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Статті в журналах з теми "Steady-State Visually Evoked Potential (SSVEP)"
Salazar, Sophia, Femi Oyewole, Ted Obi, Rebecca Baron, Dylan Mahony, Anna Kropelnicki, Adrian Cohen, David Putrino, and Adam Fry. "Steady-state visual evoked potentials are unchanged following physical and cognitive exertion paradigms." Journal of Concussion 5 (January 2021): 205970022110553. http://dx.doi.org/10.1177/20597002211055346.
Повний текст джерелаIkeda, Akira, and Yoshikazu Washizawa. "Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks." Sensors 21, no. 16 (August 6, 2021): 5309. http://dx.doi.org/10.3390/s21165309.
Повний текст джерелаChen, Jing, Matteo Valsecchi, and Karl R. Gegenfurtner. "Saccadic suppression measured by steady-state visual evoked potentials." Journal of Neurophysiology 122, no. 1 (July 1, 2019): 251–58. http://dx.doi.org/10.1152/jn.00712.2018.
Повний текст джерелаOlze, Katharina, Christof Jan Wehrmann, Luyang Mu, and Meinhard Schilling. "Obstacles in using a computer screen for steady-state visually evoked potential stimulation." Biomedical Engineering / Biomedizinische Technik 63, no. 4 (July 26, 2018): 377–82. http://dx.doi.org/10.1515/bmt-2016-0243.
Повний текст джерелаZhang, Shangen, and Xiaogang Chen. "Effect of background luminance of visual stimulus on elicited steady-state visual evoked potentials." Brain Science Advances 8, no. 1 (March 2022): 50–56. http://dx.doi.org/10.26599/bsa.2022.9050006.
Повний текст джерелаKrishnappa, Manjula, and 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, no. 2 (July 1, 2023): 215. http://dx.doi.org/10.11591/ijres.v12.i2.pp215-221.
Повний текст джерелаGao, Shouwei, Kang Zhou, Jun Zhang, Yi Cheng, and Shujun Mao. "Effects of Background Music on Mental Fatigue in Steady-State Visually Evoked Potential-Based BCIs." Healthcare 11, no. 7 (April 2, 2023): 1014. http://dx.doi.org/10.3390/healthcare11071014.
Повний текст джерелаAdam, Kirsten C. S., Lillian Chang, Nicole Rangan, and 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, no. 4 (April 2021): 695–724. http://dx.doi.org/10.1162/jocn_a_01665.
Повний текст джерелаLiu, Siyu, Deyu Zhang, Ziyu Liu, Mengzhen Liu, Zhiyuan Ming, Tiantian Liu, Dingjie Suo, Shintaro Funahashi, and Tianyi Yan. "Review of brain–computer interface based on steady‐state visual evoked potential." Brain Science Advances 8, no. 4 (November 30, 2022): 258–75. http://dx.doi.org/10.26599/bsa.2022.9050022.
Повний текст джерелаLin, Bor-Shyh, Bor-Shing Lin, Tzu-Hsiang Yen, Chien-Chin Hsu, and Yao-Chin Wang. "Design of Wearable Headset with Steady State Visually Evoked Potential-Based Brain Computer Interface." Micromachines 10, no. 10 (October 10, 2019): 681. http://dx.doi.org/10.3390/mi10100681.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаRaza, 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.
Повний текст джерелаLine, Per, and 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.
Повний текст джерелаPipingas, Andrew, and 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.
Повний текст джерелаSteedman, 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.
Повний текст джерелаA 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/.
Повний текст джерелаSi, 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.
Повний текст джерелаBrain-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/.
Повний текст джерелаIbáñez, Soria David 1983. "Analysis of brain dynamics using echo-state networks." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/663491.
Повний текст джерелаEn 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, and 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.
Повний текст джерела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.
Книги з теми "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.
Повний текст джерелаЧастини книг з теми "Steady-State Visually Evoked Potential (SSVEP)"
Xu, Zhuo, Jie Li, Rong Gu, and Bin Xia. "Steady-State Visually Evoked Potential (SSVEP)-Based Brain-Computer Interface (BCI): A Low-Delayed Asynchronous Wheelchair Control System." In Neural Information Processing, 305–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34475-6_37.
Повний текст джерелаKubacki, Arkadiusz, and Andrzej Milecki. "Control of the 6-Axis Robot Using a Brain-Computer Interface Based on Steady State Visually Evoked Potential (SSVEP)." In Lecture Notes in Mechanical Engineering, 213–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18715-6_18.
Повний текст джерелаSegers, H., A. Combaz, N. V. Manyakov, N. Chumerin, K. Vanderperren, S. Van Huffel, and M. M. Van Hulle. "Steady State Visual Evoked Potential (SSVEP) - Based Brain Spelling System with Synchronous and Asynchronous Typing Modes." In IFMBE Proceedings, 164–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21683-1_41.
Повний текст джерелаSayilgan, Ebru, Yilmaz Kemal Yuce, and Yalcin Isler. "Evaluating Steady-State Visually Evoked Potentials-Based Brain-Computer Interface System Using Wavelet Features and Various Machine Learning Methods." In Artificial Intelligence. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98335.
Повний текст джерелаVerma, Akshat, Praveen Kumar Shukla, Shrish Verma, and Rahul Kumar Chaurasiya. "A Frequency Discrimination Technique for SSVEP-Based BCIs Using Common Feature Analysis and Support Vector Machine." In Advances in Bioinformatics and Biomedical Engineering, 158–78. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3947-0.ch009.
Повний текст джерелаRaghuvanshi, Ankita, Mohit Sarin, Praveen Kumar Shukla, Shrish Verma, and Rahul Kumar Chaurasiya. "A Comprehensive Review on a Brain Simulation Tool and Its Applications." In Advances in Bioinformatics and Biomedical Engineering, 26–51. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3947-0.ch002.
Повний текст джерелаТези доповідей конференцій з теми "Steady-State Visually Evoked Potential (SSVEP)"
Anil, Divya Geethakumari, Krupal Sureshbhai Mistry, Vaibhav Palande, and Kiran George. "A Novel Steady-State Visually Evoked Potential (SSVEP) Based Brain Computer Interface Paradigm for Disabled Individuals." In 2017 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2017. http://dx.doi.org/10.1109/ichi.2017.19.
Повний текст джерелаAljshamee, Mustafa, Abbas Malekpour, and Peter Luksch. "Multiple Frequency Effects on Human-Brain Based Steady-State Visual Evoked Potential (SSVEP)." In 2016 IEEE 6th International Conference on Advanced Computing (IACC). IEEE, 2016. http://dx.doi.org/10.1109/iacc.2016.139.
Повний текст джерелаMu, Jing, David B. Grayden, Ying Tan, and Denny Oetomo. "Comparison of Steady-State Visual Evoked Potential (SSVEP) with LCD vs. LED Stimulation." In 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.
Повний текст джерелаHyunmi Lim and Jeonghun Ku. "Mirror neuron system (MNS) activation and steady state visually evoked potential (SSVEP) evocation by flickering exercise video." In 2017 International Conference on Virtual Rehabilitation (ICVR). IEEE, 2017. http://dx.doi.org/10.1109/icvr.2017.8007473.
Повний текст джерелаLongbotham, Harold G., and Randolph D. Glickman. "Analysis of Visual Evoked Potential Using Generalized Order Statistic Filters." In Noninvasive Assessment of the Visual System. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/navs.1991.md20.
Повний текст джерелаHwang, Han-Jeong, Jeong-Hwan Lim, Jun-Hak Lee, and Chang-Hwan Im. "Implementation of a mental spelling system based on steady-state visual evoked potential (SSVEP)." In 2013 International Winter Workshop on Brain-Computer Interface (BCI). IEEE, 2013. http://dx.doi.org/10.1109/iww-bci.2013.6506638.
Повний текст джерелаKubacki, Arkadiusz, Arkadiusz Jakubowski, and Dominik Rybarczyk. "Research on possibilities of transporter movement using brain-computer interface based on Steady-State Visually Evoked Potential (SSVEP)." In 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR). IEEE, 2017. http://dx.doi.org/10.1109/mmar.2017.8046827.
Повний текст джерелаLeow, R. S., F. Ibrahim, and M. Moghavvemi. "Development of a steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) system." In 2007 International Conference on Intelligent and Advanced Systems (ICIAS). IEEE, 2007. http://dx.doi.org/10.1109/icias.2007.4658399.
Повний текст джерелаWang, Shaocheng, Ehsan Tarkesh Esfahani, and V. Sundararajan. "Evaluation of SSVEP as Passive Feedback for Improving the Performance of Brain Machine Interfaces." In 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.
Повний текст джерелаGiuliani, Henrique L. V., Patrick O. de Paula, Diogo C. Soriano, Ricardo Suyama, and Denis G. Fantinato. "Ensemble Learning in BCI-SSVEP Systems for Short Window Lengths." In Escola Regional de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/ercas.2021.17438.
Повний текст джерела