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Journal articles on the topic 'Brain-Computer Interfaces (BCIs)'

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Published: 1 March 2025

Last updated: 25 July 2025

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1

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

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Tang, Feifang, Feiyang Yan, Yushan Zhong, Jinqian Li, Hui Gong, and Xiangning Li. "Optogenetic Brain–Computer Interfaces." Bioengineering 11, no. 8 (2024): 821. http://dx.doi.org/10.3390/bioengineering11080821.

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The brain–computer interface (BCI) is one of the most powerful tools in neuroscience and generally includes a recording system, a processor system, and a stimulation system. Optogenetics has the advantages of bidirectional regulation, high spatiotemporal resolution, and cell-specific regulation, which expands the application scenarios of BCIs. In recent years, optogenetic BCIs have become widely used in the lab with the development of materials and software. The systems were designed to be more integrated, lightweight, biocompatible, and power efficient, as were the wireless transmission and c

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Nijholt, Anton, and Chang S. Nam. "Arts and Brain-Computer Interfaces (BCIs)." Brain-Computer Interfaces 2, no. 2-3 (2015): 57–59. http://dx.doi.org/10.1080/2326263x.2015.1100514.

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Klein, Eran, and C. S. Nam. "Neuroethics and brain-computer interfaces (BCIs)." Brain-Computer Interfaces 3, no. 3 (2016): 123–25. http://dx.doi.org/10.1080/2326263x.2016.1210989.

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Ankita, Chhikara, and Banik Amlan. "Brain Computer Interfaces: Engineering That Changed Healthcare." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 11 (2025): 3726–30. https://doi.org/10.5281/zenodo.14942778.

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Brain-Computer Interface (BCI) technology, which enables direct interaction between the human brain and external devices, is revolutionizing healthcare. By decoding and interpreting neural signals, BCIs open new opportunities for rehabilitation, regaining lost functions, and enhancing quality of life, especially for individuals with neurological disorders, spinal cord injuries, or neurodegenerative conditions. This article examines the mechanisms, classifications, applications, and challenges of BCIs. It highlights progress in invasive, partially invasive, and non-invasive BCIs, their clinical

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Mahalakshmi, Talluri. "Brain-Computer Interfaces: An Evolution in Neurotechnology." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 4035–44. https://doi.org/10.22214/ijraset.2025.71124.

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Brain-Computer Interfaces (BCIs) are a rapidly developing technology that facilitates direct interaction between the human brain and outside systems. In this paper, there is a comprehensive review of BCI types, methods of signal acquisition such as EEG and ECoG, and the basic signal processing stages such as preprocessing, feature extraction, and classification. Medical, communication, entertainment, and cognitive applications are discussed with a focus on how BCIs can bring about revolutionary changes. Key challenges touched upon include noise, user heterogeneity, and ethics. The article conc

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Ma, Yixin, Anmin Gong, Wenya Nan, Peng Ding, Fan Wang, and Yunfa Fu. "Personalized Brain–Computer Interface and Its Applications." Journal of Personalized Medicine 13, no. 1 (2022): 46. http://dx.doi.org/10.3390/jpm13010046.

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Brain–computer interfaces (BCIs) are a new technology that subverts traditional human–computer interaction, where the control signal source comes directly from the user’s brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have ela

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Uday, S. Yeshi, A. Khode Atharva, Sangle Shashvat, Vishwasrao Surabhi, and Salve Gautami. "A Detailed Overview of Brain-Computer and Brain-Machine Interfaces." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 12 (2025): 2202–8. https://doi.org/10.5281/zenodo.14598593.

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Brain-Computer Interfaces (BCIs) and Brain- Machine Interfaces (BMIs) represent trans-formative  technologies capable of enabling communication and control for individuals with severe disabilities. These systems employ a series of intricate processes, including signal acquisition, feature extraction, feature translation, and device output, to translate neural activity into actionable commands. While BCIs predominantly focus on noninvasive applications, BMIs often involve invasive methods, with preclinical studies on animal models advancing the un- derstanding of neural decoding. Despite t

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Dariusz, Mikołajewski. "Brain-computer interfaces in control of mechatronic devices and systems." Studies and Materials in Applied Computer Science (ISSN 1689-6300) 10, no. 2 (2020): 4–9. https://doi.org/10.5281/zenodo.4321065.

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Brain-computer interfaces (BCIs) have begun to constitute the another breakthrough in contemporary neuroscience and neurorehabilitation. This paper provides an overview of brain-computer interfaces (BCIs) technology that aims to address the priorities for control of mechatronic devices and systems. We describe basic solutions in the area of BCIs and discuss technologies that may provide command signals for mechatronic devices. Despite continuous development of the topic there still remains room for improvement, including future interfaces and control signal classification enhancements.

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Imashev, A. "THE EVOLUTION OF HUMAN-COMPUTER INTERACTION FROM GUIS TO BRAIN-COMPUTER INTERFACES." Slovak international scientific journal, no. 94 (April 13, 2025): 22–24. https://doi.org/10.5281/zenodo.15206941.

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The article delves into the progression of Human-Computer Interaction (HCI) technologies, tracing their development from Graphical User Interfaces (GUIs) to the emerging field of Brain-Computer Interfaces (BCIs). GUIs revolutionized HCI by introducing visual elements like windows, icons, and menus, making computer interactions more intuitive and accessible. This shift allowed users to engage with computers without needing to understand complex command-line instructions, significantly broadening the user base. BCIs represent the forefront of HCI, enabling direct communication between the brain

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Colman, Jason, and Paul Gnanayutham. "Accessible Button Interfaces." International Journal of Web-Based Learning and Teaching Technologies 7, no. 4 (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 w

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Valeriani, Davide, Caterina Cinel, and Riccardo Poli. "Brain–Computer Interfaces for Human Augmentation." Brain Sciences 9, no. 2 (2019): 22. http://dx.doi.org/10.3390/brainsci9020022.

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The field of brain–computer interfaces (BCIs) has grown rapidly in the last few decades, allowing the development of ever faster and more reliable assistive technologies for converting brain activity into control signals for external devices for people with severe disabilities [...]

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Ferreira, Alessandro Luiz Stamatto, Leonardo Cunha de Miranda, Erica Esteves Cunha de Miranda, and Sarah Gomes Sakamoto. "A Survey of Interactive Systems based on Brain-Computer Interfaces." Journal on Interactive Systems 4, no. 1 (2013): 1. http://dx.doi.org/10.5753/jis.2013.623.

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Brain-Computer Interface (BCI) enables users to interact with a computer only through their brain biological signals, without the need to use muscles. BCI is an emerging research area but it is still relatively immature. However, it is important to reflect on the different aspects of the Human-Computer Interaction (HCI) area related to BCIs, considering that BCIs will be part of interactive systems in the near future. BCIs most attend not only to handicapped users, but also healthy ones, improving interaction for end-users. Virtual Reality (VR) is also an important part of interactive systems,

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Mikołajewska, Emilia, and Dariusz Mikołajewski. "Ethical considerations in the use of brain-computer interfaces." Open Medicine 8, no. 6 (2013): 720–24. http://dx.doi.org/10.2478/s11536-013-0210-5.

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AbstractNervous system disorders are among the most severe disorders. Significant breakthroughs in contemporary clinical practice may provide brain-computer interfaces (BCIs) and neuroprostheses (NPs). The aim of this article is to investigate the extent to which the ethical considerations in the clinical application of brain-computer interfaces and associated threats are being identified. Ethical considerations and implications may significantly influence further development of BCIs and NPs. Moreover, there is significant public interest in supervising this development. Awareness of BCIs’ and

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Xu, Jiahong. "Optimizing Brain-Computer Interfaces through Spiking Neural Networks and Memristors." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 184–90. http://dx.doi.org/10.54097/yk9r3d87.

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Brain-computer interfaces (BCIs) have emerged as a transformative conduit bridging the human brain's intricate realms and computing systems' capabilities. However, numerous challenges remain in improving BCI accuracy, efficiency, and adaptability. This paper investigates the integration of spiking neural networks (SNNs) and memristors to optimize BCI performance. SNNs offer exceptional potential to enhance BCI accuracy through biomimetic modeling of biological neural networks. By emulating the brain's spatio-temporal signaling patterns, SNNs may significantly improve neural decoding precision.

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Kotchetkov, Ivan S., Brian Y. Hwang, Geoffrey Appelboom, Christopher P. Kellner, and E. Sander Connolly. "Brain-computer interfaces: military, neurosurgical, and ethical perspective." Neurosurgical Focus 28, no. 5 (2010): E25. http://dx.doi.org/10.3171/2010.2.focus1027.

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Brain-computer interfaces (BCIs) are devices that acquire and transform neural signals into actions intended by the user. These devices have been a rapidly developing area of research over the past 2 decades, and the military has made significant contributions to these efforts. Presently, BCIs can provide humans with rudimentary control over computer systems and robotic devices. Continued advances in BCI technology are especially pertinent in the military setting, given the potential for therapeutic applications to restore function after combat injury, and for the evolving use of BCI devices i

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Zhao, Yingzhen. "Wearable brain-computer interface technology and its application." Theoretical and Natural Science 15, no. 1 (2023): 137–45. http://dx.doi.org/10.54254/2753-8818/15/20240468.

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Wearable Brain-Computer Interfaces (BCIs) signify a critical evolution in human-machine communication, driven by the convergence of neuroscience, engineering, and information technology. With applications that span across industrial, medical, and recreational domains, BCIs hold potential to redefine our interaction with the technological landscape. This manuscript elucidates this transformative juncture, bifurcating into passive and active BCIs. In passive BCIs, innovations leveraging Virtual Reality (VR) and Augmented Reality (AR) are delineated, progress has been demonstrated in the classifi

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Miller, Kai J., Dora Hermes, and Nathan P. Staff. "The current state of electrocorticography-based brain–computer interfaces." Neurosurgical Focus 49, no. 1 (2020): E2. http://dx.doi.org/10.3171/2020.4.focus20185.

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Brain–computer interfaces (BCIs) provide a way for the brain to interface directly with a computer. Many different brain signals can be used to control a device, varying in ease of recording, reliability, stability, temporal and spatial resolution, and noise. Electrocorticography (ECoG) electrodes provide a highly reliable signal from the human brain surface, and these signals have been used to decode movements, vision, and speech. ECoG-based BCIs are being developed to provide increased options for treatment and assistive devices for patients who have functional limitations. Decoding ECoG sig

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Kurup, Aathira R., and Dr Baulkani S. ""Exploring the Potential of Brain-Computer Interfaces in Managing Alzheimer\'s disease: A Review"." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (2023): 367–70. http://dx.doi.org/10.22214/ijraset.2023.49030.

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Brain-computer interfaces (BCIs) have been proposed as a potential therapeutic tool for Alzheimer's disease patients. BCIs use electrodes placed on the scalp to record brain activity and translate it into control signals for a computer or other device. In Alzheimer's disease, BCIs have been shown to improve cognitive function and quality of life, particularly in the areas of memory, attention, and executive function. However, more research is needed to fully understand the potential benefits and limitations of BCIs for Alzheimer's patients.

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Ünlü, Sudenaz Ceren. "Enhancing Accessibility through Brain-Computer Interfaces (BCIs) in Assistive Technology." Human Computer Interaction 8, no. 1 (2024): 23. http://dx.doi.org/10.62802/7tt4r452.

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Brain-Computer Interfaces (BCIs) have revolutionized assistive technology, offering transformative solutions to enhance accessibility for individuals with physical and neurological disabilities. By enabling direct communication between the brain and external devices, BCIs bypass traditional pathways, empowering users to control assistive tools through neural activity. This research explores the integration of BCIs into assistive technology, focusing on their potential to improve mobility, communication, and independence. It examines cutting-edge applications such as neural-controlled prostheti

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21

Padfield, Natasha, Jaime Zabalza, Huimin Zhao, Valentin Masero, and Jinchang Ren. "EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges." Sensors 19, no. 6 (2019): 1423. http://dx.doi.org/10.3390/s19061423.

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Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents

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WOLKENSTEIN, ANDREAS, RALF J. JOX, and ORSOLYA FRIEDRICH. "Brain–Computer Interfaces: Lessons to Be Learned from the Ethics of Algorithms." Cambridge Quarterly of Healthcare Ethics 27, no. 4 (2018): 635–46. http://dx.doi.org/10.1017/s0963180118000130.

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Abstract:Brain–computer interfaces (BCIs) are driven essentially by algorithms; however, the ethical role of such algorithms has so far been neglected in the ethical assessment of BCIs. The goal of this article is therefore twofold: First, it aims to offer insights into whether (and how) the problems related to the ethics of BCIs (e.g., responsibility) can be better grasped with the help of already existing work on the ethics of algorithms. As a second goal, the article explores what kinds of solutions are available in that body of scholarship, and how these solutions relate to some of the eth

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Fry, Adam, Ho Wing Chan, Noam Y. Harel, Lisa A. Spielman, Miguel X. Escalon, and David F. Putrino. "Evaluating the clinical benefit of brain-computer interfaces for control of a personal computer." Journal of Neural Engineering 19, no. 2 (2022): 021001. http://dx.doi.org/10.1088/1741-2552/ac60ca.

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Abstract Brain-computer interfaces (BCIs) enabling the control of a personal computer could provide myriad benefits to individuals with disabilities including paralysis. However, to realize this potential, these BCIs must gain regulatory approval and be made clinically available beyond research participation. Therefore, a transition from engineering-oriented to clinically oriented outcome measures will be required in the evaluation of BCIs. This review examined how to assess the clinical benefit of BCIs for the control of a personal computer. We report that: (a) a variety of different patient-

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Wang, Yanbo. "Convolutional Neural Network in Brain-computer Interfaces-exoskeleton System." Highlights in Science, Engineering and Technology 120 (December 25, 2024): 251–57. https://doi.org/10.54097/8a6wxg03.

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Brain-computer interfaces (BCIs) have emerged as a groundbreaking technology that has the potential to revolutionize the field of stroke rehabilitation. These innovative systems allow individuals who have suffered from strokes to regain lost motor function by directly connecting their brains with external devices, such as exoskeletons. One of the most commonly used paradigms in BCIs is motor imagery (MI), which involves generating electroencephalograms (EEGs) through imagined movements. This means that stroke patients can perform motor tasks simply with the help of exoskeletons by only thinkin

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Wang, Kaile. "Unlocking the Mind: Revolutionizing the Metaverse with Brain-Computer Interfaces." Applied and Computational Engineering 151, no. 1 (2025): 95–100. https://doi.org/10.54254/2755-2721/2025.22853.

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The current exploration of the Metaverse has now become a focus of research as research has deepened and technology has improved. Based on summarizing the experience of previous research, this paper explores the relationship between Brain-Computer Interface (BCIs) and Metaverse, and analyzes the possibilities, diversity, and creativity that BCIs offer to Metaverse research. Through user cognitive state monitoring, digitized body control, virtual interaction, and imagined voice communication, BCIs enhances the connection between the human brain and external devices to produce a more realistic f

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Kosmyna, Nataliya, Franck Tarpin-Bernard, and Bertrand Rivet. "Adding Human Learning in Brain--Computer Interfaces (BCIs)." ACM Transactions on Computer-Human Interaction 22, no. 3 (2015): 1–37. http://dx.doi.org/10.1145/2723162.

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Schalk, G., P. Brunner, L. A. Gerhardt, H. Bischof, and J. R. Wolpaw. "Brain–computer interfaces (BCIs): Detection instead of classification." Journal of Neuroscience Methods 167, no. 1 (2008): 51–62. http://dx.doi.org/10.1016/j.jneumeth.2007.08.010.

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28

Mohan Raja Pulicharla and Varsha Premani. "AI-powered Neuroprosthetics for brain-computer interfaces (BCIs)." World Journal of Advanced Engineering Technology and Sciences 12, no. 1 (2024): 109–15. http://dx.doi.org/10.30574/wjaets.2024.12.1.0201.

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Imagine a world where individuals with lost or impaired sensory or motor function can regain independence and control through technology. This is the promise of neuroprosthetics, a rapidly evolving field that bridges the gap between the nervous system and external devices. Neuroprosthetics encompass a range of implanted or external devices designed to: Substitute for a malfunctioning part of the nervous system. Assist in the recovery or enhancement of lost or impaired function. Augment existing capabilities, creating new possibilities. These devices interact with the nervous system using vario

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Angelakis, Dimitris, Errikos Ventouras, Spyros Kostopoulos, and Pantelis Asvestas. "Cybersecurity Issues in Brain-Computer Interfaces: Analysis of Existing Bluetooth Vulnerabilities." Digital Technologies Research and Applications 3, no. 2 (2024): 115–39. http://dx.doi.org/10.54963/dtra.v3i2.286.

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Brain-computer interfaces (BCIs) hold immense promise for human benefits, enabling communication between the brain and computer-controlled devices. Despite their potential, BCIs face significant cybersecurity risks, particularly from Bluetooth vulnerabilities. This study investigates Bluetooth vulnerabilities in BCIs, analysing potential risks and proposing mitigation measures. Various Bluetooth attacks such as Bluebugging, Bluejacking, Bluesnarfing, BlueBorne, Location Tracking, Man-in-the-Middle Attack, KNOB, BLESA and Reflection Attack are explored, along with their potential consequences o

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Huang, Zhibao, Zenan Zhou, Jiasheng Zeng, Sen Lin, and Hui Wu. "Flexible electrodes for non-invasive brain–computer interfaces: A perspective." APL Materials 10, no. 9 (2022): 090901. http://dx.doi.org/10.1063/5.0099722.

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At the present time, brain–computer interfaces (BCIs) are attracting considerable attention due to their application potential in many fields. In this Perspective, we provide a brief review of flexible electrode technologies for non-invasive BCIs, mainly including two types of the most representative flexible electrodes: dry electrodes and semi-dry electrodes. We also summarize the challenges encountered by the different kinds of electrodes by comparing their strengths and weaknesses in terms of manufacturing scalability, applicability, comfort, contact impedance, long-term stability, and bioc

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Huang, Yiling. "The current clinical applications of invasive brain-computer interfaces." Theoretical and Natural Science 16, no. 1 (2023): 55–60. http://dx.doi.org/10.54254/2753-8818/16/20240527.

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Brain-computer interface (BCI) technology is an emerging and swiftly expanding advanced technology. It links the brain to external devices, creates a brain-computer interface connection pathway, and ultimately realises information exchange and control. Meanwhile, as modern medicine continues to explore the composition and operation of the brain, the clinical applications of BCI have become more widespread. In particular, in the diagnosis, screening, treatment, and rehabilitation of neurological diseases and motor impairments, BCI is becoming more and more significant. This paper first explains

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Schmid, J. R., O. Friedrich, S. Kessner, and R. J. Jox. "Thoughts Unlocked by Technology—a Survey in Germany About Brain-Computer Interfaces." NanoEthics 15, no. 3 (2021): 303–13. http://dx.doi.org/10.1007/s11569-021-00392-w.

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AbstractA brain-computer interface (BCI) is a rapidly evolving neurotechnology connecting the human brain with a computer. In its classic form, brain activity is recorded and used to control external devices like protheses or wheelchairs. Thus, BCI users act with the power of their thoughts. While the initial development has focused on medical uses of BCIs, non-medical applications have recently been gaining more attention, for example in automobiles, airplanes, and the entertainment context. However, the attitudes of the general public towards BCIs have hardly been explored. Among the general

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Zhu, Fangkun, Lu Jiang, Guoya Dong, Xiaorong Gao, and Yijun Wang. "An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces." Sensors 21, no. 4 (2021): 1256. http://dx.doi.org/10.3390/s21041256.

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Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. Thi

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Yu, Lochi, and Cristian Ureña. "A Review of Current Approaches of Brain Computer Interfaces." International Journal of Measurement Technologies and Instrumentation Engineering 2, no. 2 (2012): 1–19. http://dx.doi.org/10.4018/ijmtie.2012040101.

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Since the first recordings of brain electrical activity more than 100 years ago remarkable contributions have been done to understand the brain functionality and its interaction with environment. Regardless of the nature of the brain-computer interface BCI, a world of opportunities and possibilities has been opened not only for people with severe disabilities but also for those who are pursuing innovative human interfaces. Deeper understanding of the EEG signals along with refined technologies for its recording is helping to improve the performance of EEG based BCIs. Better processing and feat

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Brumberg, Jonathan S., Kevin M. Pitt, Alana Mantie-Kozlowski, and Jeremy D. Burnison. "Brain–Computer Interfaces for Augmentative and Alternative Communication: A Tutorial." American Journal of Speech-Language Pathology 27, no. 1 (2018): 1–12. http://dx.doi.org/10.1044/2017_ajslp-16-0244.

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Purpose Brain–computer interfaces (BCIs) have the potential to improve communication for people who require but are unable to use traditional augmentative and alternative communication (AAC) devices. As BCIs move toward clinical practice, speech-language pathologists (SLPs) will need to consider their appropriateness for AAC intervention. Method This tutorial provides a background on BCI approaches to provide AAC specialists foundational knowledge necessary for clinical application of BCI. Tutorial descriptions were generated based on a literature review of BCIs for restoring communication. Re

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Gordon, Emma C., and Anil K. Seth. "Ethical considerations for the use of brain–computer interfaces for cognitive enhancement." PLOS Biology 22, no. 10 (2024): e3002899. http://dx.doi.org/10.1371/journal.pbio.3002899.

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Brain–computer interfaces (BCIs) enable direct communication between the brain and external computers, allowing processing of brain activity and the ability to control external devices. While often used for medical purposes, BCIs may also hold great promise for nonmedical purposes to unlock human neurocognitive potential. In this Essay, we discuss the prospects and challenges of using BCIs for cognitive enhancement, focusing specifically on invasive enhancement BCIs (eBCIs). We discuss the ethical, legal, and scientific implications of eBCIs, including issues related to privacy, autonomy, ineq

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Naseer, Noman, Imran Khan Niazi, and Hendrik Santosa. "Editorial: Signal Processing for Brain–Computer Interfaces—Special Issue." Sensors 24, no. 4 (2024): 1201. http://dx.doi.org/10.3390/s24041201.

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Zhang, Ruoyao. "Enhancing Human-Computer Interaction through Brain-Computer Interface: Technological Advances." Applied and Computational Engineering 145, no. 1 (2025): 170–75. https://doi.org/10.54254/2755-2721/2025.22240.

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Brain-Computer Interface (BCI) has gained significant attention due to its potential to transform human-computer interaction (HCI), especially through non-invasive methods like electroencephalography (EEG). This essay explores the fundamental principles of non-invasive BCIs, focusing on EEG-based signal acquisition, preprocessing, and decoding techniques. It examines the role of various machine learning and deep learning algorithms in enhancing the accuracy and efficiency of neural signal interpretation, including supervised learning, unsupervised learning, CNN, RNN, and transformers. These ke

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Ronzhes, Olena. "Improving the Effectiveness of Learning with the Help of Neurocomputer Interface." Visnyk of V. N. Karazin Kharkiv National University. A Series of Psychology, no. 72 (August 5, 2022): 44–51. http://dx.doi.org/10.26565/2225-7756-2022-72-05.

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The article considers modern technologies for reading signals from the human brain and nervous system and selects the optimal technology to improve the efficiency of adult learning with the help of a neurocomputer interface. Existing brain-computer interfaces (BCI) technologies can be divided into invasive and non-invasive. The first, invasive BCIs, are neuroimplants in certain parts of the brain that work on the basis of electrocorticography (ECOG) or intracranial EEG (iEEG) technology and do not require deep intervention in brain structures; or another invasive BCIs, based on intracortical r

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Baek, Hyun Jae, Min Hye Chang, Jeong Heo, and Kwang Suk Park. "Enhancing the Usability of Brain-Computer Interface Systems." Computational Intelligence and Neuroscience 2019 (June 16, 2019): 1–12. http://dx.doi.org/10.1155/2019/5427154.

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Brain-computer interfaces (BCIs) aim to enable people to interact with the external world through an alternative, nonmuscular communication channel that uses brain signal responses to complete specific cognitive tasks. BCIs have been growing rapidly during the past few years, with most of the BCI research focusing on system performance, such as improving accuracy or information transfer rate. Despite these advances, BCI research and development is still in its infancy and requires further consideration to significantly affect human experience in most real-world environments. This paper reviews

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Swan, Melanie. "The Future of Brain-Computer Interfaces." Journal of Ethics and Emerging Technologies 26, no. 2 (2016): 60–81. http://dx.doi.org/10.55613/jeet.v26i2.60.

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The aim of this paper is to explore the development of brain-computer interfacing and cloudminds as possible future scenarios. I describe potential applications such as selling unused brain processing cycles and the blockchaining of personality functions. The possibility of ubiquitous brain-computer interfaces (BCIs) that are continuously connected to the Internet suggests interesting options for our future selves. Questions about what it is to be human, the nature of our current existence and interaction with reality, and how things might be different could become more prominent. I examine sp

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Kılıç, Selin. "Brain-Computer Interfaces Enhanced by AI: Applications in Rehabilitation and Assistive Technology." Next Frontier For Life Sciences and AI 8, no. 1 (2024): 207. https://doi.org/10.62802/m89avz38.

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Brain-Computer Interfaces (BCIs) enhanced by Artificial Intelligence (AI) represent a transformative frontier in rehabilitation and assistive technology. These systems enable direct communication between the brain and external devices, empowering individuals with neurological impairments to regain lost functions and enhance their quality of life. By integrating AI, BCIs can decode complex neural signals with unprecedented accuracy, enabling applications such as motor function restoration, cognitive enhancement, and assistive communication. This research explores the current state of AI-driven

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Tarun, Gupta, and Bansal Supriya. "Deciphering the Mind: Advancing Consumer Insights through Brain-Computer Interfaces in Neuromarketing for the Digital Age." European Journal of Advances in Engineering and Technology 10, no. 3 (2023): 25–35. https://doi.org/10.5281/zenodo.10901278.

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<strong>ABSTRACT</strong> With the explosive growth of e-commerce, understanding digital consumer behavior is critical for businesses. However, traditional methods have limitations. This paper explores the potential of Brain-Computer Interfaces (BCIs) in neuromarketing to address these limitations. Through a two-stage content analysis, we examine the current landscape of BCI research and applications, highlighting its advantages over traditional methods in uncovering subconscious preferences and emotions. We showcase real-world examples demonstrating BCI's effectiveness and discuss future dire

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Parveen, Muskan, and Garima Sharma. "Enhancing Human-Computer Interaction through Brain-Computer Interfaces." Radius: Journal of Science and Technology 2, no. 1 (2025): 251010. https://doi.org/10.5281/zenodo.15399647.

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HCI is transformed by BCI interactions, breaking previous boundaries for the results to go directly into the device and creating a direct neuron-to-computer communication. The Work is centered on the improvement in HCI made possible by BCIs, notably greater simplicity and ease for those who have physical limitations. It calls into question present BCI systems, develops them into working interactive devices, tackles signal noise and user adaptability, and flagships future concepts such as neural decoding, AI, and ethical considerations. Neuroscience, AI, and user interface engineering together

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FERDI, Ahmed Yassine, and Abdelkader GHAZLI. "Mind-Controlled Web Browser Navigation Based on Brain Computer Interfaces." Algerian Journal of Signals and Systems 10, no. 1 (2025): 24–28. https://doi.org/10.51485/ajss.v10i1.260.

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Brain-Computer Interfaces (BCIs) are communication systems that enable direct interaction between the human brain and machines or devices without the need for physical contact, utilizing EEG signals generated from brain activity. A BCI system typically involves two key stages: feature extraction and classification. The classification process relies on signals collected from specific EEG sensor groups. One of the main challenges in classifying motor imagery EEG signals arises from the fact that EEG data is often a mixture of meaningful signals and noise. As a result, selecting an effective clas

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Kim, Do-Won, Jun-Chang Lee, Young-Min Park, In-Young Kim, and Chang-Hwan Im. "Auditory brain-computer interfaces (BCIs) and their practical applications." Biomedical Engineering Letters 2, no. 1 (2012): 13–17. http://dx.doi.org/10.1007/s13534-012-0051-1.

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Zhang, Hao, and Zhenghui Gu. "Adversarial sample detection for EEG-based brain-computer interfaces." Journal of Physics: Conference Series 2761, no. 1 (2024): 012037. http://dx.doi.org/10.1088/1742-6596/2761/1/012037.

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Abstract Deep neural networks (DNNs) play a pivotal role within the domain of brain-computer interfaces (BCIs). Nevertheless, DNNs are demonstrated to exhibit susceptibility to adversarial attacks. In BCIs, researchers have been concerned about the security of DNNs and have devised various adversarial defense methods to resist adversarial attacks. However, most defense methods encounter performance degradation when dealing with normal samples due to changes in the original model. As an alternative strategy, adversarial detection aims to devise additional modules or use statistical properties t

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Donnerer, Michael, and Anthony Steed. "Using a P300 Brain–Computer Interface in an Immersive Virtual Environment." Presence: Teleoperators and Virtual Environments 19, no. 1 (2010): 12–24. http://dx.doi.org/10.1162/pres.19.1.12.

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Brain–computer interfaces (BCIs) provide a novel form of human–computer interaction. The purpose of these systems is to aid disabled people by affording them the possibility of communication and environment control. In this study, we present experiments using a P300 based BCI in a fully immersive virtual environment (IVE). P300 BCIs depend on presenting several stimuli to the user. We propose two ways of embedding the stimuli in the virtual environment: one that uses 3D objects as targets, and a second that uses a virtual overlay. Both ways have been shown to work effectively with no significa

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LOPEZ-GORDO, M. A., F. PELAYO, A. PRIETO, and E. FERNANDEZ. "AN AUDITORY BRAIN-COMPUTER INTERFACE WITH ACCURACY PREDICTION." International Journal of Neural Systems 22, no. 03 (2012): 1250009. http://dx.doi.org/10.1142/s0129065712500098.

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Fully auditory Brain-computer interfaces based on the dichotic listening task (DL-BCIs) are suited for users unable to do any muscular movement, which includes gazing, exploration or coordination of their eyes looking for inputs in form of feedback, stimulation or visual support. However, one of their disadvantages, in contrast with the visual BCIs, is their lower performance that makes them not adequate in applications that require a high accuracy. To overcome this disadvantage, we employed a Bayesian approach in which the DL-BCI was modeled as a Binary phase shift keying receiver for which t

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Petit, Jimmy, José Rouillard, and François Cabestaing. "EEG-based brain–computer interfaces exploiting steady-state somatosensory-evoked potentials: a literature review." Journal of Neural Engineering 18, no. 5 (2021): 051003. http://dx.doi.org/10.1088/1741-2552/ac2fc4.

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Abstract A brain–computer interface (BCI) aims to derive commands from the user’s brain activity in order to relay them to an external device. To do so, it can either detect a spontaneous change in the mental state, in the so-called ‘active’ BCIs, or a transient or sustained change in the brain response to an external stimulation, in ‘reactive’ BCIs. In the latter, external stimuli are perceived by the user through a sensory channel, usually sight or hearing. When the stimulation is sustained and periodical, the brain response reaches an oscillatory steady-state that can be detected rather eas

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