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Journal articles on the topic 'Computer interfaces'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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1

Pepperberg, Irene M. "Animal-computer interfaces." Interaction Studies 24, no. 2 (November 3, 2023): 193–200. http://dx.doi.org/10.1075/is.23018.pep.

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Abstract The field of animal-computer interfaces has a longer history than one might at first suppose. In this Introduction, I first discuss some of the early attempts to integrate computers into the study of animal cognition, communication, and behavior and how they provided the groundwork for subsequent research in nonhuman-computer interfaces. I then summarize the various contributions to this special issue, emphasizing how they provide a snapshot into the current state of the field. I close by emphasizing the value of this work but also by suggesting some potential pitfalls of which we must also be aware.

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Allan, K. "Inspiring interfaces [computer game interfaces]." Engineering & Technology 2, no. 5 (May 1, 2007): 34–36. http://dx.doi.org/10.1049/et:20070503.

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3

Bartz, Christina. "Der Computer in der Küche." Zeitschrift für Medien- und Kulturforschung 9, no. 2 (2018): 13–26. http://dx.doi.org/10.28937/1000108172.

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Der Honeywell Kitchen Computer von 1969 ist einer der ersten Rechner, der für den Heimgebrauch hergestellt wurde. Schon allein aufgrund seines wenig benutzerfreundlichen Interfaces, das im Widerspruch zur nicht-professionellen Nutzung in der häuslichen Sphäre steht, stellt er eine Kuriosität dar. Zugleich weist er Aspekte auf, die die Idee eines Computers zu Hause plausibilisieren. Dazu gehört u.a. die Gestaltung des Interfaces, aber auch die Küche als Ort der heimischen Arbeit. In 1969, the Honeywell Kitchen Computer was the first data processor that was built explicitly for home use. Resembling something of an oddity, most of all because of its non-user-friendly interface that conflicts with the conditions of non-professional domestic use, the Honeywell Kitchen Computer at the same time shows some aspects which make the use of a computer at home plausible, i. a. the design of the interface and the factor of a kitchen being the place of domestic work

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Bogdanova, Nellija. "PRINCIPLES OF USER-CENTERED DESIGN." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 1 (June 20, 2001): 245. http://dx.doi.org/10.17770/etr2001vol1.1921.

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Good user interfaces are essential for any successful product. A process of the user interface creation is not available include in the algorithmic scheme. In this articles will formulate principles principles o f user-centered design, criteria o f ergonomics interfaces and efficient interface’s rules of project. These principles are based usability computer training courses.

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Li, Jiayi. "Brain-computer interface for the treatment of mental illness." Theoretical and Natural Science 16, no. 1 (December 4, 2023): 93–96. http://dx.doi.org/10.54254/2753-8818/16/20240539.

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A brain-computer interface is a direct communication channel between the brain and external devices. Its signals come from the central nervous system, and its transmission is independent of the peripheral nervous and muscular systems. Brain-computer interface commonly used to assist, enhance, and repair human-motor sensations. Through the classification and recognition of Electroencephalogram (EEG) signals, the monitoring and rehabilitation of some neurological and psychological diseases can be realized. Brain-computer interfaces are currently in their infancy and are being explored. Non-invasive brain-computer interfaces refer to brain-computer interfaces that are performed in the cerebral cortex. Semi-invasive brain-computer interfaces are what the chip penetrates the cerebral cortex but does not penetrate the gray matter in the brain. An immersive brain-computer interface is when the chip penetrates the gray matter of the brain. At present, Brain-computer interfaces with chips implanted in the head are mainly located in the brain. Brain-computer interfaces located in the cerebellum, brainstem and other parts have not made significant breakthroughs. The brain-computer interface first collects signals transmitted by the brain, then sequences and encodes them . Finally, transfer them to the computer. The computer controls the robotic arm through the acquired signals and finally completes the instructions. This article focuses on invasive and semi-invasive brain-computer interfaces, using case studies, combinatorial studies, and other methods. This research can help patients live better, improve patients' quality of life, and promote future research in bioelectronics and organic electronics.

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Peters, Gabriele. "Criteria for the Creation of Aesthetic Images for Human-Computer Interfaces A Survey for Computer Scientists." International Journal of Creative Interfaces and Computer Graphics 2, no. 1 (January 2011): 68–98. http://dx.doi.org/10.4018/jcicg.2011010105.

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Interaction in modern human-computer interfaces is most intuitively initiated in an image-based way. Often images are the key components of an interface. However, too frequently, interfaces are still designed by computer scientists with no explicit education in the aesthetic design of interfaces and images. This article develops a well-defined system of criteria for the aesthetic design of images, motivated by principles of visual information processing by the human brain and by considerations of the visual arts. This theoretic disquisition establishes a framework for the evaluation of images in terms of aesthetics and it serves as a guideline for interface designers by giving them a collection of criteria at hand; how to deal with images in terms of aesthetics for the purpose of developing better user interfaces. The proposed criteria are exemplified by an analysis of the images of the web interfaces of four well known museums.

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Williams, Evelyn, and Evelyn Hewlett-Packard. "Panel on Visual Interface Design." Proceedings of the Human Factors Society Annual Meeting 33, no. 5 (October 1989): 323–24. http://dx.doi.org/10.1177/154193128903300519.

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User interface design has many components. Usable computer interfaces should be easy to learn, result in high user productivity and high user satisfaction. There are a number of components in user interface design that affect the usability of the interface. Within the human factors community we tend to emphasize the ergonomic and cognitive components of the computer interface. There is another component that is frequently ignored, the visual interface design. This panel will present information on the visual component in various user-computer interfaces and will discuss the contributions of the visual designer to the interfaces and usability.

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Young, Michael J., David J. Lin, and Leigh R. Hochberg. "Brain–Computer Interfaces in Neurorecovery and Neurorehabilitation." Seminars in Neurology 41, no. 02 (March 19, 2021): 206–16. http://dx.doi.org/10.1055/s-0041-1725137.

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AbstractRecent advances in brain–computer interface technology to restore and rehabilitate neurologic function aim to enable persons with disabling neurologic conditions to communicate, interact with the environment, and achieve other key activities of daily living and personal goals. Here we evaluate the principles, benefits, challenges, and future directions of brain–computer interfaces in the context of neurorehabilitation. We then explore the clinical translation of these technologies and propose an approach to facilitate implementation of brain–computer interfaces for persons with neurologic disease.

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Gao, Xiaorong, Yijun Wang, Xiaogang Chen, and Shangkai Gao. "Interface, interaction, and intelligence in generalized brain–computer interfaces." Trends in Cognitive Sciences 25, no. 8 (August 2021): 671–84. http://dx.doi.org/10.1016/j.tics.2021.04.003.

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10

Chao, Dennis L. "Computer games as interfaces." Interactions 11, no. 5 (September 2004): 71–72. http://dx.doi.org/10.1145/1015530.1015567.

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Shirer, D. L. "Versatile Laboratory Computer Interfaces." Computing in Science & Engineering 3, no. 6 (November 2001): 9–13. http://dx.doi.org/10.1109/mcise.2001.963422.

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12

Benson, Philippa J. "Decoding brain-computer interfaces." Science 360, no. 6389 (May 10, 2018): 615.8–616. http://dx.doi.org/10.1126/science.360.6389.615-h.

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13

Kroeker, Kirk L. "Improving brain-computer interfaces." Communications of the ACM 54, no. 10 (October 2011): 11–14. http://dx.doi.org/10.1145/2001269.2001275.

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Molina, Gary Garcia, Tsvetomira Tsoneva, and Anton Nijholt. "Emotional brain-computer interfaces." International Journal of Autonomous and Adaptive Communications Systems 6, no. 1 (2013): 9. http://dx.doi.org/10.1504/ijaacs.2013.050687.

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Tribble, Dennis A., and Ralph A. Korpman. "Interfaces between computer systems." American Journal of Health-System Pharmacy 52, no. 5 (March 1, 1995): 524–28. http://dx.doi.org/10.1093/ajhp/52.5.524.

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Maye, Alexander, Dan Zhang, Yijun Wang, Shangkai Gao, and Andreas K. Engel. "Multimodal brain-computer interfaces." Tsinghua Science and Technology 16, no. 2 (April 2011): 133–39. http://dx.doi.org/10.1016/s1007-0214(11)70020-7.

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Dutoit, Thierry, Laurence Nigay, and Michael Schnaider. "Multimodal human–computer interfaces." Signal Processing 86, no. 12 (December 2006): 3515–17. http://dx.doi.org/10.1016/j.sigpro.2006.03.031.

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18

Berger, Theodore W. "Brain–Computer Interfaces (BCIs)." Journal of Neuroscience Methods 167, no. 1 (January 2008): 1. http://dx.doi.org/10.1016/j.jneumeth.2007.10.002.

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Sitaram, R., N. Weiskopf, A. Caria, R. Veit, M. Erb, and N. Birbaumer. "fMRI Brain-Computer Interfaces." IEEE Signal Processing Magazine 25, no. 1 (2008): 95–106. http://dx.doi.org/10.1109/msp.2008.4408446.

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20

Patel, Neel S., and Darin E. Hughes. "Revolutionizing human-computer interfaces." Interactions 19, no. 1 (January 2012): 34–37. http://dx.doi.org/10.1145/2065327.2065336.

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21

R, Pushpakumar, Karun Sanjaya, S. Rathika, Ahmed Hussein Alawadi, Khamdamova Makhzuna, S. Venkatesh, and B. Rajalakshmi. "Human-Computer Interaction: Enhancing User Experience in Interactive Systems." E3S Web of Conferences 399 (2023): 04037. http://dx.doi.org/10.1051/e3sconf/202339904037.

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Enhancing user experience (UX) in interactive systems requires effective human-computer interaction (HCI). The relationship between people and computers has grown in significance as technology progresses, having an impact on many areas of our life. The main ideas and tactics used in HCI to enhance the user experience in interactive systems are examined in this abstract. Understanding the capabilities and constraints of both humans and computers forms the basis of HCI. HCI researchers and designers may develop interactive systems that complement users' mental models and cognitive processes by researching human behavior, cognition, and psychology. Additionally, taking into account the features of the computer system, such as its responsiveness, processing capacity, and interface design, enables the development of systems that are more user-friendly and effective. As a result, the discipline of human-computer interaction works to improve the user experience in interactive systems. Researchers and designers in the field of human-computer interaction (HCI) can produce interactive systems that are simple, effective, entertaining, and satisfying for users by comprehending human capabilities, applying user-centered design principles, utilizing interactive techniques and interfaces, integrating multimodal interfaces, and embracing emerging technologies. The continued development of HCI will continue to influence and enhance how people use computers, enhancing user experiences and creating new opportunities for interactive systems in the future.

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MANARIS, BILL Z. "AN ENGINEERING ENVIRONMENT FOR NATURAL LANGUAGE INTERFACES TO INTERACTIVE COMPUTER SYSTEMS." International Journal on Artificial Intelligence Tools 03, no. 04 (December 1994): 557–79. http://dx.doi.org/10.1142/s0218213094000303.

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This paper discusses the development of natural language interfaces to interactive computer systems using the NALIGE user interface management system. The task of engineering such interfaces is reduced to producing a set of well-formed specifications which describe lexical, syntactic, semantic, and pragmatic aspects of the selected application domain. These specifications are converted by NALIGE to an autonomous natural language interface that exhibits the prescribed linguistic and functional behavior. Development of several applications is presented to demonstrate how NALIGE and the associated development methodology may facilitate the design and implementation of practical natural language interfaces. This includes a natural language interface to Unix and its subsequent porting to MS-DOS, VAX/VMS, and VM/CMS; a natural language interface for Internet navigation and resource location; a natural language interface for text pattern matching; a natural language interface for text editing; and a natural language interface for electronic mail management. Additionally, design issues and considerations are identified/addressed, such as reuse and portability, content coupling, morphological processing, scalability, and habitability.

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23

Hix, Deborah. "Assessment of an Interactive Environment for Developing Human-Computer Interfaces." Proceedings of the Human Factors Society Annual Meeting 30, no. 14 (September 1986): 1349–53. http://dx.doi.org/10.1177/154193128603001401.

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The goal of this research was to empirically evaluate the usefulness of an interactive environment for developing human-computer interfaces. In particular, it focused on a set of interactive tools, called the Author's Interactive Dialogue Environment (AIDE), for human-computer interface implementation. AIDE is used by an interface design specialist, called a dialogue author, to implement an interface by directly manipulating and defining its objects, rather than by the traditional method of writing source code. In a controlled experiment, a group of dialogue author subjects used AIDE 1.0 to implement a predefined interface, and a group of application programmer subjects implemented the identical interface using programming code. Dialogue author subjects performed the task more than three times faster than the application programmer subjects. This study empirically supports, possibly for the first time, the long-standing claim that interactive tools for interface development can improve productivity and reduce frustration in developing interfaces over traditional programming techniques for interface development.

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Cioczek, Michał, Tomasz Czarnota, and Tomasz Szymczyk. "Analysis of modern human-computer interfaces." Journal of Computer Sciences Institute 18 (March 30, 2021): 22–29. http://dx.doi.org/10.35784/jcsi.2403.

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This article describes two research methods that are currently used in the study of graphical interfaces. The examined aspect is human-computer interaction (HCI), which is carried out by means of manipulators, which are input devices, and by means of which the tester performs the tasks set in the research scenario, which are presented using a graphical interface (GUI). The analysis covers the path the cursor follows, its speed and time. The path that the cursor takes is also drawn, and it is divided into stages because there are intermediate elements between the start and end elements. Due to the fact that it is impossible to describe numerically the feelings of the examined person, and these feelings are important for the study, the so-called usability tests, in which, among others, the ergonomics of controllers and the graphic interface itself was examined.

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Лунев, Д. В., С. К. Полетыкин, and Д. О. Кудрявцев. "Brain-computer interfaces: technology overview and modern solutions." Современные инновации, системы и технологии - Modern Innovations, Systems and Technologies 2, no. 3 (July 12, 2022): 0117–26. http://dx.doi.org/10.47813/2782-2818-2022-2-3-0117-0126.

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The purpose of this study is to provide an overview of the current state of neural interface technology and to compare their various modern implementations with each other, highlighting their advantages and features. The article considers the essence of the concept of "neural interface", its purpose, disassembled the structure of this technology and the principles underlying it, as well as classification according to various criteria. Examples of areas of activity in which this technology is currently used or can potentially be applied in the future are given. In addition, the most commonly used modern solutions are collected and analyzed in order to identify the most promising option in terms of functionality and convenience of everyday use. It has been established that the Emotiv Epoc neural interface has the widest functionality with comfortable everyday wear. It was also concluded that the areas of application in which solutions based on neural interfaces currently show the best results are medical diagnostics and remote control of electronic devices, as evidenced by the large number of projects involving neural interfaces in this area and a large number of articles, dedicated to them.

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Gundelakh, Filipp, Lev Stankevich, Konstantin Sonkin, Ganna Nagornova, and Natalia Shemyakina. "Application of Brain-computer Interfaces in Assistive Technologies." SPIIRAS Proceedings 19, no. 2 (April 23, 2020): 277–301. http://dx.doi.org/10.15622/sp.2020.19.2.2.

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In the paper issues of brain-computer interface applications in assistive technologies are considered in particular for robotic devices control. Noninvasive brain-computer interfaces are built based on the classification of electroencephalographic signals, which show bioelectrical activity in different zones of the brain. Such brain-computer interfaces after training are able to decode electroencephalographic patterns corresponding to different imaginary movements and patterns corresponding to different audio-visual stimulus. The requirements which must be met by brain-computer interfaces operating in real time, so that biological feedback is effective and the user's brain can correctly associate responses with events are formulated. The process of electroencephalographic signal processing in noninvasive brain-computer interface is examined including spatial and temporal filtering, artifact removal, feature selection, and classification. Descriptions and comparison of classifiers based on support vector machines, artificial neural networks, and Riemann geometry are presented. It was shown that such classifiers can provide accuracy at the level of 60-80% for recognition of imaginary movements from two to four classes. Examples of application of the classifiers to control robotic devices were presented. The approach is intended both to help healthy users to perform daily functions better and to increase the quality of life of people with movement disabilities. Tasks to increase the efficiency of technology application are formulated.

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Lee, Matias, and Pedro R. D'Argenio. "Describing Secure Interfaces with Interface Automata." Electronic Notes in Theoretical Computer Science 264, no. 1 (August 2010): 107–23. http://dx.doi.org/10.1016/j.entcs.2010.07.008.

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Fitzgerald, Will, and R. James Firby. "Dialogue-based human-computer interfaces and active language understanding." International Journal of Cognition and Technology 1, no. 2 (December 31, 2002): 275–86. http://dx.doi.org/10.1075/ijct.1.2.04fit.

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Recent developments in speech, network and embedded-computer technologies indicate that human–computer interfaces that use speech as one or the main mode of interaction will become increasingly prevalent. Such interfaces must move beyond simple voice commands to support a dialogue-based interface if they are to provide for common requirements such as description resolution, perceptual anchoring, and deixis. To support human–computer dialogue effectively, architectures must support active language understanding: that is, they must support the close integration of dialogue planning and execution with general task planning and execution.

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Turovskiy, Ya A., A. V. Mamaev, A. V. Alekseev, and S. V. Borzunov. "Subjective time scales when working with perspective human-computer interfaces." Experimental Psychology (Russia) 12, no. 2 (2019): 75–86. http://dx.doi.org/10.17759/exppsy.2019120206.

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The analysis of subjective time scales of the subjects with perspective human-computer interfaces was carried out: neurocomputer (brain-computer), electromyographic, oculografic. It is shown that for all of them it is typical to underestimate the maximum time spent for the execution of one team. In this case, for the electromyographic and oculografic, this feature is also preserved for the indicators of the average time for executing the commands. The results of the assessment demonstrate a unified approach of users to the formation of subjective time when working with various interfaces: the user estimates both the averaged and the best (minimum) with the worst (maximum) time for executing commands on a single scale. Subjects who switched worse from generating one command for the interface to another subjectively rated the interface as slower. The HRV data showed the LF-band relationship with a subjective estimate of the time spent working with the interface. Analysis of the relationship (true time-subjective) / true time has shown that subjective time scales when working with the neurocomputer and oculographic interfaces demonstrate a high correlation with each other as opposed to electromyographic.

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Colman, Jason, and Paul Gnanayutham. "Accessible Button Interfaces." International Journal of Web-Based Learning and Teaching Technologies 7, no. 4 (October 2012): 40–52. http://dx.doi.org/10.4018/jwltt.2012100104.

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The number of people with brain injuries is increasing, as more people who suffer injuries survive. Some of these patients are aware of their surroundings but almost entirely unable to move or communicate. Brain-Computer Interfaces (BCIs) can enable this group of people to use computers to communicate and carry out simple tasks in a limited manner. BCIs tend to be hard to navigate in a controlled manner, and so the use of “one button” user interfaces is explored. This one button concept can not only be used brain injured personnel with BCIs but by other categories of disabled individuals too with alternative point and click devices. A number of accessible button interfaces are described, some of which have already been implemented by the authors.

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Paula Ivone, Rodríguez-Azar. "Brain-computer interface applications to decrease phantom limb pain." Open Journal of Pain Medicine 7, no. 1 (April 13, 2023): 019–21. http://dx.doi.org/10.17352/ojpm.000035.

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Phantom limb pain is mainly experienced in people who have suffered limb amputation, its study and analysis are relevant to know the causes and help patients to reduce pain. On the other hand, brain-computer interfaces encode brain signals and decode them for communication with a device. Through the motor imagination of the movement of the amputated limb and with the help of a brain-computer interface, an amputee patient can reduce or eliminate pain. The objective of this paper is to give an introduction to brain-computer interfaces as a tool for the reduction of phantom limb pain. Therefore, the applications of interfaces in relation to pain are highlighted, some related studies are discussed, and their benefits and disadvantages are analyzed. Finally, it is established that phantom limb pain research benefits from incorporating brain-computer interfaces as part of non-pharmaceutical therapy because it is a tool that provides feedback on the reconfiguration of the cerebral cortex in relation to pain.

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Aricò, Pietro, Nicolina Sciaraffa, and Fabio Babiloni. "Brain–Computer Interfaces: Toward a Daily Life Employment." Brain Sciences 10, no. 3 (March 9, 2020): 157. http://dx.doi.org/10.3390/brainsci10030157.

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Recent publications in the Electroencephalogram (EEG)-based brain–computer interface field suggest that this technology could be ready to go outside the research labs and enter the market as a new consumer product. This assumption is supported by the recent advantages obtained in terms of front-end graphical user interfaces, back-end classification algorithms, and technology improvement in terms of wearable devices and dry EEG sensors. This editorial paper aims at mentioning these aspects, starting from the review paper “Brain–Computer Interface Spellers: A Review” (Rezeika et al., 2018), published within the Brain Sciences journal, and citing other relevant review papers that discussed these points.

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Bonci, Andrea, Simone Fiori, Hiroshi Higashi, Toshihisa Tanaka, and Federica Verdini. "An Introductory Tutorial on Brain–Computer Interfaces and Their Applications." Electronics 10, no. 5 (February 27, 2021): 560. http://dx.doi.org/10.3390/electronics10050560.

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The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brain–computer interfaces (such as reliable acquisition of brain signals, filtering and processing of the acquired brainwaves, ethical and legal issues related to brain–computer interface (BCI), data privacy, and performance assessment) with special emphasis to biomedical engineering and automation engineering applications. The content of this paper is aimed at students, researchers, and practitioners to glimpse the multifaceted world of brain–computer interfacing.

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Pineda, Roger Gacula. "Where the Interaction Is Not." International Journal of Art, Culture and Design Technologies 5, no. 1 (January 2016): 1–12. http://dx.doi.org/10.4018/ijacdt.2016010101.

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The concept of interaction is foundational in technology interface design with its presuppositions being taken for granted. But the interaction metaphor has become ambiguous to the extent that its application to interface design contributes to misalignments between people's expected and actual experience with computers. This article re-examines the presuppositions governing human-computer interaction with the motivation of strengthening weaknesses in their foundational concepts. It argues for abandoning the interaction metaphor to refocus design discourse toward the mediation roles of technology interfaces. ‘Remediation', i.e. representation of one medium in another, is proposed as a conceptual model that more precisely describes the human-to-computer actions.

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Ibrahim, Umar, and Aliyu Danmaigoro. "Human-Computer Interaction in Agricultural User Interfaces." International Journal of Applied and Scientific Research 2, no. 2 (February 29, 2024): 187–98. http://dx.doi.org/10.59890/ijasr.v2i2.1381.

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This study explores the intersection of Human-Computer Interaction (HCI) and agricultural user interfaces, aiming to enhance the efficiency and user experience within the agricultural domain. A thorough literature review identifies current trends, challenges, and gaps in HCI research specific to agricultural interfaces. The research objectives encompass the proposal of HCI design principles tailored to the unique characteristics of the agricultural environment. Methodologically, the study employs user surveys, interviews, and usability testing to gather insights into user preferences and challenges. The paper presents case studies highlighting successful implementations of HCI principles in agricultural settings, providing tangible examples of improved user engagement and task performance. Challenges and limitations in HCI adoption in agriculture are discussed, along with user feedback and evaluations shaping interface design. The implications of the research extend to HCI practitioners, agricultural technology developers, and policymakers, offering practical insights for future developments. This paper contributes valuable knowledge to the evolving field of HCI in agricultural contexts, emphasizing the importance of user-centered design.

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Chen, Anqi, Feng Xie, Jingbo Wang, and Jun Chen. "Intelligent Optimization Method of Human–Computer Interaction Interface for UAV Cluster Attack Mission." Electronics 12, no. 21 (October 27, 2023): 4426. http://dx.doi.org/10.3390/electronics12214426.

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In modern warfare, it is often necessary for the operator to control the UAV cluster from a ground control station to perform an attack task. However, the absence of an effective method for optimizing the human–computer interface in ground control stations for UAV clusters leads to usability difficulties and heightens the probability of human errors. Hence, we propose an optimization framework for human–computer interaction interfaces within UAV ground control stations, rooted in interface-essential elements. Specifically, the interface evaluation model was formulated by combining the Salient, Effort, Expectancy, and Value (SEEV) framework with the essential factor mutation cost of the quantified interface. We employed the SEEV–ant colony algorithm to address the challenge of optimizing the interface design within this context. For a typical UAV cluster attack mission, we optimized the human–computer interaction interfaces of the three mission stages based on the proposed SEEV-AC model. We conducted extensive simulation experiments in these optimized interfaces, and used eye-movement indicators to evaluate the effectiveness of the interface optimization model. Based on the experimental results, divergence is reduced by 11.59%, and the fitness of the optimized interface is increased from 1.34 to 3.42. The results show that the proposed intelligent interface optimization method can effectively improve the interface design and reduce the operator’s workload.

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唐, 健. "為侵入性腦機介面技術進行倫理辯護." International Journal of Chinese & Comparative Philosophy of Medicine 21, no. 2 (December 29, 2023): 107–10. http://dx.doi.org/10.24112/ijccpm.212691.

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LANGUAGE NOTE | Document text in Chinese; abstract also in English. The idea that invasive brain–computer interface technology is dangerous is debatable. This technology requires ethical justification, recognizing the coexistence of their benefits and risks. The newly formed professional consensus in China illustrates that it is possible to develop forward-looking and binding ethical guidelines through multidisciplinary argumentation and stakeholder consultation. Invasive brain–computer interfaces need not be limited to the smallest scope of application. Invasive brain–computer interfaces for healthy people and for enhancement purposes will provide technical and ethical options for future human society. Therefore, research into invasive brain–computer interfaces should not be prohibited. Rather, we must be alert to the risky possibility of research and development on brain–computer interfaces becoming entirely driven by business interests and controlled by capital. Furthermore, Chinese Taoist wisdom does not necessarily reject this technology.

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Mikołajewska, Emilia, and Dariusz Mikołajewski. "The prospects of brain — computer interface applications in children." Open Medicine 9, no. 1 (February 1, 2014): 74–79. http://dx.doi.org/10.2478/s11536-013-0249-3.

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AbstractThe restoring of motor functions in adults through brain-computer interface applications is widely studied in the contemporary literature. But there is a lack of similar analyses and research on the application of brain-computer interfaces in the neurorehabilitation of children. There is a need for expanded knowledge in the aforementioned area. This article aims at investigating the extent to which the available opportunities in the area of neurorehabilitation and neurological physiotherapy of children with severe neurological deficits using brain-computer interfaces are being applied, including our own concepts, research and observations.

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Jylhä, Henrietta, and Juho Hamari. "Development of measurement instrument for visual qualities of graphical user interface elements (VISQUAL): a test in the context of mobile game icons." User Modeling and User-Adapted Interaction 30, no. 5 (May 17, 2020): 949–82. http://dx.doi.org/10.1007/s11257-020-09263-7.

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Abstract Graphical user interfaces are widely common and present in everyday human–computer interaction, dominantly in computers and smartphones. Today, various actions are performed via graphical user interface elements, e.g., windows, menus and icons. An attractive user interface that adapts to user needs and preferences is progressively important as it often allows personalized information processing that facilitates interaction. However, practitioners and scholars have lacked an instrument for measuring user perception of aesthetics within graphical user interface elements to aid in creating successful graphical assets. Therefore, we studied dimensionality of ratings of different perceived aesthetic qualities in GUI elements as the foundation for the measurement instrument. First, we devised a semantic differential scale of 22 adjective pairs by combining prior scattered measures. We then conducted a vignette experiment with random participant (n = 569) assignment to evaluate 4 icons from a total of pre-selected 68 game app icons across 4 categories (concrete, abstract, character and text) using the semantic scales. This resulted in a total of 2276 individual icon evaluations. Through exploratory factor analyses, the observations converged into 5 dimensions of perceived visual quality: Excellence/Inferiority, Graciousness/Harshness, Idleness/Liveliness, Normalness/Bizarreness and Complexity/Simplicity. We then proceeded to conduct confirmatory factor analyses to test the model fit of the 5-factor model with all 22 adjective pairs as well as with an adjusted version of 15 adjective pairs. Overall, this study developed, validated, and consequently presents a measurement instrument for perceptions of visual qualities of graphical user interfaces and/or singular interface elements (VISQUAL) that can be used in multiple ways in several contexts related to visual human-computer interaction, interfaces and their adaption.

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Sreedharan, Sujesh, Ranganatha Sitaram, Joseph S. Paul, and C. Kesavadas. "Brain-Computer Interfaces for Neurorehabilitation." Critical Reviews in Biomedical Engineering 41, no. 3 (2013): 269–79. http://dx.doi.org/10.1615/critrevbiomedeng.2014010697.

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LUO XUAN, QIAN GE-FEI, and WANG YU-MING. "COMPUTER SIMULATION OF METAL INTERFACES." Acta Physica Sinica 43, no. 12 (1994): 1957. http://dx.doi.org/10.7498/aps.43.1957.

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Nicolas-Alonso, Luis Fernando, and Jaime Gomez-Gil. "Brain Computer Interfaces, a Review." Sensors 12, no. 2 (January 31, 2012): 1211–79. http://dx.doi.org/10.3390/s120201211.

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Coyle, Shirley, Tomas Ward, and Charles Markham. "Brain–computer interfaces: a review." Interdisciplinary Science Reviews 28, no. 2 (June 2003): 112–18. http://dx.doi.org/10.1179/030801803225005102.

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Wahlstrom, Kirsten, N. Ben Fairweather, and Helen Ashman. "Privacy and brain-computer interfaces." ACM SIGCAS Computers and Society 46, no. 1 (March 28, 2016): 41–53. http://dx.doi.org/10.1145/2908216.2908223.

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Pennycook, Bruce W. "Computer-music interfaces: a survey." ACM Computing Surveys 17, no. 2 (June 1985): 267–89. http://dx.doi.org/10.1145/4468.4470.

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Bernal, Sergio López, Alberto Huertas Celdrán, Gregorio Martínez Pérez, Michael Taynnan Barros, and Sasitharan Balasubramaniam. "Security in Brain-Computer Interfaces." ACM Computing Surveys 54, no. 1 (January 2, 2021): 1–35. http://dx.doi.org/10.1145/3427376.

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Hirschman, Lynette, and Donna Cuomo. "Evaluation of human computer interfaces." ACM SIGCHI Bulletin 27, no. 2 (April 1995): 28–29. http://dx.doi.org/10.1145/202511.202516.

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Shih, Jerry J., Dean J. Krusienski, and Jonathan R. Wolpaw. "Brain-Computer Interfaces in Medicine." Mayo Clinic Proceedings 87, no. 3 (March 2012): 268–79. http://dx.doi.org/10.1016/j.mayocp.2011.12.008.

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Hochberg, L. R., and J. P. Donoghue. "Sensors for brain-computer interfaces." IEEE Engineering in Medicine and Biology Magazine 25, no. 5 (September 2006): 32–38. http://dx.doi.org/10.1109/memb.2006.1705745.

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Marque, P. "Non-invasive brain computer interfaces." Annals of Physical and Rehabilitation Medicine 55 (October 2012): e345. http://dx.doi.org/10.1016/j.rehab.2012.07.873.

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