Literatura académica sobre el tema "Electrooculogram"
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Artículos de revistas sobre el tema "Electrooculogram"
Liu, Jun, Guozheng Wang, Zhen-gang Cao, Fan Hang, Kangli Dong y Delin Zhang. "The depth of anesthesia under sevoflurane can be monitored based on calling names language paradigm and Ag/AgCl electrooculogram electrode". Materials Express 12, n.º 10 (1 de octubre de 2022): 1315–22. http://dx.doi.org/10.1166/mex.2022.2269.
Texto completoLi, Shuai, Dongmei Hao, Bing Liu, Zhijie Yin, Lin Yang y Jie Yu. "Evaluation of eyestrain with vertical electrooculogram". Computer Methods and Programs in Biomedicine 208 (septiembre de 2021): 106171. http://dx.doi.org/10.1016/j.cmpb.2021.106171.
Texto completoOhn, Young-Hoon, Osamu Katsumi, Edilson Kruger-Leite, Elizabeth W. Larson y Tatsuo Hirose. "Electrooculogram in Central Retinal Vein Obstruction". Ophthalmologica 203, n.º 4 (1991): 189–95. http://dx.doi.org/10.1159/000310251.
Texto completoLee, Kwang-Ryeol, Won-Du Chang, Sungkean Kim y Chang-Hwan Im. "Real-Time “Eye-Writing” Recognition Using Electrooculogram". IEEE Transactions on Neural Systems and Rehabilitation Engineering 25, n.º 1 (enero de 2017): 37–48. http://dx.doi.org/10.1109/tnsre.2016.2542524.
Texto completoEconomou, Stratis G. y Costas N. Stefanis. "Changes of electrooculogram (EOG) in Parkinson's disease". Acta Neurologica Scandinavica 58, n.º 1 (29 de enero de 2009): 44–52. http://dx.doi.org/10.1111/j.1600-0404.1978.tb02858.x.
Texto completoNakanishi, Masaki, Yasue Mitsukura, Yijun Wang, Yu-Te Wang y Tzyy-Ping Jung. "Online Voluntary Eye Blink Detection using Electrooculogram". IEICE Proceeding Series 1 (17 de marzo de 2014): 114–17. http://dx.doi.org/10.15248/proc.1.114.
Texto completoBanerjee, Anwesha, Shreyasi Datta, Monalisa Pal, Amit Konar, D. N. Tibarewala y R. Janarthanan. "Classifying Electrooculogram to Detect Directional Eye Movements". Procedia Technology 10 (2013): 67–75. http://dx.doi.org/10.1016/j.protcy.2013.12.338.
Texto completoRiemslag, F. C. C., H. F. E. Verduyn Lunel y H. Spekreijse. "The electrooculogram: A refinement of the method". Documenta Ophthalmologica 73, n.º 4 (diciembre de 1989): 369–75. http://dx.doi.org/10.1007/bf00154492.
Texto completoD’Souza, Sandra y N. Sriraam. "Statistical Based Analysis of Electrooculogram (EOG) Signals". International Journal of Biomedical and Clinical Engineering 2, n.º 1 (enero de 2013): 12–25. http://dx.doi.org/10.4018/ijbce.2013010102.
Texto completoFricke, Kyle, Robert Sobot y Anestis Dounavis. "Analogue portable electrooculogram real-time signal processor". International Journal of Circuit Theory and Applications 42, n.º 2 (21 de septiembre de 2012): 195–208. http://dx.doi.org/10.1002/cta.1848.
Texto completoTesis sobre el tema "Electrooculogram"
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.
Texto completoCoughlin, Michael J. y 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.
Texto completoCoughlin, Michael J. "Calibration of Two Dimensional Saccadic Electro-Oculograms Using Artificial Neural Networks". Thesis, Griffith University, 2003. http://hdl.handle.net/10072/365854.
Texto completoThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Applied Psychology
Griffith Health
Full Text
DERCHI, CHIARA CAMILLA. "BEHIND AN EYE BLINK: A NEW EMPIRICAL PERSPECTIVE ON INTENTIONAL ACTION". Doctoral thesis, Università degli Studi di Milano, 2018. http://hdl.handle.net/2434/555411.
Texto completoBlinking is a rapid closing and opening of the eyelid. Eye blinks with identical kinematical features can have different origins and meanings. For example, one can blink automatically, due to a simple reflex arc – such as when moistening the cornea – or one can blink voluntarily to communicate a fundamental message – such as when a locked-in patient communicates that he/she is happy or frustrated (Laureys, et al., 2005) The main aim of the present project is to find a brain-based objective way to know whether a given blink is a meaningless automatic neural event or the endpoint of a complex conscious process. The proposal builds up on the empirical work by Kornhuber & Deecke and Benjamin Libet, who showed that the awareness of intention to move is preceded by a recordable cerebral activity called “Readiness Potential”. The present proposal is relevant for two reasons: 1. In healthy subjects, automatic blinking occurs spontaneously every 5 seconds, or so. At the same time, healthy subjects can be instructed to blink voluntarily in a controlled fashion. In this way, blinking offers the ideal contrast between unconscious and conscious acts – the physical, kinematic aspects of the movement being equal. In this perspective, analyzing brain activity prior to automatic and voluntary blinks may offer a unique insight on the neural correlates of a conscious act. 2. In patients with severe brain injuries blinking is often the only motor act that can be reliably detected. By employing operant conditioning, we aim at training patients on the association between a specific eyelid closure and a positive reinforcement. Specifically, Readiness Potential like activity will be computed on the cortical activity preceding eye blinking as a measure of “volition,” first in healthy controls and then in vegetative and minimally conscious state patients undergoing operant conditioning. In healthy controls, we will contrast spontaneous blinks against voluntary blinks. The results of this experiment are meant to explore the dynamic range of the changes in brain activity that underlies voluntary vs. spontaneous blinks in controlled conditions. In patients, detecting a progressive increase in the strength or complexity of brain activity (up to the levels obtained in healthy subjects during voluntary blinks) during the course of the conditioning sessions will indicate that their blinking might reflect a voluntary act. Ultimately, this project, if successful, will link operant conditioning to the long-standing topic of the neural substrates of a wilful decision to act, bearing important scientific/ethical implications. The novelty of this project rests on: a. Exploring, empirically, the relationships between brain activity and the will. The underlying hypothesis guiding this project is that a wilful act should be reflected, to some measurable degree, in high levels of anticipatory brain dynamics. b. Taking Libet’s work one-step forward, by using slow cortical potentials such as the “Readiness Potential” as a neural marker of volition. c. Using the “Readiness Potential” to distinguish between spontaneous and voluntary blinks. d. Answering the critical question of whether the blinks produced by vegetative patients after a conditioning protocol are voluntary or not.
Young, Chieh-neng y 楊傑能. "Electrooculogram Signals for the Detection of REM Sleep Via VQ Methods". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/h372fr.
Texto completo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.
Chen, Hsiaw-Shuw y 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.
Texto completo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.
Libros sobre el tema "Electrooculogram"
Butkov, Nic. Polysomnography. Editado por Sudhansu Chokroverty, Luigi Ferini-Strambi y Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0007.
Texto completoCapítulos de libros sobre el tema "Electrooculogram"
Zayit-Soudry, Shiri y Ido Perlman. "Electrooculogram". En Encyclopedia of Ophthalmology, 1–3. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-35951-4_1033-1.
Texto completoZayit-Soudry, Shiri y Ido Perlman. "Electrooculogram". En Encyclopedia of Ophthalmology, 705–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-540-69000-9_1033.
Texto completoYang, Fumeng y Bin Xia. "Single Electrooculogram Channel-Based Sleep Stage Classification". En Advances in Cognitive Neurodynamics (V), 595–600. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0207-6_80.
Texto completoBanerjee, Anwesha, Shreyasi Datta, Amit Konar, D. N. Tibarewala y Janarthanan Ramadoss. "Cognitive Activity Recognition Based on Electrooculogram Analysis". En Smart Innovation, Systems and Technologies, 637–44. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07353-8_73.
Texto completoSandra, D’Souza y N. Sriraam. "Feature Based Reading Skill Analysis Using Electrooculogram Signals". En Advanced Computing and Communication Technologies, 233–44. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1023-1_24.
Texto completoMedeiros, Romeu, Ana Cláudia S. Souza y Gustavo F. Rodrigues. "Mouse Control Interface Using Electrooculogram and Genetic Programming". En XXVI Brazilian Congress on Biomedical Engineering, 335–39. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-2517-5_51.
Texto completoGoswami, Laxmi. "Human Computer Interface Using Electrooculogram as a Substitute". En 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.
Texto completoBorchardt, A. R., L. S. Schiavon, L. G. L. Silva, A. A. Souza Junior y M. G. Lucas. "Acquisition and Comparison of Classification Algorithms in Electrooculogram Signals". En 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.
Texto completoGondou, Kazuya, Hiroki Tamura y Koichi Tanno. "A Study on Human Interface for Communication Using Electrooculogram Signals". En 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.
Texto completoNing, Bo, Ming-jie Li, Tong Liu, Hui-min Shen, Liang Hu y Xin Fu. "Human Brain Control of Electric Wheelchair with Eye-Blink Electrooculogram Signal". En Intelligent Robotics and Applications, 579–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33509-9_58.
Texto completoActas de conferencias sobre el tema "Electrooculogram"
Datta, Shreyasi, Anwesha Banerjee, Amit Konar y D. N. Tibarewala. "Electrooculogram based cognitive context recognition". En 2014 International Conference on Electronics, Communication and Instrumentation (ICECI). IEEE, 2014. http://dx.doi.org/10.1109/iceci.2014.6767362.
Texto completoBrahmaiah, V. Priyanka, Y. Padma Sai y M. N. Giri Prasad. "Data Acquisition System of Electrooculogram". En 2017 IEEE 7th International Advance Computing Conference (IACC). IEEE, 2017. http://dx.doi.org/10.1109/iacc.2017.0149.
Texto completoAtique, Md Moin Uddin, Sakhawat Hossen Rakib y Khondkar Siddique-e-Rabbani. "An electrooculogram based control system". En 2016 International Conference on Informatics, Electronics and Vision (ICIEV). IEEE, 2016. http://dx.doi.org/10.1109/iciev.2016.7760113.
Texto completoAlquran, Hiam, Ali Mohammad Alqudah, Isam Abu Qasmieh y Sami Almashaqbeh. "Gaussian Model of Electrooculogram Signals". En 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). IEEE, 2019. http://dx.doi.org/10.1109/jeeit.2019.8717499.
Texto completoTrikha, Mrinal, Tapan Gandhi, Ayush Bhandari y Vijay Khare. "Multiple Channel Electrooculogram Classification using Automata". En 2007 IEEE International Workshop on Medical Measurement and Applications. IEEE, 2007. http://dx.doi.org/10.1109/memea.2007.4285158.
Texto completoMalaekah, Emad, Chanakya Reddy Patti y Dean Cvetkovic. "Automatic sleep-wake detection using electrooculogram signals". En 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 2014. http://dx.doi.org/10.1109/iecbes.2014.7047603.
Texto completoRosa, Andrei, Virgı́nia Bordignon, Carla Becker y Sergio Almeida. "A New Approach for Electrooculogram Recognition Algorithms". En XXXV Simpósio Brasileiro de Telecomunicações e Processamento de Sinais. Sociedade Brasileira de Telecomunicações, 2017. http://dx.doi.org/10.14209/sbrt.2017.40.
Texto completoKim-Tien, Nguyen y Nguyen Truong-Thinh. "Using Electrooculogram and Electromyogram for powered wheelchair". En 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2011. http://dx.doi.org/10.1109/robio.2011.6181515.
Texto completoBardhan, Jayetri, P. Suma y M. Jyothirmayi. "Motorized wheelchair control using electrooculogram and head gear". En 2016 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2016. http://dx.doi.org/10.1109/inventive.2016.7830190.
Texto completoGaliana-Merino, Juan Jose, Daniel Ruiz-Fernandez y Agustin Jarones-Gonzalez. "Electrooculogram filtering using wavelet and wavelet packet transforms". En 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013. http://dx.doi.org/10.1109/embc.2013.6609680.
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