro-oculogram." In Diagnostic and Imaging Techniques in Ophthalmology, 175. Jaypee Brothers Medical Publishers (P) Ltd., 2009. http://dx.doi.org/10.5005/jp/books/11236_13.

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"Origin and Significance of the Electro-Oculogram." In Principles and Practice of Clinical Electrophysiology of Vision. The MIT Press, 2006. http://dx.doi.org/10.7551/mitpress/5557.003.0018.

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Conference papers on the topic "Electro-oculogram"

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Gandhi, T., M. Trikha, J. Santosh, and S. Anand. "VHDL Based Electro-Oculogram Signal Classification." In 15th International Conference on Advanced Computing and Communications (ADCOM 2007). IEEE, 2007. http://dx.doi.org/10.1109/adcom.2007.129.

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Ghasad, Preeti P. "Design and Implementation of Electro-Oculogram Based Brain-Computer-Interaction." In 2016 8th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2016. http://dx.doi.org/10.1109/cicn.2016.131.

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Garcia-Molina, Gary, F. Abtahi, and M. Lagares-Lemos. "Automated NREM sleep staging using the Electro-oculogram: A pilot study." In 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6346411.

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Bhandari, Ayush, Vijay Khare, Mrinal Trikha, and Sneh Anand. "Wavelet based Novel Technique for Signal Conditioning of Electro-Oculogram Signals." In 2006 Annual IEEE India Conference. IEEE, 2006. http://dx.doi.org/10.1109/indcon.2006.302851.

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Yao, Kai-chao, and Der-fa Chen. "Establishment of New Electro-oculogram Measurement Technique by Integrating Virtual Instrument." In 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.280.

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Banerjee, A., A. Konar, D. N. Tibarewala, and R. Janarthanan. "Detecting eye movement direction from stimulated Electro-oculogram by intelligent algorithms." In 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT 2012). IEEE, 2012. http://dx.doi.org/10.1109/icccnt.2012.6395881.

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Swami, Piyush, and Tapan K. Gandhi. "Assistive communication system for speech disabled patients based on electro-oculogram character recognition." In 2014 International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2014. http://dx.doi.org/10.1109/indiacom.2014.6828162.

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ElMessidi, Mohamed, Sana Tmar-Ben Hamida, Beena Ahmed, and Thomas Penzel. "Accurate automatic identification of slow wave sleep using a single electro-oculogram channel." In 2014 Middle East Conference on Biomedical Engineering (MECBME). IEEE, 2014. http://dx.doi.org/10.1109/mecbme.2014.6783247.

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Rakshit, Arnab, Anwesha Banerjee, and D. N. Tibarewala. "Electro-oculogram based digit recognition to design assitive communication system for speech disabled patients." In 2016 International Conference on Microelectronics, Computing and Communications (MicroCom). IEEE, 2016. http://dx.doi.org/10.1109/microcom.2016.7522560.

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"A Blind Source Separation Algorithm for the Processing and Classification of Electro-oculogram Data." In The Fifth International Workshop on Artificial Neural Networks and Intelligent Information Processing. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0002263200500056.

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