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Sirobaba, N. S., and D. O. Marchenko. "Brain-computer interface." Thesis, Вид-во СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/22533.
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A brain–computer interface (BCI), sometimes called a direct neural interface or a brain–machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often aimed at assisting, augmenting or repairing human cognitive or sensory-motor functions.
The field of BCI has advanced mostly toward neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-nineties.
When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/22533
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Shliahetskiy, A. A. "Brain - computer interface." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/46934.
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Brain -computer interface - the interface that implements the
connection between the human brain and the computer. The main idea is
that when you think about action and do it, the same part of the brain is
activated.In the middle of the XIX century, Emil Du Bois- Reymond
showed the relationship between electric current and nerve impulses; in
1875. Richard ketone managed to register the electrical activity of the brain
of animals. The psychiatrist Hans Berger in 1924 invented a method to
record the electrical activity of the human brain. In 1967, psychiatrist
Edmond Dewan published a paper in which he described
the experiment where a man was trying to send a message
to electroencephalogram by means of dot-and-dash,using brain
activity.One of the first practically implemented IMC is
considered a virtual keyboard made by Farwell and Donchyn which
was created in 1988.
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Almeida, Luís Filipe Martinho de. "Brain computer interface." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/21618.
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Mestrado em Engenharia Eletrónica e TelecomunicaçõesA investigação e desenvolvimento de sistemas BCI, Brain Computer Interface tem crescido de ano para ano, com resultados cada vez melhores. Uma das principais vertentes para a qual estes sistemas têm sido usados é na área da neuroprostética. Desta forma tem-se demonstrado em vários estudos e investiga ções a possibilidade de controlar membros completos ou parciais robóticos por nós seres humanos, dando assim uma liberdade e conquista de movimentos perdidos a pessoas incapacitadas. No entanto uma grande parte dos melhores resultados obtidos envolve a utilização de BCI invasivos, o que necessita de ser implantado diretamente no cérebro humano, através de uma operação cirúrgica. Isto é ainda um dos grandes inconvenientes que esta abordagem implica e também o facto de uma grande parte destes estudos ainda estarem na fase de testes. Este trabalho teve como objetivo tentar comprovar que os BCI não invasivos também conseguem obter bons resultados apesar das suas limitações e pior aquisição de resultados devido à inclusão de ruído por parte do nosso crânio e cabelo, assim como a inclusão dos Parâmetros Hjorth proporciona melhores resultados na identificação das classes desejadas. Dividiu-se o trabalho em duas partes, uma para a identificação das classes de “Piscar de Olho” e outra para identificação das classes de “Ações Pensadas” . Os resultados foram todos obtidos tendo em conta apenas um utilizador. Relativamente à deteção do “Piscar de Olho” comprovou-se que ́e facilmente conseguido com resultados quase perfeitos, com uma precisção de 99 . 98%. Relativamente à deteção de “Ações Pensadas” não foi possível comprovar a sua deteçãao usando sessções de gravação diferentes, no entanto verificou-se que a classificação das classes tendo em conta a mesma sessão de gravação, obtém resultados muito bons com valores acima dos 99% para o melhor m ́etodo preditivo. A inclusão dos Parâmetros Hjorth foi em todos os casos de estudo, a opção em que os resultados foram sempre melhores, demonstrando assim que a inclusão dos mesmos é uma opção aconselhável, pois em alguns casos, a precisão na deteção das classes aumento para duas ou mais vezes. Os resultados são promissores e apesar de não ter conseguido obter os melhores resultados para sessões de gravação independentes na classificação de “Ações Pensadas” , indico nas análises os passos necessáios para a obtenção de melhores resultados e a possibilidade de generalização do processo para diversos utilizadores.
The research and development of BCI systems, Brain Computer Interface has grown from year to year, with better and better results. One of the main areas for which these systems have been used is the neuroprosthetic. Several studies and investigations have shown the possibility of controlling complete or partial robotic members by people, thus giving a freedom and conquest of lost movements to incapacitated persons. However, a great part of the best results obtained involves the use of invasive BCI, which needs to be implanted directly into the human brain through a sirurgical operation. This is still one of the great drawbacks that this approach entails and also the fact that a large part of these studies are still in the testing phase. The aim of this study was to try and prove that non-invasive BCI can also achieve good results despite their limitations and inferior quality on the acquisition of data due to the inclusion of noise from our skull and hair, and also that the inclusion of the Hjorth Parameters on the analysis provides better results in identifying the desired classes. The work was split into two parts, one for the identification of “Eye Blinking” classes and the other for “Thought Actions” classes. The results were all obtained with only one user in mind. Regarding the detection of “Eye Blinking” it has been found that it is easily achieved with near-perfect results, with an accuracy of 99 . 98%. Regarding the detection of “Thought Actions” it was not possible to verify its detection using different recording sessions, however it was verified that the classification of classes taking into account the same recording session, obtains very good results with values above 99% for the best predictive method. The inclusion of Hjorth Parameters was in all study cases, the option in which the results were always better, thus demonstrating that their inclusion is an advisable option, since in some cases, the accuracy in detecting classes doubled or more. The results are promising and although I haven’t been able to obtain the best results for independent recording sessions in the classification of “Thought actions” , I indicate in the analysis some steps necessary to obtain better results and the possibility of generalizing the process for several users.
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Skidmore, Trent A. "The electroencephalographic human-computer interface." Ohio : Ohio University, 1991. http://www.ohiolink.edu/etd/view.cgi?ohiou1173327705.
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Halder, Sebastian [Verfasser]. "Prediction of Brain-Computer Interface Performance: For P300 and Motor Imagery Brain-Computer Interfaces / Sebastian Halder." München : Verlag Dr. Hut, 2011. http://d-nb.info/1015607330/34.
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Lachman, Richard W. 1972. "Animist interface : experiments in mapping character animation to computer interface." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/61831.
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Soukup, Michael. "Brain-Computer Interface In Control Systems." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-25749.
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A Brain-Computer Interface (BCI) is a system that allows for direct communication between the brain and an external device. Originally, the motivation for developing BCIs has been to provide severely disabled individuals with a basic communication system. Recent years, BCIs directed at regular consumers in practical control applications have gained popularity as well, for which the ultimate goal is to provide a more natural way of communicating with machines. However, BCIs intended at use in control systems face several challenges and are still inferior to conventional controllers in terms of usability, reliability and practical value. In this thesis, we explore two novel concepts that can enforce BCIs. The first concept relies on detection of so-called Error-Related Potentials (ErrPs), which are the response in brainwaves to an erroneous event. We argue for that these potentials can serve as reward-based signals that give feedback to the system, which enables the BCI to adapt to the user. The second concept is to use sequence labeling frameworks based on Conditional Random Fields (CRFs) to translate brainwaves into control signals with greater accuracy. We also suggest how these two concepts can be combined.Our experiments to detect ErrPs in BCI control applications using a consumer grade headset to obtain EEG measurements indicate no presence of ErrPs, however, the reliability of the EEG recordings is questionable. Furthermore, we have developed a new implementation of the so-called Sparse Hidden-Dynamic CRF (SHDCRF) and measure its performance on a common BCI classification task. In our experiment, the model outperforms similar classifiers that represent the state-of-the-art, and the results suggest that the proposed model is superior in terms of accuracy and modeling capacity.
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Kaanta, Bradley C. (Bradley Carter) 1980. "PINS : a haptic computer interface system." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28419.
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Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 73-74).
The research goal was to develop a dense array of discreet vertical actuators as an input and output device with haptic feedback for Human Computer Interaction (HCI). This expands upon the current research of table surfaces as medium for HCI by adding a third dimension that both a user and a computer can control. The use of vertical actuation makes possible new kinds of physical interactions with virtual objects and allows a computer to maintain constancy with the physical representation and the digital information. This requires the design and constructions of an elegant, reliable, and economically reasonable actuator array. Each array element requires autonomy to quickly and accurately move to a precise height. As an array, combined elements must provide enough resolution so that the user perceives the array as a continuously morphing, three-dimensional surface. Shape transformations are accomplished either indirectly by digital means or directly by user touch. The proposed research will focus on development of a real-time haptic actuation arrays supporting technology. The process includes working on the design, function, appearance, response, and implementation.
by Bradley C. Kaanta.
M.Eng.and S.B.
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Wiklund, Victor, and Axel Karlsson. "Generalisation in brain computer interface classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 1992. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229999.
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Brain computer interfaces (BCIs) are systems that allow users to interact with devices without relying on the neuromuscular pathways. This interaction is achieved by allowing the system to read the electrical activity of the brain and teaching it to map certain patterns of activation to certain commands. There are many applications for BCIs ranging from controlling prosthetics to gaming, but adapting both the user and the system to one another is a time and resource consuming process. Even more problematic, BCIs tend to only perform well for a single user and only for a limited time. This paper aims to investigate the accuracy of single-subject singlesession BCIs on other subjects and other sessions. To that end three different classifiers, a Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Long Short-Term Memory network (LSTM) are developed and tested on a data set consisting of five subjects, two sessions for a binary classification task. Our results show that training on single-subject single-session data leads to an average cross-subject accuracy of 45-50% and an average cross-session accuracy of 50-55%. We find that there is no statistically significant difference in accuracy depending on the classifier used and discuss factors that affect generalization such as model complexity and good subjects.Brain computer interfaces (BCIs) är system som gör det möjligt för användare att interagera med apparater utan behov av de neuromuskulära banorna. Den här interaktionen möjliggörs genom att systemet läser den elektriska aktiviteten i hjärnan och lär sig associera vissa mönster av aktivitet till vissa kommandon. Det finns många användningsområden för BCIs, från att kontrollera proteser till spel, men att anpassa både användaren och systemet till varandra är en process som kräver både tid och resurser. Än värre, BCIs tenderar att bara funka bra för en enskild användare och bara under en begränsad tid. Den här rapporten avser undersöka hur bra ett BCI system tränat på data för ett subjekt och en session är på klassificering av data för andra subjekt och andra sessioner. Tre typer av klassificerare, en Support Vector Machine (SVM), Convolutional Neural Network (CNN) och Long Short-Term Memory network (LSTM) byggs och utvärderas på data från fem subjekt över två sessioner på en binär klassificeringuppgift. Våra resultat indikerar att träning på data för ett subjekt, en session leder till en genomsnittlig pricksäkerhet på 45-50% på andra subjekt, 50-55% på andra sessioner. Vi finner även att det inte finns någon statistiskt signifikant skillnad i pricksäkerhet beroende på vilken typ av klassificerare som används och diskuterar faktorer som påverkar generalisering såsom modellkomplexitet och bra subjekt.
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Oates, Shawn P. "CHILD-COMPUTER INTERACTION: EXPLORING INTERFACE DESIGN." Miami University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=miami1133800774.
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Kadambi, Pooja. "Brain Computer Interface for Fatigue Assessment." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1393237787.
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Devi, M. K. Sowmia Agrawal Prathima. "Interface selection in multi-interface mobile devices." Auburn, Ala., 2006. http://repo.lib.auburn.edu/2006%20Summer/Theses/MYLAPORE_KRISHNA_8.pdf.
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Moore, Melody M. "User interface reengineering." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/12899.
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Del, Monte Tamara. "Utilizzo dell'elettroencefalografia per la brain-computer interface." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9220/.
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Con Brain-Computer Interface si intende un collegamento diretto tra cervello e macchina, che essa sia un computer o un qualsiasi dispositivo esterno, senza l’utilizzo di muscoli. Grazie a sensori applicati alla cute del cranio i segnali cerebrali del paziente vengono rilevati, elaborati, classificati (per mezzo di un calcolatore) e infine inviati come output a un device esterno. Grazie all'utilizzo delle BCI, persone con gravi disabilità motorie o comunicative (per esempio malati di SLA o persone colpite dalla sindrome del chiavistello) hanno la possibilità di migliorare la propria qualità di vita. L'obiettivo di questa tesi è quello di fornire una panoramica nell'ambito dell'interfaccia cervello-computer, mostrando le tipologie esistenti, cercando di farne un'analisi critica sui pro e i contro di ogni applicazione, ponendo maggior attenzione sull'uso dell’elettroencefalografia come strumento per l’acquisizione dei segnali in ingresso all'interfaccia.
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Manzaneda, Martin. "Natural Language Interface Technology in Computer Games." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166647.
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This project delivers the results of research about the implementation of natural language interfaces (NLIs) on computer games. We will study how to combine these concepts, making a small game and evaluating different methods to achieve this.
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Chahine, Fredrick. "Classification of Electroencephalographic Signalsfor Brain-Computer Interface." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-136678.
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Brain-computer interface is a promising research area that has the potential to aid impaired individuals in their daily lives. There are several different methods for capturing brain signals, both invasive and noninvasive. A popular noninvasive technique is electroencephalography (EEG). It is of great interest to be able to interpret EEG signals accurately so that a machine can carry out correct instructions. This paper looks at different machine learning techniques, both linear and nonlinear, in an attempt to classify EEG signals. It is found that support vector machines provide more satisfactory results than neural networks.
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NORDSTRÖM, RICHARD, and ANNIKA TÄNGMARK. "Classification of Electroencephalographic SignalsFor Brain-Computer Interface." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-136679.
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Brain-Computer Interface (BCI) can be used for example to help disabled people to control a computer without the use of mouse or keyboard. The brain signals beta and mu are acquired by electroencephalography (EEG) and shows what parts of the brain that are active not only at the performing of a muscular movement, but also by thinking about it. By analyzing EEG-signals with the methods linear discriminant analysis and artificial neural networks the aim is to explore which of two possible cognitive tasks a subject is performing. In the essay these methods are compared with aspect to correct classifications. In conclusion, when performing binary classification of mu and beta waves, a small multi layer perception is sufficient.Hjärna-datorgränssnitt (brain-computer interface, BCI) kan användas för att exempelvis hjälpa svårt funktionsnedsatta människor att styra en dator utan att använda mus eller tangentbord. Hjärnsignalerna beta och my erhålls via electroencefalografi (EEG) och visar vilka delar av hjärnan som är aktiva inte bara vid utförandet av muskelrörelser utan även vid tanken därpå. Genom att analysera EEG-signalerna med metoderna linjär diskriminantanalys och artificiellt neuralt nätverk är syftet att undersöka vilken av två möjliga kognitiva uppgifter en försöksperson utför. I uppsatsen jämförs dessa metoder med avseende på korrekta klassificering. Som slutsats kan sägas att vid binär klassifikation av beta- och my-signaler är minsta möjliga flerlagersperceptron tillräcklig.
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Wallace, Michael D. "METIS : a human-computer interface design methodology." Thesis, University of Ulster, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261053.
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Fergle, Ronald. "Improving the computer interface in architectural education." Thesis, Massachusetts Institute of Technology, 1986. http://hdl.handle.net/1721.1/79944.
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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1986.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH
Includes bibliographical references (leaves 73-74).
The influence of the computer is increasing within the architectural profession. One aspect of this is the growing use of microcomputer programs in architectural education. Many of these programs have their roots in the engineering disciplines, and therefore their procedural methodology may no t be compatible with the architectural design process. In addition to this, most of the programs used in the universities are originally designed for the professional practitioner, and may not be appropriate for the academic environment This discussion explores the characteristics of the architectural design process, and what issues programmers need to address when writing software for use by designers. More specifically, how should the programmer approach designing educational software, so that the computer becomes a more effective tool in enabling the student to develop heuristic knowledge about some aspect of architectural design. The programming factors that influence the effectiveness of this type of educational software include: the appropriate use of graphics, flexible input/output sequences, procedural transparency of the program structure, and the iterative comparison of design options. These concepts are analyzed in a series of programming examples involving energy analysis and daylighting analysis. Existing programs are critiqued, and suggestions for improvements are made. The use of processors to facilitate the testing and comparison of results are presented, as well as guidelines for additional developments using knowledge base overlays.
by Ronald Fergle.
M.S.
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Ellis, Loftie. "Human-computer interface using a web camera." Thesis, Stellenbosch : University of Stellenbosch, 2007. http://hdl.handle.net/10019.1/1988.
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Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2007.In this thesis we present a human-computer interface (HCI) system for disabled persons using only a basic web camera. Mouse movements are simulated by small movements of the head, while clicks are simulated by eye blinks. In this study, a system capable of face tracking, eye detection (including iris detection), blink detection and finally skin detection and face recognition has been developed. A detection method based on Haar-like features are used to detect the face and eyes. Once the eyes have been detected, a support vector machines classifier is used to detect whether the eye is open or closed (for use in blink detection). Skin detection is done using K-means clustering, while Eigenfaces is used for face recognition. It is concluded that using a web camera as a human-computer interface can be a viable input method for the severely disabled.
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Murdoch, Michael J. "Nonverbal vocal interface /." Link to online version, 2006. https://ritdml.rit.edu/dspace/handle/1850/10346.
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Metais, Thierry. "A dynamic gesture interface." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/26983.
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Technological advancements during the last years made three dimensional virtual environments ubiquious computer applications. The traditional input devices such as keyboards and mice have reached their limits for they are no longer intuitive to manage 3 dimensional interactions. During the last decades, digital gloves were invented in an attempt to capture hand gestures. The work presented in this thesis is about recognizing hand shape based gestures for virtual environment navigation. We addressed this issue in two steps: first, we look for gesture representations that would allow a good discrimation between gestures; second, we have examined three strategies namely template matching, neural network and hidden Markov models to classify inputs suitably formatted. Experiments on classifying gesture samples with the three methods seem encouraging (more than 90 percent recognition), but when looking at real time gestural sequencee we conclude that only the Viterbi and neural network approaches should be used for a gesture interface.
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Sylenko, E. V. "Brain-computer." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/45871.
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The development of different variants of the interface ―brain-computer‖ (BCI) in recent years ceased to be an experimental direction and finds its practical application. What were the expectations like, what works now and what to expect from this technology in the future?
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Bhalotiya, Anuj Arun. "Brain Computer Interface (BCI) Applications: Privacy Threats and Countermeasures." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984122/.
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In recent years, brain computer interfaces (BCIs) have gained popularity in non-medical domains such as the gaming, entertainment, personal health, and marketing industries. A growing number of companies offer various inexpensive consumer grade BCIs and some of these companies have recently introduced the concept of BCI "App stores" in order to facilitate the expansion of BCI applications and provide software development kits (SDKs) for other developers to create new applications for their devices. The BCI applications access to users' unique brainwave signals, which consequently allows them to make inferences about users' thoughts and mental processes. Since there are no specific standards that govern the development of BCI applications, its users are at the risk of privacy breaches. In this work, we perform first comprehensive analysis of BCI App stores including software development kits (SDKs), application programming interfaces (APIs), and BCI applications w.r.t privacy issues. The goal is to understand the way brainwave signals are handled by BCI applications and what threats to the privacy of users exist. Our findings show that most applications have unrestricted access to users' brainwave signals and can easily extract private information about their users without them even noticing. We discuss potential privacy threats posed by current practices used in BCI App stores and then describe some countermeasures that could be used to mitigate the privacy threats. Also, develop a prototype which gives the BCI app users a choice to restrict their brain signal dynamically.
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Yang, Grant. "WIMP and Beyond: The Origins, Evolution, and Awaited Future of User Interface Design." Scholarship @ Claremont, 2015. http://scholarship.claremont.edu/cmc_theses/1126.
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The field of computer user interface design is rapidly changing and diversifying as new devices are developed every day. Technology has risen to become an integral part of life for people of all ages around the world. Modern life as we know it depends on computers, and understanding the interfaces through which we communicate with them is critically important in an increasingly digital age. The first part of this paper examines the technological origins and historical background driving the development of graphical user interfaces from its earliest incarnations to today. Hardware advancements and key turning points are presented and discussed. In the second part of this paper, skeuomorphism and flat design, two of the most common design trends today, are analyzed and explained. Finally, the future course of user interface is predicted based off of emergent technologies such as the Apple Watch, Google Glass, Microsoft HoloLens, and Microsoft PixelSense. Through understanding the roots and current state of computer user interface design, engineers, designers, and scientists can help us get the most out of our ever-changing world of advanced technology as it becomes further intertwined with our existence.
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Petrucci, Maila. "Sistemi Brain Computer Interface: dalla macchina al paziente." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10137/.
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C’è un crescente interesse nella comunità scientifica per l’applicazione delle tecniche della bioingegneria nel campo delle interfacce fra cervello e computer. Questo interesse nasce dal fatto che in Europa ci sono almeno 300.000 persone con paralisi agli arti inferiori, con una età media piuttosto bassa (31 anni), registrandosi circa 5.000 nuovi casi ogni anno, in maggioranza dovuti ad incidenti automobilistici. Tali lesioni traumatiche spinali inducono delle disfunzioni sensoriali a causa dell’interruzione tra gli arti e i centri sopraspinali. Per far fronte a questi problemi gli scienziati si sono sempre più proiettati verso un nuovo settore: il Brain Computer Interaction, ossia un ambito della ricerca volto alla costruzione di interfacce in grado di collegare direttamente il cervello umano ad un dispositivo elettrico come un computer.
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Sicbaldi, Marcello. "Brain-Computer Interface per riabilitazione motoria e cognitiva." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18556/.
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Pazienti con lesioni cerebrali o spinali possono essere affetti da gravi deficit nelle funzioni sensoriali, motorie e comunicative; sono perciò sempre più necessarie tecniche di riabilitazione avanzate, personalizzate e adattative, per limitare i deficit insorti e restituire al paziente una vita il più normale possibile. Negli ultimi decenni, numerosi gruppi di ricerca hanno sviluppato Brain-Computer Interface (BCI) basate sul segnale elettroencefalografico (EEG) con l’obbiettivo di fornire mezzi di comunicazione o riabilitazione motoria funzionale. Tuttavia, le tecnologie BCI hanno un ampio potenziale al di là della sola riabilitazione motoria. Applicazioni dei sistemi BCI in protocolli di riabilitazione cognitiva, ad esempio, hanno conseguito risultati promettenti nella prospettiva di migliorare funzioni quali l’attenzione, l'apprendimento e la memoria in pazienti con disturbi delle funzioni cognitive.
In questo lavoro di Tesi si analizzano i principi di funzionamento dei sistemi BCI, a partire dall’acquisizione del segnale elettroencefalografico fino all’estrazione e alla classificazione delle feature del segnale per decodificare intenzioni motorie e processi cognitivi (memoria, attenzione) dell’utente. Viene poi presentata un’analisi della letteratura per quando riguarda gli approcci BCI in riabilitazione sia motoria che cognitiva, prestando particolare attenzione ai metodi utilizzati per l’elaborazione e traduzione del segnale EEG. Sono stati considerati con particolare attenzione studi che valutano gli effetti dell’applicazione di BCI non solo attraverso performance motorie e cognitive ma anche utilizzando tecniche di neuro-imaging avanzate, per indagare possibili cambiamenti nell’organizzazione funzionale della corteccia cerebrale sottostanti i risultati positivi ottenuti. Infine, vengono commentati i vantaggi e le limitazioni di queste tecnologie riabilitative e i problemi ancora aperti.
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Peterman, Robert B. "Exploring the human computer interface and photic driving." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999. http://handle.dtic.mil/100.2/ADA362006.
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Thesis (M.S. in Computer Science) Naval Postgraduate School, March 1999."March 1999". Thesis advisor(s): Timothy J. Shimeall. Appendix D is located in Restricted Resource Service. Includes bibliographical references (p. 95-96). Also available online.
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Bernard, Arnaud Jean Marc. "Human computer interface based on hand gesture recognition." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/42748.
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With the improvement of multimedia technologies such as broadband-enabled HDTV, video on demand and internet TV, the computer and the TV are merging to become a single device. Moreover the previously cited technologies as well as DVD or Blu-ray can provide menu navigation and interactive content.
The growing interest in video conferencing led to the integration of the webcam in different devices such as laptop, cell phones and even the TV set. Our approach is to directly use an embedded webcam to remotely control a TV set using hand gestures. Using specific gestures, a user is able to control the TV. A dedicated interface can then be used to select a TV channel, adjust volume or browse videos from an online streaming server.
This approach leads to several challenges. The first is the use of a simple webcam which leads to a vision based system. From the single webcam, we need to recognize the hand and identify its gesture or trajectory. A TV set is usually installed in a living room which implies constraints such as a potentially moving background and luminance change. These issues will be further discussed as well as the methods developed to resolve them. Video browsing is one example of the use of gesture recognition. To illustrate another application, we developed a simple game controlled by hand gestures.
The emergence of 3D TVs is allowing the development of 3D video conferencing. Therefore we also consider the use of a stereo camera to recognize hand gesture.
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Bashashati, Hossein. "A user-customized self-paced brain computer interface." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/61248.
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Much attention has been directed towards synchronous Brain Computer Interfaces (BCIs). For these BCIs, the user can only operate the system during specific system-defined periods. Self-paced BCIs, however, allow users to operate the system at any time he/she wishes. The classification of Electroencephalography (EEG) signals in self-paced BCIs is extremely challenging, as the BCI system does not have any clue about the start time of a control task. Also, the data contains a large number of periods during which the user has no intention to control the BCI.
For sensory motor self-paced BCIs (focus of this thesis), the brain of a user goes through several well-defined internal state changes while performing a mental task. Designing classifiers that exploit such temporal correlations in EEG data can enhance the performance of BCIs. It is also important to customize these BCIs for each user, because the brain characteristics of different people are not the same.
In this thesis, we first develop a unified comparison framework to compare the performance of different classifiers in sensory motor BCIs followed by rigorous statistical tests. This study is the largest of its kind as it has been performed on 29 subjects of synchronous and self-paced BCIs. We then develop a Bayesian optimization-based strategy that automatically customizes a synchronous BCI based on the brain characteristics of each individual subject. Our results show that our automated algorithm (which relies on less sophisticated feature extraction and classification methods) yields similar or superior results compared to the best performing designs in the literature.
We then propose an algorithm that can capture the time dynamics of the EEG signal for self-paced BCI systems. We show that this algorithm yields better results compared to several well-known algorithms, over 13 self-paced BCI subjects. Finally, we propose a fully automatic, scalable algorithm that customizes a self-paced BCI system based on the brain characteristics of each user and at the same time captures the dynamics of the EEG signal. Our final algorithm is an important step towards transitioning BCIs from research environments to real-life applications, where automatic, scalable and easy to use systems are needed.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Valsan, Gopal. "Brain computer interface using detection of movement intention." Thesis, University of Strathclyde, 2007. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=23482.
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Warne, Mark Robert. "Computer simulation of molecules at the kaolinite interface." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322604.
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Srinivasan, Sukumar. "Evolution of a Computer Aided Project Management Interface." The University of Arizona, 1992. http://hdl.handle.net/10150/555289.
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Osuagwu, Bethel Chikadibia A. "Neurorehabilitation of hand functions using brain computer interface." Thesis, University of Glasgow, 2015. http://theses.gla.ac.uk/7245/.
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Introduction: Brain computer interface (BCI) is a promising new technology with possible application in neurorehabilitation after spinal cord injury. Movement imagination or attempted movement-based BCI coupled with functional electrical stimulation (FES) enables the simultaneous activation of the motor cortices and the muscles they control. When using the BCI- coupled with FES (known as BCI-FES), the subject activates the motor cortex using attempted movement or movement imagination of a limb. The BCI system detects the motor cortex activation and activates the FES attached to the muscles of the limb the subject is attempting or imaging to move. In this way the afferent and the efferent pathways of the nervous system are simultaneously activated. This simultaneous activation encourages Hebbian type learning which could be beneficial in functional rehabilitation after spinal cord injury (SCI). The FES is already in use in several SCI rehabilitation units but there is currently not enough clinical evidence to support the use of BCI-FES for rehabilitation. Aims: The main aim of this thesis is to assess outcomes in sub-acute tetraplegic patients using BCI-FES for functional hand rehabilitation. In addition, the thesis explores different methods for assessing neurological rehabilitation especially after BCI-FES therapy. The thesis also investigated mental rotation as a possible rehabilitation method in SCI. Methods: Following investigation into applicable methods that can be used to implement rehabilitative BCI, a BCI based on attempted movement was built. Further, the BCI was used to build a BCI-FES system. The BCI-FES system was used to deliver therapy to seven sub-acute tetraplegic patients who were scheduled to receive the therapy over a total period of 20 working days. These seven patients are in a 'BCI-FES' group. Five more patients were also recruited and offered equivalent FES quantity without the BCI. These further five patients are in a 'FES-only' group. Neurological and functional measures were investigated and used to assess both patient groups before and after therapy. Results: The results of the two groups of patients were compared. The patients in the BCI-FES group had better improvements. These improvements were found with outcome measures assessing neurological changes. The neurological changes following the use of the BCI-FES showed that during movement attempt, the activation of the motor cortex areas of the SCI patients became closer to the activation found in healthy individuals. The intensity of the activation and its spatial localisation both improved suggesting desirable cortical reorganisation. Furthermore, the responses of the somatosensory cortex during sensory stimulation were of clear evidence of better improvement in patients who used the BCI-FES. Missing somatosensory evoked potential peaks returned more for the BCI-FES group while there was no overall change in the FES-only group. Although the BCI-FES group had better neurological improvement, they did not show better functional improvement than the FES-only group. This was attributed mainly to the short duration of the study where therapies were only delivered for 20 working days. Conclusions: The results obtained from this study have shown that BCI-FES may induce cortical changes in the desired direction at least faster than FES alone. The observation of better improvement in the patients who used the BCI-FES is a good result in neurorehabilitation and it shows the potential of thought-controlled FES as a neurorehabilitation tool. These results back other studies that have shown the potential of BCI-FES in rehabilitation following neurological injuries that lead to movement impairment. Although the results are promising, further studies are necessary given the small number of subjects in the current study.
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Johansson, Bo. "Generellt interface till Javadoc." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-57.
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Syftet med detta arbete var att undersöka möjligheten att få ett väl fungerande interface mellan ©VizzAnalyzer [3] och Javadoc, och därmed kunna producera htmldokumentation av de analyserade klasserna. VizzAnalyzer analyserar javaprogram och producerar därvid ett abstrakt syntaxträd (AST) som representerar det analyserade programmet. Detta AST kan sedan kopplas till javadocs interna AST-modell för att representera de klasser som förekommer vid analysen. Resultatet av detta blir då en komplett dokumentation av de analyserade klasserna. Det finns även möjligheter att dynamiskt lägga in ytterligare information om dem som kommit fram vid analysen. Utöver interfacet till VizzAnalyzer gjordes också ett mer generellt interface som kan kopplas till vilket program som helst som kan producera en lämplig representation av ett analyserat program. Detta gäller då främst javakod, men i och med att klasser, metoder, fält mm. enbart är representerade som text innan de behandlas av javadocinterfacet, finns det också möjligheter att använda det för andra objektorienterade språk.
The purpose of this work was to examine the possibility to get a well functioning interface between ©VizzAnalyzer [3] and Javadoc, thereby be able to produce html documentation of the analysed classes. The VizzAnalyzer analyses java programs and so produces an abstract syntax tree (AST) that represents the analysed program. This AST can then be coupled to the internal AST model of javadoc to represent the classes that were part of the analysis. The result of this is a complete documentation of the analysed classes. There is also the possibility to add further information about them that may have been discovered during analysis. A more general interface was also made in addition to the interface for VizzAnalyzer. This interface can be coupled with any program that can produce a suitable representation of the analysed program. This is mostly pertinent to java code, but due to classes being represented only as text before they are processed by the interface, there is a possibility to use it for other object oriented languages.
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Olatidoye, Olugbemiga A. "Design-oriented graphic-user-interface." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/23110.
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Dillon, Andrew. "User interface design." London: Macmillan, 2003. http://hdl.handle.net/10150/105299.
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This item is not the definitive copy. Please use the following citation when referencing this material: Dillon, A. (2003) User Interface Design. MacMillan Encyclopedia of Cognitive Science, Vol. 4, London:MacMillan, 453-458. Article definition: This article covers the basic issues that the field of cognitive science raises in the design and testing of new digital technologies for human use. Contents list: Introduction, Cognitive Science and design, The Basics of Human-Computer Interaction, Cognitive Design Guidelines: from psychophysics to semiotics, Beyond guidelines Cognitive theories and models in HCI, Developing user-centered design methods, Summary, Bibliography, Glossary
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Li, Bin. "An interface between single assignment C and vector pascal." Connect to e-thesis, 2007. http://theses.gla.ac.uk/107/.
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Thesis (Ph.D.) - University of Glasgow, 2007.Ph.D. thesis submitted to the Department of Computing Science, Faculty of Information and Mathematical Sciences, University of Glasgow, 2007. Includes bibliographical references. Print version also available.
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Hecker, Gabriele A. "A non-normal form database interface." Thesis, Kansas State University, 1986. http://hdl.handle.net/2097/9917.
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Altiero, Roberto A. "Digital Forensics Tool Interface Visualization." NSUWorks, 2015. http://nsuworks.nova.edu/gscis_etd/24.
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Recent trends show digital devices utilized with increasing frequency in most crimes committed. Investigating crime involving these devices is labor-intensive for the practitioner applying digital forensics tools that present possible evidence with results displayed in tabular lists for manual review. This research investigates how enhanced digital forensics tool interface visualization techniques can be shown to improve the investigator's cognitive capacities to discover criminal evidence more efficiently. This paper presents visualization graphs and contrasts their properties with the outputs of The Sleuth Kit (TSK) digital forensic program. Exhibited is the textual-based interface proving the effectiveness of enhanced data presentation. Further demonstrated is the potential of the computer interface to present to the digital forensic practitioner an abstract, graphic view of an entire dataset of computer files. Enhanced interface design of digital forensic tools means more rapidly linking suspicious evidence to a perpetrator.
Introduced in this study is a mixed methodology of ethnography and cognitive load measures. Ethnographically defined tasks developed from the interviews of digital forensics subject matter experts (SME) shape the context for cognitive measures. Cognitive load testing of digital forensics first-responders utilizing both a textual-based and visualized-based application established a quantitative mean of the mental workload during operation of the applications under test. A t-test correlating the dependent samples' mean tested for the null hypothesis of less than a significant value between the applications' comparative workloads of the operators. Results of the study indicate a significant value, affirming the hypothesis that a visualized application would reduce the cognitive workload of the first-responder analyst. With the supported hypothesis, this work contributes to the body of knowledge by validating a method of measurement and by providing empirical evidence that the use of the visualized digital forensics interface will provide a more efficient performance by the analyst, saving labor costs and compressing time required for the discovery phase of a digital investigation.
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BRATT, JESPER, and ALEXANDER WILCZEK. "USAR-Robot Interface Evaluation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-136299.
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Urban Search And Rescue (USAR) is something that is utilized more frequently in modern society due to the advancements made in the robotics field. Being able to use a robot to search for survivors in urban environments after disasters is invaluable. The fact that robots are able to endure hazardous areas significantly reduces response times when searching for survivors, and, in turn increasing chances of finding and rescuing victims. A major contribution to why robots have yet to dominate USAR is the fact that the robot interfaces have not evolved at the same pace that the robots have. Being able to intuitively instruct the robots is a core functionality that could be improved. Creating and implementing such an interface is not an easy task however, which is why research has to be conducted in order to find out what really makes a well functioning interface. In this case has a USAR robot interface not only been created and implemented in different varieties, but user testing sessions have also been conducted in order to properly determine the requirements of a well functioning interface. The results of these tests have shown that some factors appear to be more important than others, but also that appearances can be deceiving and that none of these factors are in fact without its own importance.Urban Search And Rescue (USAR) som på svenska kan översättas till sökning och räddning i stadsmiljö, är något som på senare tid allt oftare används i dagens samhälle på grund av framstegen gjorda inom robotiken. Att kunna använda en robot för att söka efter överlevande i stadsmiljöer efter katastrofer är ovärdeligt. Faktumet att robotar kan hantera farligare områden än vad människor kan kortar ner tiden för att hitta överlevande avsevärt, och ökar samtidigt chansen att hitta överlevande offer. En stor bidragande faktor till varför robotar inte används överallt inom detta område är faktumet att gränssnittet som används inte har utvecklats i samma takt som robotarna har. Att intuitivt kunna instruera robotarna vad de ska utföra är en kärnfunktion som väsentligt kan förbättras. Att skapa och implementera ett gränssnitt är däremot inget lätt åtagande. Detta leder till att forskning behöver genomföras för att ta reda på vad ett funktionellt gränssnitt innebär. I detta fall har USAR-robot gränssnittet inte bara skapats, utan även implementerats i olika versioner. Användartester har använts för att kunna bestämma vad kraven för ett välfungerande gränssnitt är. Resultaten av dessa test har visat att vissa faktorer verkar vara viktigare än andra, men de påvisar även att allt inte alltid är som det verkar.
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Hohmann, Sandra. "Mensch - Maschine - Interface : Studien zu einer Theorie der Mensch-Computer-Interaktion / Men - Machine - Interface : Studies towards a theory of human-computer-interaction." [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=968823734.
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Palivela, Yaswanth. "Speech Assisted Interface for Quadcopter Flight Control." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1526247041269609.
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Carlsson, Ulla. "A general interface for computer control in real time." Thesis, University West, Department of Technology, Mathematics and Computer Science, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-388.
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Genc, Serkan. "Vision-based Hand Interface Systems In Human Computer Interaction." Phd thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12611700/index.pdf.
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People began to interact with their own environment since their birth. Their main organs to sense their surroundings are their hands, and this is the most natural way of interaction in human-human interactions. The goal of this dissertation is to
enable users to employ their hands in interaction with computers similar to human-human interaction. Using hands in the computer interaction increases both the naturalness of computer usage and the speed of interaction. One way of
accomplishing this goal is to utilize computer vision methods to develop hand interfaces. In this study, a regular, low-cost camera is used for image acquisition, and the images from camera are processed by our novel vision system to detect user intention. The contributions are (i) a method for interacting with a screen without touching in a distributed computer system is proposed, (ii) a benchmark of four hand
shape representation methods is performed using a comprehensive hand shape video database, and (iii) a vison-based hand interface is designed for an application that
queries a video database system, and its usability and performances are also assessed by a group of test users to determine its suitability for the application.
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Duncan, Alexander A. "EEG pattern classification for the brain-computer musical interface." Thesis, University of Glasgow, 2001. http://theses.gla.ac.uk/3709/.
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Polak, Mark John. "Adaptive logic networks in a brain-computer interface system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0012/NQ59652.pdf.
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McCreadie, Karl A. "Musical spacialised auditory feedback for a brain-computer interface." Thesis, Ulster University, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.633652.
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A brain-computer interface (BCI) offers an alternative method of communication and control for those who have suffered neuromuscular damage due to disease or injury. Imagined movement can be used to modulate the brain activity of an individual which may then be translated into a control signal which is regulated using sensory feedback. However, this feedback is predominantly presented visually and excludes those with a disability coupled with vision problems, a typical BCI beneficiary target group. Presenting feedback using an alternative sensory pathway, such as hearing, can allow for a more inclusive method of control. Additionally, the diagnosis of those with disorders of consciousness (DOC) can be problematic due to the lack of overt motor responses and inherent ocular instabilities. Moreover, auditory BCI allows the possibility of freeing up the visual channel in able-bodied users permitting other tasks. Although audio based BCI systems exist, variations in the methods of auditory feedback presentation have not been fully investigated. There is a clear need for an auditory motor imagery based BCI which has the potential not only to offer an alternative communication channel for the disabled, but also an additional method of assessing and training those with a DOC. Firstly, the most suitable form of auditory feedback is investigated before a between group comparison study examines the differences in performance comparing visual and auditory able-bodied feedback groups (N=20) when presented with stereophonic broadband noise. Prior to this research stereophonic auditory sensorimotor feedback with broadband noise had not been tested. Secondly, performance of able-bodied participants is assessed in a within group study (N=7) where the amount of spatial information is varied. Additionally, the development of an auditory asteroids avoidance game is detailed and the performance compared both with a visual equivalent and with other auditory presentation methods. All studies to this point present audio through loudspeakers, but a subsequent study including two patients with a DOC, shows results when presenting musical feedback through earphones, which is paralleled with results obtained using broadband noise. Subsequently a modified earphone presentation method including spatial cues is used to present musical feedback to two able-bodied participants to assess performance. Results show that stereo pink noise is a suitable replacement for the visual equivalent, that there is no statistical difference in performance when spatial information is varied, and that there is a correlation between certain psychological, factors and performance. This has implications not only for those previously mentioned, but also for those with conditions resulting in a reduction in information from the traditional pathways allowing for a possible sensory extension. Finally, suggestions are made to other possibly fruitful research directions, including the need for user specific music libraries which have shown to enhance engagement and could improve BCI performance in future developments, the potential for crowd sourced datasets, the prospect for an eyes-closed system, and highlights the need for a greater focus on training the mind rather than just training the system.
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Clanton, Samuel T. "Brain-Computer Interface Control of an Anthropomorphic Robotic Arm." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/170.
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This thesis describes a brain-computer interface (BCI) system that was developed to allow direct cortical control of 7 active degrees of freedom in a robotic arm. Two monkeys with chronic microelectrode implants in their motor cortices were able to use the arm to complete an oriented grasping task under brain control. This BCI system was created as a clinical prototype to exhibit (1) simultaneous decoding of cortical signals for control of the 3-D translation, 3-D rotation, and 1-D finger aperture of a robotic arm and hand, (2) methods for constructing cortical signal decoding models based on only observation of a moving robot, (3) a generalized method for training subjects to use complex BCI prosthetic robots using a novel form of operator-machine shared control, and (4) integrated kinematic and force control of a brain-controlled prosthetic robot through a novel impedance-based robot controller. This dissertation describes each of these features individually, how their integration enriched BCI control, and results from the monkeys operating the resulting system.
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Arico', Pietro <1985>. "Mental states monitoring through passive Brain-Computer Interface systems." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6557/.
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The monitoring of cognitive functions aims at gaining information about the current cognitive state of the user by decoding brain signals. In recent years, this approach allowed to acquire valuable information about the cognitive aspects regarding the interaction of humans with external world. From this consideration, researchers started to consider passive application of brain–computer interface (BCI) in order to provide a novel input modality for technical systems solely based on brain activity. The objective of this thesis is to demonstrate how the passive Brain Computer Interfaces (BCIs) applications can be used to assess the mental states of the users, in order to improve the human machine interaction. Two main studies has been proposed. The first one allows to investigate whatever the Event Related Potentials (ERPs) morphological variations can be used to predict the users’ mental states (e.g. attentional resources, mental workload) during different reactive BCI tasks (e.g. P300-based BCIs), and if these information can predict the subjects’ performance in performing the tasks. In the second study, a passive BCI system able to online estimate the mental workload of the user by relying on the combination of the EEG and the ECG biosignals has been proposed. The latter study has been performed by simulating an operative scenario, in which the occurrence of errors or lack of performance could have significant consequences. The results showed that the proposed system is able to estimate online the mental workload of the subjects discriminating three different difficulty level of the tasks ensuring a high reliability.La valutazione delle funzioni cognitive ha l’obbiettivo di ottenere informazioni sullo stato mentale attuale dell'utente, attraverso la decodifica dei segnali cerebrali. Negli ultimi anni, questo approccio ha consentito di indagare informazioni preziose sugli aspetti cognitivi riguardanti l'interazione tra l’uomo ed il mondo esterno. In base a queste considerazioni, recentemente si è considerata in letteratura la possibilità di utilizzare le interfacce cervello computer passive (BCI passivi) per interagire con dispositivi esterni, sfruttando l’attività spontanea dell’utente. L'obiettivo di questa tesi è quello di dimostrare come le interfacce cervello computer passive possano essere utilizzate per valutare lo stato mentale dell’utente, al fine di migliorare l'interazione uomo-macchina. Sono stati presentati due studi principali. Il primo ha l’obbiettivo di investigare le variazioni morfologiche dei potenziali evento correlati (ERP), al fine di associarle agli stati mentali dell’utente (es. attenzione, carico di lavoro mentale) durante l’utilizzo di BCI reattive, e come predittori delle performance raggiunte dai soggetti. Nel secondo studio è stato sviluppato e validato un sistema BCI passivo in grado di stimare il carico di lavoro mentale dell'utente durante task operative, attraverso la combinazione del segnale elettroencefalografico (EEG) ed elettrocardiografico (ECG). Quest'ultimo studio è stato effettuato simulando uno scenario operativo, in cui il verificarsi di errori da parte dell’operatore o il calo di prestazioni poteva avere conseguenze importanti. I risultati hanno mostrato che il sistema proposto è in grado di discriminare il carico di lavoro mentale percepito dall’utente su tre livelli di difficoltà, garantendo un’elevata affidabilità.