Relevant bibliographies by topics / SSCEP

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'SSCEP'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "SSCEP"

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Vulliamy, Graham, and James Carrier. "Sorcery and SSCEP: the cultural context of an educational innovation." British Journal of Sociology of Education 6, no. 1 (March 1985): 17–33. http://dx.doi.org/10.1080/0142569850060102.

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Weeks, Sheldon. "Education for village life? What has happened to grade ten leavers from the five SSCEP high schools?" International Journal of Educational Development 7, no. 1 (January 1987): 33–48. http://dx.doi.org/10.1016/0738-0593(87)90005-8.

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Punsawad, Yunyong, and Yodchanan Wongsawat. "Multi-command SSAEP-based BCI system with training sessions for SSVEP during an eye fatigue state." IEEJ Transactions on Electrical and Electronic Engineering 12 (June 2017): S72—S78. http://dx.doi.org/10.1002/tee.22441.

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Teng, Fei, Yixin Chen, Aik Min Choong, Scott Gustafson, Christopher Reichley, Pamela Lawhead, and Dwight Waddell. "Square or Sine: Finding a Waveform with High Success Rate of Eliciting SSVEP." Computational Intelligence and Neuroscience 2011 (2011): 1–5. http://dx.doi.org/10.1155/2011/364385.

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Steady state visual evoked potential (SSVEP) is the brain's natural electrical potential response for visual stimuli at specific frequencies. Using a visual stimulus flashing at some given frequency will entrain the SSVEP at the same frequency, thereby allowing determination of the subject's visual focus. The faster an SSVEP is identified, the higher information transmission rate the system achieves. Thus, an effective stimulus, defined as one with high success rate of eliciting SSVEP and high signal-noise ratio, is desired. Also, researchers observed that harmonic frequencies often appear in the SSVEP at a reduced magnitude. Are the harmonics in the SSVEP elicited by the fundamental stimulating frequency or by the artifacts of the stimuli? In this paper, we compare the SSVEP responses of three periodic stimuli: square wave (with different duty cycles), triangle wave, and sine wave to find an effective stimulus. We also demonstrate the connection between the strength of the harmonics in SSVEP and the type of stimulus.
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Wang, Xiaoqing. "Stochastic Separated Continuous Conic Programming: Strong Duality and a Solution Method." Mathematical Problems in Engineering 2014 (2014): 1–20. http://dx.doi.org/10.1155/2014/896591.

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We study a new class of optimization problems calledstochastic separated continuous conic programming(SSCCP). SSCCP is an extension to the optimization model calledseparated continuous conic programming(SCCP) which has applications in robust optimization and sign-constrained linear-quadratic control. Based on the relationship among SSCCP, its dual, and their discretization counterparts, we develop a strong duality theory for the SSCCP. We also suggest a polynomial-time approximation algorithm that solves the SSCCP to any predefined accuracy.
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Chang, Min Hye, Jeong Su Lee, Jeong Heo, and Kwang Suk Park. "Eliciting dual-frequency SSVEP using a hybrid SSVEP-P300 BCI." Journal of Neuroscience Methods 258 (January 2016): 104–13. http://dx.doi.org/10.1016/j.jneumeth.2015.11.001.

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Marchetti, Antonio, Fiamma Buttitta, Vittoria Carnicelli, Silvia Pellegrini, Gloria Bertacca, Giorgio Merlo, and Generoso Bevilacqua. "Enriched SSCP." Diagnostic Molecular Pathology 6, no. 4 (August 1997): 185–91. http://dx.doi.org/10.1097/00019606-199708000-00002.

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Wang, Lu, Dan Han, Binbin Qian, Zhenhao Zhang, Zhijun Zhang, and Zhifang Liu. "The Validity of Steady-State Visual Evoked Potentials as Attention Tags and Input Signals: A Critical Perspective of Frequency Allocation and Number of Stimuli." Brain Sciences 10, no. 9 (September 7, 2020): 616. http://dx.doi.org/10.3390/brainsci10090616.

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Steady-state visual evoked potential (SSVEP) is a periodic response to a repetitive visual stimulus at a specific frequency. Currently, SSVEP is widely treated as an attention tag in cognitive activities and is used as an input signal for brain–computer interfaces (BCIs). However, whether SSVEP can be used as a reliable indicator has been a controversial issue. We focused on the independence of SSVEP from frequency allocation and number of stimuli. First, a cue–target paradigm was adopted to examine the interaction between SSVEPs evoked by two stimuli with different frequency allocations under different attention conditions. Second, we explored whether signal strength and the performance of SSVEP-based BCIs were affected by the number of stimuli. The results revealed that no significant interaction of SSVEP responses appeared between attended and unattended stimuli under various frequency allocations, regardless of their appearance in the fundamental or second-order harmonic. The amplitude of SSVEP suffered no significant gain or loss under different numbers of stimuli, but the performance of SSVEP-based BCIs varied along with duration of stimuli; that is, the recognition rate was not affected by the number of stimuli when the duration of stimuli was long enough, while the information transfer rate (ITR) presented the opposite trend. It can be concluded that SSVEP is a reliable tool for marking and monitoring multiple stimuli simultaneously in cognitive studies, but much caution should be taken when choosing a suitable duration and the number of stimuli, in order to achieve optimal utility of BCIs in the future.
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Zhu, Fangkun, Lu Jiang, Guoya Dong, Xiaorong Gao, and Yijun Wang. "An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces." Sensors 21, no. 4 (February 10, 2021): 1256. http://dx.doi.org/10.3390/s21041256.

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Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.
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Chen, Yeou-Jiunn, Pei-Chung Chen, Shih-Chung Chen, and Chung-Min Wu. "Denoising Autoencoder-Based Feature Extraction to Robust SSVEP-Based BCIs." Sensors 21, no. 15 (July 23, 2021): 5019. http://dx.doi.org/10.3390/s21155019.

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For subjects with amyotrophic lateral sclerosis (ALS), the verbal and nonverbal communication is greatly impaired. Steady state visually evoked potential (SSVEP)-based brain computer interfaces (BCIs) is one of successful alternative augmentative communications to help subjects with ALS communicate with others or devices. For practical applications, the performance of SSVEP-based BCIs is severely reduced by the effects of noises. Therefore, developing robust SSVEP-based BCIs is very important to help subjects communicate with others or devices. In this study, a noise suppression-based feature extraction and deep neural network are proposed to develop a robust SSVEP-based BCI. To suppress the effects of noises, a denoising autoencoder is proposed to extract the denoising features. To obtain an acceptable recognition result for practical applications, the deep neural network is used to find the decision results of SSVEP-based BCIs. The experimental results showed that the proposed approaches can effectively suppress the effects of noises and the performance of SSVEP-based BCIs can be greatly improved. Besides, the deep neural network outperforms other approaches. Therefore, the proposed robust SSVEP-based BCI is very useful for practical applications.
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Dissertations / Theses on the topic "SSCEP"

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Wingi, Hereuwar Walipe, and n/a. "A review and analysis of Secondary Schools Community Extension Project (SSCEP)." University of Canberra. Education, 1991. http://erl.canberra.edu.au./public/adt-AUC20061110.163118.

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Baker, Mark. "Differentiation of dermatophyte fungi using SSCP." Thesis, University of Kent, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.498820.

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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/.

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Una Brain Computer Interface (BCI) è un dispositivo che permette la misura e l’utilizzo di segnali cerebrali al fine di comandare software e/o periferiche di vario tipo, da semplici videogiochi a complesse protesi robotizzate. Tra i segnali attualmente più utilizzati vi sono i Potenziali Evocati Visivi Steady State (SSVEP), variazioni ritmiche di potenziale elettrico registrabili sulla corteccia visiva primaria con un elettroencefalogramma (EEG) non invasivo; essi sono evocabili attraverso una stimolazione luminosa periodica, e sono caratterizzati da una frequenza di oscillazione pari a quella di stimolazione. Avendo un rapporto segnale rumore (SNR) particolarmente favorevole ed una caratteristica facilmente studiabile, gli SSVEP sono alla base delle più veloci ed immediate BCI attualmente disponibili. All’utente vengono proposte una serie di scelte ciascuna associata ad una stimolazione visiva a diversa frequenza, fra le quali la selezionata si ripresenterà nelle caratteristiche del suo tracciato EEG estratto in tempo reale. L’obiettivo della tesi svolta è stato realizzare un sistema integrato, sviluppato in LabView che implementasse il paradigma BCI SSVEP-based appena descritto, consentendo di: 1. Configurare la generazione di due stimoli luminosi attraverso l’utilizzo di LED esterni; 2. Sincronizzare l’acquisizione del segnale EEG con tale stimolazione; 3. Estrarre features (attributi caratteristici di ciascuna classe) dal suddetto segnale ed utilizzarle per addestrare un classificatore SVM; 4. Utilizzare il classificatore per realizzare un’interfaccia BCI realtime con feedback per l’utente. Il sistema è stato progettato con alcune delle tecniche più avanzate per l’elaborazione spaziale e temporale del segnale ed il suo funzionamento è stato testato su 4 soggetti sani e comparato alle più moderne BCI SSVEP-based confrontabili rinvenute in letteratura.
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Hodgskiss, Dean Leslie. "Towards improved visual stimulus discrimination in an SSVEP BCI." Master's thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/11283.

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The dissertation investigated the influence of stimulus characteristics, electroencephalographic (EEG) electrode location and three signal processing methods on the spectral signal to noise ratio (SNR) of Steady State Visual Evoked Potentials (SSVEPs) with a view for use in Brain-Computer Interfaces (BCIs). It was hypothesised that the new spectral baseline processing method introduced here, termed the 'activity baseline', would result in an improved SNR.
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Raza, 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.

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Brain-computer interface (BCI) or Brain Machine Interface (BMI) provides direct communication channel between user’s brain and an external device without any requirement of user’s physical movement. Primarily BCI has been employed in medical sciences to facilitate the patients with severe motor, visual and aural impairments. More recently many BCI are also being used as a part of entertainment. BCI differs from Neuroprosthetics, a study within Neuroscience, in terms of its usage; former connects the brain with a computer or external device while the later connects the nervous system to an implanted device. A BCI receives the modulated input from user either invasively or non-invasively. The modulated input, concealed in the huge amount of noise, contains distinct brain patterns based on the type of activity user is performing at that point in time. Primary task of a typical BCI is to find out those distinct brain patterns and translates them to meaningful communication command set. Cursor controllers, Spellers, Wheel Chair and robot Controllers are classic examples of BCI applications. This study aims to investigate an Electroencephalography (EEG) based non-invasive BCI in general and its interaction with a web interface in particular. Different aspects related to BCI are covered in this work including feedback techniques, BCI frameworks, commercial BCI hardware, and different BCI applications. BCI paradigm Steady State Visually Evoked Potentials (SSVEP) is being focused during this study. A hybrid solution is developed during this study, employing a general purpose BCI framework OpenViBE, which comprised of a low-level stimulus management and control module and a web based Google Street View client application. This study shows that a BCI can not only provide a way of communication for the impaired subjects but it can also be a multipurpose tool for a healthy person. During this study, it is being established that the major hurdles that hamper the performance of a BCI system are training protocols, BCI hardware and signal processing techniques. It is also observed that a controlled environment and expert assistance is required to operate a BCI system.
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Mgijima-Msindwana, Mirriam Miranda Nomso. "Implementing Educational Innovations: The case of the Secondary School Curriculum Diversification Programme in Lesotho." University of the Western Cape, 1991. http://hdl.handle.net/11394/8434.

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Philosophiae Doctor - PhD
Between 1974 and 1982 the MOE introduced in two phases the diversification programme [SSCDP] which sought to establish practical subjects in the secondary school curriculum. This study examines the sustainability of implementation efforts beyond project expiry. It was hypothesised that SSCDP is not working as originally intended. The broad research problem was framed thus: What implementation response arises from an open-ended innovation policy? Subsidiary questions are: 1. How far have the policy-makers communicated the meaning of SSCDP and what factors account for mismatches between policy intentions and innovation practice? 2. What is the response of Project schools and what factors explain variation in response? 3. What is their significance for the sustainability of SSCDP? The analysis draws key concepts from the innovation literature on models and strategies of planned change; relationships in the implementation hierarchy; determinants of and orientations to the implementation process. Centred around qualitative research methods, the investigation utilises data from project documents, semi-structured interviews and from observations during school visits. Findings show an overall low level of implementation that varies among project schools. This is attributed to: Poor interpretation of SSCDP goals; Deficiencies in the implementation management; Idiosyncratic school behaviours. The study concludes that the 'practitioner-policy-maker' discrepancy is significant, hence the gap between policy intents and innovation practice. The gap is not regarded so much as an ultimate failure of the programme but as a necessary condition that allows for mutual adaptation between the innovation and its setting. This is reflected in the varied patterns of implementation response, classified as the: faithful; negotiators; selective adaptors; expansionists; and reductionist. As a policy-oriented study aiming at providing an 'improvement value', the findings lead to a proposal of improvements in the strategies of managing change in three areas: shifting focus from an adoption to an implementation perspective. Recognising implementation as a process dependent on a mutual linkage relationship among participants. Recognising schools as important bearers of change. These three are crucial factors in the implementation-sustainability relationship.
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Lüth, Thorsten [Verfasser]. "Individualisierte Kalibrierung in einem SSVEP-basierten Brain-Computer Interface / Thorsten Lüth." Aachen : Shaker, 2016. http://d-nb.info/1098042409/34.

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TELLO, R. J. M. G. "A Novel Approach Of Independent Brain-computer Interface Based On SSVEP." Universidade Federal do Espírito Santo, 2016. http://repositorio.ufes.br/handle/10/9683.

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Made available in DSpace on 2018-08-02T00:01:45Z (GMT). No. of bitstreams: 1 tese_10281_TeseDoutoradoRichardTello2016.pdf: 12331551 bytes, checksum: 0dae4547527893319ca299b5e22f6234 (MD5) Previous issue date: 2016-09-01
Durante os últimos dez anos, as Interfaces Cérebro Computador (ICC) baseadas em Potenciais Evocados Visuais de Regime Permanente (SSVEP) têm chamado a atenção de muitos pesquisadores devido aos resultados promissores e as altas taxas de precisão atingidas. Este tipo de ICC permite que pessoas com dificuldades motoras severas possam se comunicar com o mundo exterior através da modulação da atenção visual a luzes piscantes com frequência determinada. Esta Tese de Doutorado tem o intuito de desenvolver um novo enfoque dentro das chamadas ICC Independentes, nas quais os usuários não necessitam executar tarefas neuromusculares para seleção visual de objetivos específicos, característica que a distingue das tradicionais ICCs-SSVEP. Assim, pessoas com difculdades motoras severas, como pessoas com Esclerose Lateral Amiotrófca (ELA), contam com uma nova alternativa de se comunicar através de sinais cerebrais. Diversas contribuições foram realizadas neste trabalho, como, por exemplo, melhoria do algoritmo extrator de características, denominado Índice de Sincronização Multivariável (ou MSI, do Inglês), para a detecção de potenciais evocados; desenvolvimento de um novo método de detecção de potenciais evocados através da correlação entre modelos multidimensionais (tensores); o desenvolvimento do primeiro estudo sobre a influência de estímulos coloridos na detecção de SSVEPs usando LEDs; a aplicação do conceito de Compressão na detecção de SSVEPs; e, fnalmente, o desenvolvimento de uma nova ICC independente que utiliza o enfoque de Percepção Fundo-Figura (ou FGP, do Inglês).
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Rogers, Michael. "PCR-SSCP analysis of microbial communities in a model system." Thesis, University of Kent, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270818.

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Cotrina, A. "A Novel Stimulation Paradigm For a Brain-computer Interface Based On Ssvep." Universidade Federal do Espírito Santo, 2015. http://repositorio.ufes.br/handle/10/9719.

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Made available in DSpace on 2018-08-02T00:02:02Z (GMT). No. of bitstreams: 1 tese_5146_Tese Anibal Cotrina Atencio20160413-94207.pdf: 18045911 bytes, checksum: 2e54a921fab59f7fa26a089e26928f91 (MD5) Previous issue date: 2015-11-30
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Books on the topic "SSCEP"

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Vulliamy, Graham. A comparative analysis of SSCEP outstations. Port Moresby: Educational Research Unit, University of Papua New Guinea, 1985.

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Crossley, Michael. The policy of SSCEP: Context and development. Papua New Guinea, Oceania: Educational Research Unit, University of Papua New Guinea, 1986.

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International Information Systems Security Certification Consortium, ed. Official (ISC)℗ø guide to the SSCP CBK. 2nd ed. Boca Raton: CRC Press, 2011.

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Timothy-Asobele, S. J. A manual of poetry for Kogi SSCE pupils. Lagos, Nigeria: Upper Standard Publications, 2003.

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Gibson, Darril. SSCP systems security certified practitioner: Exam guide : all in one. New York: McGraw-Hill, 2012.

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SSCP systems security certified practitioner: Exam guide : all in one. New York: McGraw-Hill, 2012.

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J, Isaac Michael, ed. The SSCP prep guide: Mastering the seven key areas of system security. New York: Wiley, 2003.

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Anamgba, A. O. The substance of economics: For SSCE, GCE, JAMB, UME and undergraduate examinations. Enugu [Nigeria]: Snaap Press Ltd., 2002.

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Vredeveld, Jan W. Somatosensory evoked potentials (median nerve stimulation) in acute stroke: A prospective study of the SSEP-N20 in relation to the recovery from acute stroke and a discussion of the source of the N20 in relation with CT-scan findings. Lisse: Swets & Zeitlinger, 1985.

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Vulliamy, Graham. Sorcery and SSCEP : the cultural context of an educational innovation. 1985.

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Book chapters on the topic "SSCEP"

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Etscheid, Michael, and Detlev Riesner. "TGGE and SSCP." In Molecular Tools for Screening Biodiversity, 150–51. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-009-0019-6_29.

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Colon, E. J. "Introduction to SSEP." In Evoked Potential Manual, 207–20. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-2059-0_6.

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Diez, P. F., A. Garcés Correa, and E. Laciar Leber. "SSVEP Detection Using Adaptive Filters." In V Latin American Congress on Biomedical Engineering CLAIB 2011 May 16-21, 2011, Habana, Cuba, 1154–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-21198-0_293.

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Jordan, William C., Katherine Foley, and Michael W. Bruford. "Modifications to SSCP Analysis Conditions." In Molecular Tools for Screening Biodiversity, 164. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-009-0019-6_32.

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Han, Mei, and Mary Ann Robinson. "PCR-SSCP Analysis of Polymorphism." In PCR Protocols, 327–33. Totowa, NJ: Humana Press, 2003. http://dx.doi.org/10.1007/978-1-4612-0055-0_48.

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Chevallier, Sylvain, Emmanuel K. Kalunga, Quentin Barthélemy, and Florian Yger. "Riemannian Classification for SSVEP-Based BCI." In Brain–Computer Interfaces Handbook, 371–96. Boca Raton : Taylor & Francis, CRC Press, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351231954-19.

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Chatzivassiliou, Elisavet K., and Grazia Licciardello. "Assessment of Genetic Variability of Citrus tristeza virus by SSCP and CE-SSCP." In Methods in Molecular Biology, 79–104. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9558-5_7.

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Leung, Kim Hung, and Shea Ping Yip. "Single Strand Conformation Polymorphism (SSCP) Analysis." In Springer Protocols Handbooks, 117–31. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-375-6_9.

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Jordan, William C., Katherine Foley, and Michael W. Bruford. "Single-Strand Conformation Polymorphism (SSCP) Analysis." In Molecular Tools for Screening Biodiversity, 152–56. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-009-0019-6_30.

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Richter, Erich O., Marina V. Abramova, and Kenneth M. Alò. "EMG/SSEP Monitoring During Sacral Neuromodulation." In Atlas of Implantable Therapies for Pain Management, 101–5. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-0-387-88567-4_15.

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Conference papers on the topic "SSCEP"

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Wang, 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.

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Research in brain-computer interfaces have focused primarily on motor imagery tasks such as those involving movement of a cursor or other objects on a computer screen. In such applications, it is important to detect when the user is interested in moving an object and when the user is not active in this task. This paper evaluates the steady state visual evoked potential (SSVEP) as a feedback mechanism to confirm the mental state of the user during motor imagery. These potentials are evoked when a subject looks at a flashing objects of interest. Four different experiments are conducted in this paper. Subjects are asked to imagine the movement of flashing object in a given direction. If the subject is involved in this task, the SSVEP signal will be detectable in the visual cortex and therefore the motor imagery task is confirmed. During the experiment, EEG signal is recorded at 4 locations near visual cortex. Using a weighting scheme, the best combination of the recorded signal is selected to evaluate the presence of flashing frequency. The experimental result shows that the SSVEP can be detected even in complex motor imagery of flickering objects. The detection rate of 85% is achieved while the refreshing time for SSVEP feedback is set to 0.5 seconds.
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Riyahi, Pouria, and Azim Eskandarian. "Analyzing Steady-State Visual Evoked Potentials for Effective User Response Detection for Brain-Computer Interfaces." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-65592.

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This article evaluates an M-order Adaptive Kalman filter analysis on Steady-State Visual Evoked Potentials (SSVEPs). This model is based on finding the original brain source signals from their combined observed EEG signals. At each time step, observed brain signals are filtered according to their ideal reference signals measured from 10, 11, 12 and 13 Hz LED stimuli. SSVEP response detection is based on maximum Signal to Noise Ratio (SNR) of the brain source signals. In each test, the average system accuracy is calculated with and without overlapped time-windows along with system Information Transfer Rate (ITR). The overall system accuracy and ITR are showing promising level of SSVEP detection for future online BCI systems.
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Luzheng Bi, Ke Jie, Xin-an Fan, and Yun Li. "A SSVEP brain-computer interface with the hybrid stimuli of SSVEP and P300." In 2013 ICME International Conference on Complex Medical Engineering (CME 2013). IEEE, 2013. http://dx.doi.org/10.1109/iccme.2013.6548241.

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Bi, Luzheng, Ke Jie, Jinling Lian, and Bingqing Wu. "A SSVEP brain–computer interface under the hybrid stimuli of P300 and SSVEP." In International Conference on Simulation and Modeling Methodologies, Technologies and Applications. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/smta141252.

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GREENE, WILLIAM, and DAVID VAUGHAN. "SSME Streamtube Evaluation Program (SSTEP)." In 28th Joint Propulsion Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-3734.

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Vidyasagar, L., and K. S. Latha Reddy. "Performance Evaluations of SSCOP Layer." In Joint International Conference on Autonomic and Autonomous Systems and International Conference on Networking and Services - (icas-isns'05). IEEE, 2005. http://dx.doi.org/10.1109/icas-icns.2005.75.

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Koo, Bonkon, and Seungjin Choi. "SSVEP response on Oculus Rift." In 2015 3rd International Winter Conference on Brain-Computer Interface (BCI). IEEE, 2015. http://dx.doi.org/10.1109/iww-bci.2015.7073028.

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Sastry, R. V., S. Karthik, Adithya R., Aravind Ravi, S. Indrapriyadarsini, Gagandeep Panwar, and A. G. Ramakrishnan. "Calibration-free SSVEP-based BCI Switch." In 2019 IEEE 16th India Council International Conference (INDICON). IEEE, 2019. http://dx.doi.org/10.1109/indicon47234.2019.9028973.

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Kalaganis, Fotis, Elisavet Chatzilari, Kostas Georgiadis, Spiros Nikolopoulos, Nikos Laskaris, and Yiannis Kompatsiaris. "An Error Aware SSVEP-based BCI." In 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2017. http://dx.doi.org/10.1109/cbms.2017.44.

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Prasad, P. M. Shyam, Radhika Swarnkar, K. Visnu Prasad, M. Radhakrishnan, Md Farukh Hashmi, and A. G. Keskar. "SSVEP Signal Detection for BCI Application." In 2017 IEEE 7th International Advance Computing Conference (IACC). IEEE, 2017. http://dx.doi.org/10.1109/iacc.2017.0126.

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