Дисертації з теми "Steady-State Visually Evoked Potential (SSVEP)"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-15 дисертацій для дослідження на тему "Steady-State Visually Evoked Potential (SSVEP)".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте дисертації для різних дисциплін та оформлюйте правильно вашу бібліографію.
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.
Das, Aritra. "Effect of Stimulus Normalization and Visual Attention at multiple scales of Neural Integration." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5986.
Повний текст джерелаDBT-Wellcome Trust India Alliance (Grant IA/S/18/2/504003), Tata Trusts, DBT-IISc Partnership Programme
Santos, Joao David Franca. "Development of a BCI framework: hardware/software architecture and control of domotic appliances." Master's thesis, 2019. http://hdl.handle.net/10316/87882.
Повний текст джерелаA interface cérebro-computador (ICC) permite a comunicação direta entre o cérebro e um computador digital. Os sistemas de ICC ainda não são exequíveis para o uso diário e, portanto, estãoconfinados principalmente à experimentação em laboratório. É necessário converter os sistemasICC em dispositivos portáteis, confiáveis e independentes que possam ser usados em aplicativosdo mundo real fora do laboratório, para que possam atender a potenciais usuários-alvo, em aplicativos de comunicação / controlo para pessoas com deficiências motoras graves, ou como umaferramenta para a neuro-reabilitação de distúrbios motores ou distúrbios do desenvolvimento neurológico. Neste trabalho, propõe-se o desenvolvimento de um sistema ICC que possa controlardispositivos domóticos com base em um equipamento de open source (OpenBCI). Um dos principais objetivos desta tese é avaliar se este dispositivo fornece alguns dos recursos desejados paraum ICC, no que diz respeito ao desgaste, operação autónoma de confiabilidade e baixo custo. OICC proposto é baseado num neuromecanismo chamado potencial evocado visual em estado estacionário (PEVEE), que é estimulado por estimulação visual. Vários métodos de processamentode sinal (FFT, Welch, CCA-Standard, CCA-IT, CCA-Comb e CCA-Lite) foram implementadose comparados para extrair características do PEVEE. Inicialmente, os métodos de processamentode sinal foram testados offline em bancos de dados de referência e, em seguida, testados offline eonline com nossa estrutura / configuração do ICC. Foram comparados dois tipos de estimulação,um provocado por uma matriz de LEDs e o outro provocado por flashes de ecrã. Os resultadosoffline mostraram que a Análise Combinacional de Correlação Canónica atinge melhor precisãoem comparação com os outros métodos de extração de recursos, mas em termos de tempo de processamento do sinal, o método CCA-Lite diminui para 58 % do CCA-Comb. Os resultados obtidosonline mostraram ser possível controlar o ICC com uma precisão de 84,6 % (para segmentos deEEG de 5 segundos), o que mostra a viabilidade do sistema, embora várias limitações do sistemaOpenBCI tenham sido identificadas.
Brain-Computer Interface (BCI) allows the direct communication between the brain and a digital computer. BCIs systems are still impractical for everyday use and therefore are mostly confinedto lab experimentation. There is a need to convert BCI systems into wearable, reliable and standalone devices that can be used in real-world applications outside the lab, so that they can servepotential target users, in communication/control applications for people with severe motor disabilities, or as a tool for neurorehabilitation of motor disorders or neurodevelopmental disorders. Inthis work, it is proposed the development of a BCI system that can control domotic appliancesbased on an open-source equipment (OpenBCI). One of the main goals of this thesis is to evaluatewhether this device provides some of the desired features for a BCI, regarding wearability, reliability stand-alone operation and low-cost. The proposed BCI is based on a neural mechanism calledsteady-state visual evoked potential (SSVEP) which is elicited by visual stimulation. Several signal processing methods (FFT, Welch, CCA-Standard, CCA-IT, CCA-Comb and CCA-Lite) wereimplemented and compared to extract SSVEP features. Initially, the signal processing methodswere tested offline on benchmark datasets and then they were tested offline and online with ourBCI setup/framework. Two types of stimulation were compared, one elicited by a LED matrix andthe other elicited by screen flashes. Offline results showed that Combinational Canonical Correlation Analysis achieves the best accuracy in comparison to the other feature extraction methods,but in terms of signal processing time, the CCA-Lite method decreases to 58% of CCA-Comb.The results obtained in online experiments showed that it was possible to control the BCI with anaccuracy of 84.6% (for 5 second identification EEG segments), which shows the feasibility of thesystem, although several limitations of the OpenBCI system were identified.
Stamps, Kenyon. "A steady-state visually evoked potential based brain-computer interface system for control of electric wheelchairs." 2012. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1001343.
Повний текст джерелаDetermines whether Hidden Markov models (HMM) can be used to classify steady state visual evoked electroencephalogram signals in a BCI system. This is for the purpose of aiding disabled people in driving a wheelchair.
Yen, Tzu-Hsiang, and 顏子翔. "Design of Wearable Steady State Visually Evoked Potential-based Brain Computer Interface by Using Field Programmable Gate Array." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/dt8skd.
Повний текст джерелаPramod, Salelkar Siddhesh. "Characterization of Brain Signals Across Scales Using Temporally Modulated Visual Stimuli." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/4973.
Повний текст джерела