Literatura académica sobre el tema "Steady-State Visually Evoked Potential (SSVEP)"
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Artículos de revistas sobre el tema "Steady-State Visually Evoked Potential (SSVEP)"
Salazar, Sophia, Femi Oyewole, Ted Obi, Rebecca Baron, Dylan Mahony, Anna Kropelnicki, Adrian Cohen, David Putrino y Adam Fry. "Steady-state visual evoked potentials are unchanged following physical and cognitive exertion paradigms". Journal of Concussion 5 (enero de 2021): 205970022110553. http://dx.doi.org/10.1177/20597002211055346.
Texto completoIkeda, Akira y Yoshikazu Washizawa. "Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks". Sensors 21, n.º 16 (6 de agosto de 2021): 5309. http://dx.doi.org/10.3390/s21165309.
Texto completoChen, Jing, Matteo Valsecchi y Karl R. Gegenfurtner. "Saccadic suppression measured by steady-state visual evoked potentials". Journal of Neurophysiology 122, n.º 1 (1 de julio de 2019): 251–58. http://dx.doi.org/10.1152/jn.00712.2018.
Texto completoOlze, Katharina, Christof Jan Wehrmann, Luyang Mu y Meinhard Schilling. "Obstacles in using a computer screen for steady-state visually evoked potential stimulation". Biomedical Engineering / Biomedizinische Technik 63, n.º 4 (26 de julio de 2018): 377–82. http://dx.doi.org/10.1515/bmt-2016-0243.
Texto completoZhang, Shangen y Xiaogang Chen. "Effect of background luminance of visual stimulus on elicited steady-state visual evoked potentials". Brain Science Advances 8, n.º 1 (marzo de 2022): 50–56. http://dx.doi.org/10.26599/bsa.2022.9050006.
Texto completoKrishnappa, Manjula y Madaveeranahally Boregowda Anandaraju. "Adaptive filters based efficient EEG classification for steady state visually evoked potential based BCI system". International Journal of Reconfigurable and Embedded Systems (IJRES) 12, n.º 2 (1 de julio de 2023): 215. http://dx.doi.org/10.11591/ijres.v12.i2.pp215-221.
Texto completoGao, Shouwei, Kang Zhou, Jun Zhang, Yi Cheng y Shujun Mao. "Effects of Background Music on Mental Fatigue in Steady-State Visually Evoked Potential-Based BCIs". Healthcare 11, n.º 7 (2 de abril de 2023): 1014. http://dx.doi.org/10.3390/healthcare11071014.
Texto completoAdam, Kirsten C. S., Lillian Chang, Nicole Rangan y John T. Serences. "Steady-State Visually Evoked Potentials and Feature-based Attention: Preregistered Null Results and a Focused Review of Methodological Considerations". Journal of Cognitive Neuroscience 33, n.º 4 (abril de 2021): 695–724. http://dx.doi.org/10.1162/jocn_a_01665.
Texto completoLiu, Siyu, Deyu Zhang, Ziyu Liu, Mengzhen Liu, Zhiyuan Ming, Tiantian Liu, Dingjie Suo, Shintaro Funahashi y Tianyi Yan. "Review of brain–computer interface based on steady‐state visual evoked potential". Brain Science Advances 8, n.º 4 (30 de noviembre de 2022): 258–75. http://dx.doi.org/10.26599/bsa.2022.9050022.
Texto completoLin, Bor-Shyh, Bor-Shing Lin, Tzu-Hsiang Yen, Chien-Chin Hsu y Yao-Chin Wang. "Design of Wearable Headset with Steady State Visually Evoked Potential-Based Brain Computer Interface". Micromachines 10, n.º 10 (10 de octubre de 2019): 681. http://dx.doi.org/10.3390/mi10100681.
Texto completoTesis sobre el tema "Steady-State Visually Evoked Potential (SSVEP)"
Calore, E. "TOWARDS STEADY-STATE VISUALLY EVOKED POTENTIALS BRAIN-COMPUTER INTERFACES FOR VIRTUAL REALITY ENVIRONMENTS EXPLICIT AND IMPLICIT INTERACTION". Doctoral thesis, Università degli Studi di Milano, 2014. http://hdl.handle.net/2434/233319.
Texto completoRaza, Asim. "SSVEP based EEG Interface for Google Street View Navigation". Thesis, Linköpings universitet, Medie- och Informationsteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-104276.
Texto completoLine, Per y pline@swin edu au. "Cognition and the steady state visually evoked potential". Swinburne University of Technology, 1993. http://adt.lib.swin.edu.au./public/adt-VSWT20060118.170228.
Texto completoPipingas, Andrew y apipingas@bsi swin edu au. "Steady-state visually evoked potential correlates of object recognition memory". Swinburne University of Technology, 2003. http://adt.lib.swin.edu.au./public/adt-VSWT20050322.171342.
Texto completoSteedman, David John. "Simultaneous measurement of human brain activity using near infra-red spectroscopy, electroencephalogram and the steady state visually evoked potential". Swinburne Research Bank, 2008. http://hdl.handle.net/1959.3/48535.
Texto completoA thesis submitted for M.Sc by Research, Brain Sciences Institute, Swinburne University of Technology - 2008. Typescript. Includes bibliographical references (p. 117-153)
Talevi, Luca. "Sviluppo e test di un sistema BCI SSVEP-based". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11636/.
Texto completoSi, Mohammed Hakim. "Design and Study of Interactive Systems based on Brain- Computer Interfaces and Augmented Reality". Thesis, Rennes, INSA, 2019. http://www.theses.fr/2019ISAR0024.
Texto completoBrain-Computer Interfaces (BCI) enable interaction directly from brain activity. Augmented Reality (AR) on the other hand, enables the integration of virtual elements in the real world. In this thesis, we aimed at designing interactive systems associating BCIs and AR, to offer new means of hands-free interaction with real and virtual elements. In the first part, we have studied the possibility to extract different BCI paradigms in AR. We have shown that it was possible to use Steady-State Visual Evoked Potentials (SSVEP) in AR. Then, we have studied the possibility to extract Error-Related Potentials (ErrPs) in AR, showing that ErrPs were elicited in users facing errors, often occurring in AR. In the second part, we have deepened our research in the use of SSVEP for direct interaction in AR. We have proposed HCCA, a new algorithm for self-paced detection of SSVEP responses. Then, we have studied the design of AR interfaces, for the development of intuitive and efficient interactive systems. Lastly, we have illustrated our results, through the development of a smart-home system combining SSVEP and AR, which integrates in a commercially available smart-home system
Gregori, Federica. "Sistemi di comunicazione alternativa basati su Brain Computer Interface: stato dell’arte e prospettive future". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/19912/.
Texto completoIbáñez, Soria David 1983. "Analysis of brain dynamics using echo-state networks". Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/663491.
Texto completoEn la última decada las redes neuronales recurrentes han revolucionado el campo de la inteligencia artificial. Sus conexiones cíclicas les proporcionan memoria y por tanto la capacidad de modelar problemas con contexto temporal. Las redes echo-state simplifican enormemente el diseño y entrenamiento de las redes recurrentes. En esta tesis exploramos el uso de redes echo-state y su aplicación en problemas de clasificación y detección de patrones en señales EEG. En un primer estudio demostramos que son capaces de detectar cambios de sincronización generalizada entre dos series temporales caóticas. En un segundo utilizamos redes echo-state para caracterizar la no estacionaridad de un fenómeno considerado de estado estable, potenciales visuales evocados steady-sate (SSVEP). Finalmente en un tercer estudio proponemos un nuevo biomarcardor para TDAH capaz de cuantificar cambios en la dinámica de la señal EEG entre condiciones bajas y normales de excitación. Los resultados aquí presentados demuestran la excelente capacidad de detección de patrones no estacionarios de estas redes, así como su aplicabilidad en el análisis de datos electrofisiológicos.
Sie, Jyun-jie y 謝竣傑. "Implementation of a high-performance steady-state visual evoked potential (SSVEP)-based brain computer interface using frequency and phase encoded flash lights". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/35804818538671355209.
Texto completo國立中央大學
電機工程研究所
95
The present study proposes a new visual evoked potential (VEP)-based brain computer interface (BCI). Users gaze at different spatially separated flash channels (FCs) in order to induce visual evoked signals that have temporal sequences corresponding to the gazed FCs, so that the gazed FC can be recognized and the command mapping to the gazed FC can be sent out to achieve control purposes. To achieve distinct flickering sequences among different FCs, we utilized different frequencies and phases to encode the flashing sequences of different FCs. The proposed system provides the high flexibility in expansion of FC number and high information transfer rate (ITR) which are superior to the traditional SSVEP-based and FVEP-based BCIs. In this thesis, we have built an eight-FC system. The command transfer rate and detected accuracy are 0.52 sec/command and 100%, respectively.
Libros sobre el tema "Steady-State Visually Evoked Potential (SSVEP)"
Prasad, Girijesh. Brain–machine interfaces. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0049.
Texto completoCapítulos de libros sobre el tema "Steady-State Visually Evoked Potential (SSVEP)"
Xu, Zhuo, Jie Li, Rong Gu y Bin Xia. "Steady-State Visually Evoked Potential (SSVEP)-Based Brain-Computer Interface (BCI): A Low-Delayed Asynchronous Wheelchair Control System". En Neural Information Processing, 305–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34475-6_37.
Texto completoKubacki, Arkadiusz y Andrzej Milecki. "Control of the 6-Axis Robot Using a Brain-Computer Interface Based on Steady State Visually Evoked Potential (SSVEP)". En Lecture Notes in Mechanical Engineering, 213–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18715-6_18.
Texto completoSegers, H., A. Combaz, N. V. Manyakov, N. Chumerin, K. Vanderperren, S. Van Huffel y M. M. Van Hulle. "Steady State Visual Evoked Potential (SSVEP) - Based Brain Spelling System with Synchronous and Asynchronous Typing Modes". En IFMBE Proceedings, 164–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21683-1_41.
Texto completoSayilgan, Ebru, Yilmaz Kemal Yuce y Yalcin Isler. "Evaluating Steady-State Visually Evoked Potentials-Based Brain-Computer Interface System Using Wavelet Features and Various Machine Learning Methods". En Artificial Intelligence. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98335.
Texto completoVerma, Akshat, Praveen Kumar Shukla, Shrish Verma y Rahul Kumar Chaurasiya. "A Frequency Discrimination Technique for SSVEP-Based BCIs Using Common Feature Analysis and Support Vector Machine". En Advances in Bioinformatics and Biomedical Engineering, 158–78. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3947-0.ch009.
Texto completoRaghuvanshi, Ankita, Mohit Sarin, Praveen Kumar Shukla, Shrish Verma y Rahul Kumar Chaurasiya. "A Comprehensive Review on a Brain Simulation Tool and Its Applications". En Advances in Bioinformatics and Biomedical Engineering, 26–51. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3947-0.ch002.
Texto completoActas de conferencias sobre el tema "Steady-State Visually Evoked Potential (SSVEP)"
Anil, Divya Geethakumari, Krupal Sureshbhai Mistry, Vaibhav Palande y Kiran George. "A Novel Steady-State Visually Evoked Potential (SSVEP) Based Brain Computer Interface Paradigm for Disabled Individuals". En 2017 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2017. http://dx.doi.org/10.1109/ichi.2017.19.
Texto completoAljshamee, Mustafa, Abbas Malekpour y Peter Luksch. "Multiple Frequency Effects on Human-Brain Based Steady-State Visual Evoked Potential (SSVEP)". En 2016 IEEE 6th International Conference on Advanced Computing (IACC). IEEE, 2016. http://dx.doi.org/10.1109/iacc.2016.139.
Texto completoMu, Jing, David B. Grayden, Ying Tan y Denny Oetomo. "Comparison of Steady-State Visual Evoked Potential (SSVEP) with LCD vs. LED Stimulation". En 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9175838.
Texto completoHyunmi Lim y Jeonghun Ku. "Mirror neuron system (MNS) activation and steady state visually evoked potential (SSVEP) evocation by flickering exercise video". En 2017 International Conference on Virtual Rehabilitation (ICVR). IEEE, 2017. http://dx.doi.org/10.1109/icvr.2017.8007473.
Texto completoLongbotham, Harold G. y Randolph D. Glickman. "Analysis of Visual Evoked Potential Using Generalized Order Statistic Filters". En Noninvasive Assessment of the Visual System. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/navs.1991.md20.
Texto completoHwang, Han-Jeong, Jeong-Hwan Lim, Jun-Hak Lee y Chang-Hwan Im. "Implementation of a mental spelling system based on steady-state visual evoked potential (SSVEP)". En 2013 International Winter Workshop on Brain-Computer Interface (BCI). IEEE, 2013. http://dx.doi.org/10.1109/iww-bci.2013.6506638.
Texto completoKubacki, Arkadiusz, Arkadiusz Jakubowski y Dominik Rybarczyk. "Research on possibilities of transporter movement using brain-computer interface based on Steady-State Visually Evoked Potential (SSVEP)". En 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR). IEEE, 2017. http://dx.doi.org/10.1109/mmar.2017.8046827.
Texto completoLeow, R. S., F. Ibrahim y M. Moghavvemi. "Development of a steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) system". En 2007 International Conference on Intelligent and Advanced Systems (ICIAS). IEEE, 2007. http://dx.doi.org/10.1109/icias.2007.4658399.
Texto completoWang, Shaocheng, Ehsan Tarkesh Esfahani y V. Sundararajan. "Evaluation of SSVEP as Passive Feedback for Improving the Performance of Brain Machine Interfaces". En ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71068.
Texto completoGiuliani, Henrique L. V., Patrick O. de Paula, Diogo C. Soriano, Ricardo Suyama y Denis G. Fantinato. "Ensemble Learning in BCI-SSVEP Systems for Short Window Lengths". En Escola Regional de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/ercas.2021.17438.
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