Literatura académica sobre el tema "Electrooculograms"
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Artículos de revistas sobre el tema "Electrooculograms"
Becerra-García, Roberto A., Rodolfo García-Bermúdez y Gonzalo Joya. "Differentiation of Saccadic Eye Movement Signals". Sensors 21, n.º 15 (24 de julio de 2021): 5021. http://dx.doi.org/10.3390/s21155021.
Texto completoChang, Won-Du. "Electrooculograms for Human–Computer Interaction: A Review". Sensors 19, n.º 12 (14 de junio de 2019): 2690. http://dx.doi.org/10.3390/s19122690.
Texto completoIshii, Chiharu, Shunsuke Murooka y Minato Tajima. "Navigation of an Electric Wheelchair Using Electromyograms, Electrooculograms, and Electroencephalograms". International Journal of Mechanical Engineering and Robotics Research 7, n.º 2 (2016): 143–49. http://dx.doi.org/10.18178/ijmerr.7.2.143-149.
Texto completoIanov, Alexsandr Igorevitch, Hiroaki Kawamoto y Yoshiyuki Sankai. "Development of Hybrid Resistive-Capacitive Electrodes for Electroencephalograms and Electrooculograms". IEEJ Transactions on Sensors and Micromachines 133, n.º 3 (2013): 57–65. http://dx.doi.org/10.1541/ieejsmas.133.57.
Texto completoDasgupta, Anirban y Aurobinda Routray. "Piecewise empirical mode Bayesian estimation – A new method to denoise electrooculograms". Biomedical Signal Processing and Control 70 (septiembre de 2021): 102945. http://dx.doi.org/10.1016/j.bspc.2021.102945.
Texto completoChang, Won-Du, Ho-Seung Cha y Chang-Hwan Im. "Removing the Interdependency between Horizontal and Vertical Eye-Movement Components in Electrooculograms". Sensors 16, n.º 2 (14 de febrero de 2016): 227. http://dx.doi.org/10.3390/s16020227.
Texto completoSasaki, Tatsuya, Kyouichi Suzuki, Masato Matsumoto, Taku Sato, Namio Kodama y 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, n.º 4 (octubre de 2002): 941–44. http://dx.doi.org/10.3171/jns.2002.97.4.0941.
Texto completoNoguchi, Kazuhito, Koichi Haishi y Daisuke Sato. "An Illusion of Velocity in Motion Perception". Perceptual and Motor Skills 78, n.º 1 (febrero de 1994): 112–14. http://dx.doi.org/10.2466/pms.1994.78.1.112.
Texto completoChakraborty, Suvodip, Anirban Dasgupta y Aurobinda Routray. "Localization of eye Saccadic signatures in Electrooculograms using sparse representations with data driven dictionaries". Pattern Recognition Letters 139 (noviembre de 2020): 104–11. http://dx.doi.org/10.1016/j.patrec.2017.11.001.
Texto completoLi, Hao, Xia Mao y Lijiang Chen. "An emotion classification method from electroencephalogram based on 1/f fluctuation theory". Measurement and Control 53, n.º 5-6 (24 de abril de 2020): 824–32. http://dx.doi.org/10.1177/0020294020913893.
Texto completoTesis sobre el tema "Electrooculograms"
Coughlin, 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
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 completoYoung, 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 "Electrooculograms"
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 "Electrooculograms"
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 "Electrooculograms"
Mishra, Saswat, Yongkuk Lee, Dong Sup Lee y Woon-Hong Yeo. "Fractal-Structured, Wearable Soft Sensors for Control of a Robotic Wheelchair via Electrooculograms". En 2017 IEEE 67th Electronic Components and Technology Conference (ECTC). IEEE, 2017. http://dx.doi.org/10.1109/ectc.2017.68.
Texto completoDatta, 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.
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