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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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Tang, Feifang, Feiyang Yan, Yushan Zhong, Jinqian Li, Hui Gong, and Xiangning Li. "Optogenetic Brain–Computer Interfaces." Bioengineering 11, no. 8 (August 12, 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 chip-level embedded BCIs. The software is also constantly improving, with better real-time performance and accuracy and lower power consumption. On the other hand, as a cutting-edge technology spanning multidisciplinary fields including molecular biology, neuroscience, material engineering, and information processing, optogenetic BCIs have great application potential in neural decoding, enhancing brain function, and treating neural diseases. Here, we review the development and application of optogenetic BCIs. In the future, combined with other functional imaging techniques such as near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI), optogenetic BCIs can modulate the function of specific circuits, facilitate neurological rehabilitation, assist perception, establish a brain-to-brain interface, and be applied in wider application scenarios.
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Nijholt, Anton, and Chang S. Nam. "Arts and Brain-Computer Interfaces (BCIs)." Brain-Computer Interfaces 2, no. 2-3 (April 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 (July 2, 2016): 123–25. http://dx.doi.org/10.1080/2326263x.2016.1210989.
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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 (December 26, 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 elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.
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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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Valeriani, Davide, Caterina Cinel, and Riccardo Poli. "Brain–Computer Interfaces for Human Augmentation." Brain Sciences 9, no. 2 (January 24, 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 (August 28, 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, and combined with BCI could greatly enhance user interactions, improving the user experience by using brain signals as input with immersive environments as output. This paper addresses only noninvasive BCIs, since this kind of capture is the only one to not present risk to human health. As contributions of this work we highlight the survey of interactive systems based on BCIs focusing on HCI and VR applications, and a discussion on challenges and future of this subject matter.
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Mikołajewska, Emilia, and Dariusz Mikołajewski. "Ethical considerations in the use of brain-computer interfaces." Open Medicine 8, no. 6 (December 1, 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 NPs’ threats and limitations allow for wise planning and management in further clinical practice, especially in the area of long-term neurorehabilitation and care.
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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. Meanwhile, memristors can simulate synaptic plasticity and potentially enable real-time adaptive learning in BCIs. Preliminary studies demonstrate substantially improved signal processing, feature extraction, and classification capabilities when using SNNs and memristors in BCIs. This neuroinspired integration offers a compelling vision for personalized BCIs that continuously adapt to individual users. However, realizing the full potential relies on addressing lingering technical hurdles as well as emerging ethical considerations around user autonomy, privacy, responsibility, and access. Ultimately, interdisciplinary collaboration remains imperative to harness the promising trajectory of optimized BCIs while navigating the multifaceted challenges ahead.
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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 (May 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 in military operations and performance enhancement. Neurosurgeons will play a central role in the further development and implementation of BCIs, but they will also have to navigate important ethical questions in the translation of this highly promising technology. In the following commentary the authors discuss realistic expectations for BCI use in the military and underscore the intersection of the neurosurgeon's civic and clinical duty to care for those who serve their country.
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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 (July 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 signals in real time provides direct feedback to the patient and can be used to control a cursor on a computer or an exoskeleton. In this review, the authors describe the current state of ECoG-based BCIs that are approaching clinical viability for restoring lost communication and motor function in patients with amyotrophic lateral sclerosis or tetraplegia. These studies provide a proof of principle and the possibility that ECoG-based BCI technology may also be useful in the future for assisting in the cortical rehabilitation of patients who have suffered a stroke.
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Zhao, Yingzhen. "Wearable brain-computer interface technology and its application." Theoretical and Natural Science 15, no. 1 (December 4, 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 classification of passively induced emotional signals, highlighting the emergence of hands-free control systems like quadcopter control and industrial inspections. The discourse on active BCIs unveils advancements such as real-time handwriting and speech decoding, driver drowsiness detection, and emotion recognition, aided by machine learning techniques. Despite groundbreaking progress, challenges in algorithm optimization, adaptability, multimodal signal complexity, and ethics persist. Future directions emphasize the potential of deep learning and multimode input signals collaboration. The manuscript underscores the societal implications, particularly in rehabilitation, communication, and entertainment. The review, therefore, serves as both an appraisal and a roadmap for the burgeoning field of wearable BCIs, underlining its role as a pathway to enhance human capabilities and quality of life.
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Ünlü, Sudenaz Ceren. "Enhancing Accessibility through Brain-Computer Interfaces (BCIs) in Assistive Technology." Human Computer Interaction 8, no. 1 (November 19, 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 prosthetics, speech-generating devices, and smart home systems tailored for accessibility. The study also addresses challenges including signal processing accuracy, user adaptability, and ethical considerations surrounding data privacy and inclusivity. By analyzing advancements in machine learning algorithms and neurofeedback systems, the research provides insights into optimizing BCI functionality for practical deployment. Ultimately, this study highlights the role of BCIs in creating a more inclusive society by redefining the capabilities of assistive technologies.
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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 (March 22, 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 a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.
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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 (September 10, 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 ethical questions around BCIs. In short, the article asks what lessons can be learned about the ethics of BCIs from looking at the ethics of algorithms. To achieve these goals, the article proceeds as follows. First, a brief introduction into the algorithmic background of BCIs is given. Second, the debate about epistemic concerns and the ethics of algorithms is sketched. Finally, this debate is transferred to the ethics of BCIs.
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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 (April 1, 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-reported outcome measures can be used to evaluate improvements in how a patient feels, and we offer some considerations that should guide instrument selection. (b) Activities of daily living can be assessed to demonstrate improvements in how a patient functions, however, new instruments that are sensitive to increases in functional independence via the ability to perform digital tasks may be needed. (c) Benefits to how a patient survives has not previously been evaluated but establishing patient-initiated communication channels using BCIs might facilitate quantifiable improvements in health outcomes.
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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 (February 28, 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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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 thinking about them, without any physical movement required. The ability to decode these EEG signals is crucial for enabling effective communication between the brain and external devices. Deep learning (DL), particularly convolutional neural networks (CNNs) which are able to extract meaningful features from raw EEG data and accurately classify different types of imagined movements, has proven to be highly effective in decoding EEG signals generated during motor imagery tasks and has already made numerous contributions in this area. This article provides an initial overview of BCIs and CNNs, followed by an explanation of how CNNs decode EEG signals derived from motor imagery, as well as the utilization of BCIs for controlling exoskeleton devices. Finally, the current limitations and future directions in this field are discussed.
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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 (June 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 (January 2008): 51–62. http://dx.doi.org/10.1016/j.jneumeth.2007.08.010.
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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 (May 30, 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 various methods, including: Electrical stimulation: Directly stimulating nerves or brain tissue to evoke desired responses. Recording brain activity: Capturing electrical signals generated by the brain for further processing and interpretation. Common applications of neuroprosthetics include: Cochlear implants: Restoring hearing in individuals with severe hearing loss. Deep brain stimulation (DBS): Treating movement disorders like Parkinson's disease and essential tremor. Bionic limbs: Providing control of prosthetic arms and legs for individuals with limb loss. Brain-computer interfaces (BCIs): Enabling communication and control of external devices using brain signals alone. Neuroprosthetics offer a glimpse into the future of medicine and technology. With ongoing advancements, these devices have the potential to revolutionize how we treat neurological conditions, restore lost abilities, and even enhance human potential. However, significant challenges remain, including ensuring long-term safety, improving accuracy and reliability, and addressing ethical considerations. As research continues, neuroprosthetics holds immense potential to improve the lives of millions and redefine what it means to be human. The integration of artificial intelligence (AI) with neuroprosthetics has marked a significant milestone in the development of brain-computer interfaces (BCIs). This emerging synergy aims to enhance the quality of life for individuals with disabilities by restoring lost sensory, motor, and cognitive functions. This review article explores the advancements in AI-powered neuroprosthetics for BCIs, focusing on their design, functionality, and the ethical considerations that accompany their integration into medical practice.
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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 (July 10, 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 on commercial BCI systems. Each attack is examined in terms of its modus operandi and effective mitigation strategies.
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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 (September 1, 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 biocompatibility. In addition, we describe some advanced configurations and suggest potential applications for non-invasive BCIs based on flexible electrodes and consider future development prospects.
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Huang, Yiling. "The current clinical applications of invasive brain-computer interfaces." Theoretical and Natural Science 16, no. 1 (December 4, 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 the implementation and present state of BCI and provides a systematic evaluation of invasive BCIs, including the concepts of current invasive treatment techniques. The paper then review the current clinical applications of invasive BCIs technology, discuss its technical applications and benefits through case studies, and provide a comprehensive assessment of its risks. The prospects of invasive BCIs and their growing trend in the medical field are also reviewed.
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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 (November 2, 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 population in Germany aged 18–65 years, a representative online survey with 20 items was conducted in summer 2018 (n = 1000) and analysed by descriptive statistics. The survey assessed: affinity for technology; previous knowledge and experience concerning BCIs; the attitude towards ethical, social and legal implications of BCI use and demographic information. Our results indicate that BCIs are a unique and puzzling way of human–machine interaction. The findings reveal a positive view and high level of trust in BCIs on the one hand but on the other hand a wide range of ethical and anthropological concerns. Agency and responsibility were clearly attributed to the BCI user. The participants’ opinions were divided regarding the impact BCIs have on humankind. In summary, a high level of ambivalence regarding BCIs was found. We suggest better information of the public and the promotion of public deliberation about BCIs in order to ensure responsible development and application of this potentially disruptive technology.
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Zhu, Fangkun, Lu Jiang, Guoya Dong, Xiaorong Gao, and Yijun Wang. "An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces." Sensors 21, no. 4 (February 10, 2021): 1256. http://dx.doi.org/10.3390/s21041256.
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Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.
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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 (April 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 features extraction methods, like Independent Component Analysis (ICA) and Wavelet Transform (WT) respectively, are giving promising results that need to be explored. Different types of classifiers and combination of them have been used on EEG BCIs. Linear, neural and nonlinear Bayesian have been the most used classifiers providing accuracies ranges between 60% and 90%. Some demand more computational resources like Support Vector Machines (SVM) classifiers but give good generality. Linear Discriminant Analysis (LDA) classifiers provide poor generality but low computational resources, making them optimal for some real time BCIs. Better classifiers must be developed to tackle the large patterns variability across different subjects by using every available resource, method or technology.
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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 (February 6, 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. Results The tutorial responses directly address 4 major areas of interest for SLPs who specialize in AAC: (a) the current state of BCI with emphasis on SLP scope of practice (including the subareas: the way in which individuals access AAC with BCI, the efficacy of BCI for AAC, and the effects of fatigue), (b) populations for whom BCI is best suited, (c) the future of BCI as an addition to AAC access strategies, and (d) limitations of BCI. Conclusion Current BCIs have been designed as access methods for AAC rather than a replacement; therefore, SLPs can use existing knowledge in AAC as a starting point for clinical application. Additional training is recommended to stay updated with rapid advances in BCI.
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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 (October 28, 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, inequality, and the broader societal impact of cognitive enhancement technologies. We conclude that the development of eBCIs raises challenges far beyond practical pros and cons, prompting fundamental questions regarding the nature of conscious selfhood and about who—and what—we are, and ought, to be.
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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 (February 12, 2024): 1201. http://dx.doi.org/10.3390/s24041201.
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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 the most recent studies and findings about ergonomic issues in BCIs. We review dry electrodes that can be used to detect brain signals with high enough quality to apply in BCIs and discuss their advantages, disadvantages, and performance. Also, an overview is provided of the wide range of recent efforts to create new interface designs that do not induce fatigue or discomfort during everyday, long-term use. The basic principles of each technique are described, along with examples of current applications in BCI research. Finally, we demonstrate a user-friendly interface paradigm that uses dry capacitive electrodes that do not require any preparation procedure for EEG signal acquisition. We explore the capacitively measured steady-state visual evoked potential (SSVEP) response to an amplitude-modulated visual stimulus and the auditory steady-state response (ASSR) to an auditory stimulus modulated by familiar natural sounds to verify their availability for BCI. We report the first results of an online demonstration that adopted this ergonomic approach to evaluating BCI applications. We expect BCI to become a routine clinical, assistive, and commercial tool through advanced EEG monitoring techniques and innovative interface designs.
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Swan, Melanie. "The Future of Brain-Computer Interfaces." Journal of Ethics and Emerging Technologies 26, no. 2 (October 1, 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 speculative future scenarios such as digital selves and cloudmind collaborations. Applications could be adopted in tiers of advancing complexity and risk, starting with health tracking, followed by information seeking and entertainment, and finally, self-actualization. By linking brains to the Internet, BCIs could allow individuals to be more highly connectable not just to communications networks but also to other minds, and thus could enable participation in new kinds of collective applications such as a cloudmind. A cloudmind (or crowdmind) is the concept of multiple individual minds (human or machine) joined together to pursue a collaborative goal such as problem solving, idea generation, creative expression, or entertainment. The prospect of cloudminds raises questions about individual versus collective personhood. Some of the necessary conditions for individuals to feel comfortable in joining a cloudmind include privacy, security, reversibility, and retention of personal identity. Blockchain technology might be employed to orchestrate the security, automation, coordination, and credit-assignation requirements of cloudmind collaborations.
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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 (December 26, 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 BCIs, focusing on their impact on rehabilitation for stroke survivors, individuals with spinal cord injuries, and those with neurodegenerative disorders. Ethical challenges, such as data privacy, consent, and accessibility, are also examined. Through a review of case studies and emerging trends, this study highlights the potential of AI-enhanced BCIs to revolutionize neurorehabilitation and foster greater independence for individuals with disabilities.
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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 recording technology using implants with electrodes placed in brain closer to the signal source, and required more complicate operation. The second, non-invasive BCI, reads signals from the brain and nervous system and is based on electroencephalogram (EEG). Compared to invasive BCIs with their more accurate signal, transcranial BCIs communicate with the brain through the skull bones, muscles, and all tissues. Their use does not require intervention in the human body. To increase the effectiveness of training, there was chosen a physiotherapeutic method of transcranial electrical stimulation (TES) in combination with a braincomputer interface based on electroencephalography (EEG), as the most accessible non-invasive method of exposure and feedback due to BCI without known side effects to mental functions and personality. The use of brain-computer interfaces, in particular transcranial electrical stimulation in combination with electroencephalography, increases cognitive abilities in learning, including multitasking. This method can also be used to increase the effectiveness of human assimilation of the necessary new digital environments and is used not only for training complex professions, but also for the masses. Side effects on higher mental functions and personality have not been sufficiently studied to recommend or avoid the use of neurocomputer interfaces for widespread use in education.
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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 (March 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 (May 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 to identify potentially adversarial samples without changing the original model. Hence, the present study provides a comprehensive evaluation of several typical adversarial detection methods applied to EEG datasets. The experiments indicate that the detection method based on the kernel density estimation (KDE) shows the best performance under various adversarial attacks.
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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 (February 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 significant difference in selection accuracy. The results suggest that P300 BCIs can be used successfully in a 3D environment, and this suggests some novel ways of using BCIs in real world environments.
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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 (May 16, 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 the accuracy can be estimated a priori as a function of the signal-to-noise ratio. The results showed the measured accuracy to match the predefined target accuracy, thus validating this model that made possible to estimate in advance the classification accuracy on a trial-by-trial basis. This constitutes a novel methodology in the design of fully auditory DL-BCIs that let us first, define the target accuracy for a specific application and second, classify when the signal-to-noise ratio guarantees that target accuracy.
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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 (October 1, 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 easily. We focus our attention on electroencephalography-based BCIs (EEG-based BCI) in which a periodical signal, either mechanical or electrical, stimulates the user skin. This type of stimulus elicits a steady-state response of the somatosensory system that can be detected in the recorded EEG. The oscillatory and phase-locked voltage component characterising this response is called a steady-state somatosensory-evoked potential (SSSEP). It has been shown that the amplitude of the SSSEP is modulated by specific mental tasks, for instance when the user focuses their attention or not to the somatosensory stimulation, allowing the translation of this variation into a command. Actually, SSSEP-based BCIs may benefit from straightforward analysis techniques of EEG signals, like reactive BCIs, while allowing self-paced interaction, like active BCIs. In this paper, we present a survey of scientific literature related to EEG-based BCI exploiting SSSEP. Firstly, we endeavour to describe the main characteristics of SSSEPs and the calibration techniques that allow the tuning of stimulation in order to maximise their amplitude. Secondly, we present the signal processing and data classification algorithms implemented by authors in order to elaborate commands in their SSSEP-based BCIs, as well as the classification performance that they evaluated on user experiments.
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Rezaei Tabar, Yousef, and Ugur Halici. "Brain Computer Interfaces for Silent Speech." European Review 25, no. 2 (December 22, 2016): 208–30. http://dx.doi.org/10.1017/s1062798716000569.
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Brain Computer Interface (BCI) systems provide control of external devices by using only brain activity. In recent years, there has been a great interest in developing BCI systems for different applications. These systems are capable of solving daily life problems for both healthy and disabled people. One of the most important applications of BCI is to provide communication for disabled people that are totally paralysed. In this paper, different parts of a BCI system and different methods used in each part are reviewed. Neuroimaging devices, with an emphasis on EEG (electroencephalography), are presented and brain activities as well as signal processing methods used in EEG-based BCIs are explained in detail. Current methods and paradigms in BCI based speech communication are considered.
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Mohammed Mosa, Mosa Muntadher, and Arief Ruhullah A. Harris. "Home Automation for Disabled Using Brain Computer Interface and Raspberry Pi." Journal of Human Centered Technology 3, no. 2 (August 9, 2024): 21–28. http://dx.doi.org/10.11113/humentech.v3n2.77.
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Electroencephalography (EEG)-based smart home control systems are a key application of Brain-Computer Interfaces (BCIs). BCIs empower people with disabilities to achieve greater independence at home. These interfaces allow individuals with severe impairments to interact with their surroundings and communicate with others. Many people with special needs, particularly the elderly, face significant challenges in their daily lives that can severely impact their quality of life. This project aims to develop a non-invasive BCI for people with special needs to control household appliances and access an emergency system. A graphical user interface (GUI) will provide users with the ability to manage various smart home devices. This system will also benefit people with physical limitations by granting them greater control over their home's electrical and electronic appliances. This study successfully developed and implemented a BCI system for controlling home appliances. By leveraging the Steady-State Visually Evoked Potentials (SSVEPs) generated in response to flickering visual stimuli, the BCI system accurately interpreted user intentions through signal analysis and classification techniques.
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Kim, Minju, Min-Ki Kim, Minho Hwang, Hyun-Young Kim, Jeongho Cho, and Sung-Phil Kim. "Online Home Appliance Control Using EEG-Based Brain–Computer Interfaces." Electronics 8, no. 10 (September 30, 2019): 1101. http://dx.doi.org/10.3390/electronics8101101.
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Brain–computer interfaces (BCIs) allow patients with paralysis to control external devices by mental commands. Recent advances in home automation and the Internet of things may extend the horizon of BCI applications into daily living environments at home. In this study, we developed an online BCI based on scalp electroencephalography (EEG) to control home appliances. The BCI users controlled TV channels, a digital door-lock system, and an electric light system in an unshielded environment. The BCI was designed to harness P300 and N200 components of event-related potentials (ERPs). On average, the BCI users could control TV channels with an accuracy of 83.0% ± 17.9%, the digital door-lock with 78.7% ± 16.2% accuracy, and the light with 80.0% ± 15.6% accuracy, respectively. Our study demonstrates a feasibility to control multiple home appliances using EEG-based BCIs.
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Yang, Siyu, Ruobing Li, Hongtao Li, Ke Xu, Yuqing Shi, Qingyong Wang, Tiansong Yang, and Xiaowei Sun. "Exploring the Use of Brain-Computer Interfaces in Stroke Neurorehabilitation." BioMed Research International 2021 (June 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/9967348.
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With the continuous development of artificial intelligence technology, “brain-computer interfaces” are gradually entering the field of medical rehabilitation. As a result, brain-computer interfaces (BCIs) have been included in many countries’ strategic plans for innovating this field, and subsequently, major funding and talent have been invested in this technology. In neurological rehabilitation for stroke patients, the use of BCIs opens up a new chapter in “top-down” rehabilitation. In our study, we first reviewed the latest BCI technologies, then presented recent research advances and landmark findings in BCI-based neurorehabilitation for stroke patients. Neurorehabilitation was focused on the areas of motor, sensory, speech, cognitive, and environmental interactions. Finally, we summarized the shortcomings of BCI use in the field of stroke neurorehabilitation and the prospects for BCI technology development for rehabilitation.
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Siviero, Ilaria, Gloria Menegaz, and Silvia Francesca Storti. "Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain–Computer Interface Performance." Sensors 23, no. 17 (August 30, 2023): 7520. http://dx.doi.org/10.3390/s23177520.
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(1) Background: in the field of motor-imagery brain–computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which leads to limited results. To address this limitation, there has been growing interest in leveraging functional brain connectivity (FC) as a feature in MI-BCIs. However, the high inter- and intra-subject variability has so far limited its effectiveness in this domain. (2) Methods: we propose a novel signal processing framework that addresses this challenge. We extracted translation-invariant features (TIFs) obtained from a scattering convolution network (SCN) and brain connectivity features (BCFs). Through a feature fusion approach, we combined features extracted from selected channels and functional connectivity features, capitalizing on the strength of each component. Moreover, we employed a multiclass support vector machine (SVM) model to classify the extracted features. (3) Results: using a public dataset (IIa of the BCI Competition IV), we demonstrated that the feature fusion approach outperformed existing state-of-the-art methods. Notably, we found that the best results were achieved by merging TIFs with BCFs, rather than considering TIFs alone. (4) Conclusions: our proposed framework could be the key for improving the performance of a multiclass MI-BCI system.
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Vidaurre, Carmen, Claudia Sannelli, Klaus-Robert Müller, and Benjamin Blankertz. "Machine-Learning-Based Coadaptive Calibration for Brain-Computer Interfaces." Neural Computation 23, no. 3 (March 2011): 791–816. http://dx.doi.org/10.1162/neco_a_00089.
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Brain-computer interfaces (BCIs) allow users to control a computer application by brain activity as acquired (e.g., by EEG). In our classic machine learning approach to BCIs, the participants undertake a calibration measurement without feedback to acquire data to train the BCI system. After the training, the user can control a BCI and improve the operation through some type of feedback. However, not all BCI users are able to perform sufficiently well during feedback operation. In fact, a nonnegligible portion of participants (estimated 15%–30%) cannot control the system (a BCI illiteracy problem, generic to all motor-imagery-based BCIs). We hypothesize that one main difficulty for a BCI user is the transition from offline calibration to online feedback. In this work, we investigate adaptive machine learning methods to eliminate offline calibration and analyze the performance of 11 volunteers in a BCI based on the modulation of sensorimotor rhythms. We present an adaptation scheme that individually guides the user. It starts with a subject-independent classifier that evolves to a subject-optimized state-of-the-art classifier within one session while the user interacts continuously. These initial runs use supervised techniques for robust coadaptive learning of user and machine. Subsequent runs use unsupervised adaptation to track the features’ drift during the session and provide an unbiased measure of BCI performance. Using this approach, without any offline calibration, six users, including one novice, obtained good performance after 3 to 6 minutes of adaptation. More important, this novel guided learning also allows participants with BCI illiteracy to gain significant control with the BCI in less than 60 minutes. In addition, one volunteer without sensorimotor idle rhythm peak at the beginning of the BCI experiment developed it during the course of the session and used voluntary modulation of its amplitude to control the feedback application.
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İbişağaoğlu, Duru. "Neuro-Responsive AI: Pioneering Brain-Computer Interfaces for Enhanced Human-Computer Interaction." Next Frontier For Life Sciences and AI 8, no. 1 (November 14, 2024): 115. http://dx.doi.org/10.62802/qpefwc98.
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The convergence of artificial intelligence (AI) and neuroscience has propelled the development of brain-computer interfaces (BCIs), creating new paradigms for human-computer interaction (HCI). Neuro-responsive AI leverages real-time neural signals to enable seamless communication between the brain and external devices, revolutionizing fields such as assistive technology, healthcare, and user experience design. By decoding neural activity, AI-powered BCIs can enhance cognitive capabilities, restore lost functions, and open new possibilities for immersive virtual and augmented reality environments. This research explores the underlying mechanisms, technological advancements, and ethical considerations associated with neuro-responsive AI. It delves into the integration of machine learning algorithms for decoding neural signals, adaptive feedback systems for personalized interactions, and hardware innovations in electrode design for non-invasive applications. Despite its transformative potential, challenges such as data privacy, signal noise reduction, and equitable access must be addressed to ensure responsible deployment. This study aims to bridge the gap between neuroscience and technology, providing insights into the future of neuro-responsive AI in reshaping HCI.
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Bosworth, Russell, and Jacob. "Update of fNIRS as an Input to Brain–Computer Interfaces: A Review of Research from the Tufts Human–Computer Interaction Laboratory." Photonics 6, no. 3 (August 4, 2019): 90. http://dx.doi.org/10.3390/photonics6030090.
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Over the past decade, the Human–Computer Interaction (HCI) Lab at Tufts University has been developing real-time, implicit Brain–Computer Interfaces (BCIs) using functional near-infrared spectroscopy (fNIRS). This paper reviews the work of the lab; we explore how we have used fNIRS to develop BCIs that are based on a variety of human states, including cognitive workload, multitasking, musical learning applications, and preference detection. Our work indicates that fNIRS is a robust tool for the classification of brain-states in real-time, which can provide programmers with useful information to develop interfaces that are more intuitive and beneficial for the user than are currently possible given today’s human-input (e.g., mouse and keyboard).
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De Souza, Gabriel Henrique, Gabriel Oliveira Moreira Faria, Luciana Paixão Motta, Heder Soares Bernardino, and Alex Borges Vieira. "EEG data for motor imagery brain-computer interface using low-cost equipment." Latin American Data in Science 2, no. 2 (May 15, 2023): 67–73. http://dx.doi.org/10.53805/lads.v2i2.49.
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EEG-based brain-computer interfaces (BCI) for motor imagery recognition can be used in many applications, including prosthesis control, post-stroke motor rehabilitation, communication, and videogames. Such BCIs usually need to be calibrated with EEG data before being used. The calibration can use data from either a single person, the same person who will use the equipment, or a group of different people. However, although BCIs are increasingly used in research and real-world problems, high equipment costs prevent their popularization in personal use applications. For this reason, there are many ongoing efforts to create more affordable BCI devices. Nevertheless, most public datasets for motor imagery EEG-BCIs still use expensive equipment. Therefore, our work presents a dataset for EEG-based motor imagery BCIs focused on personal use applications. Using a low-cost 16-electrode EEG OpenBCI Cyton+Daisy Biosensing Board, we recorded the brain signals of 6 subjects while they imagined the movements of their hands, resulting in a dataset containing 960 trials of left and right-hand motor imagery. This dataset can be used to calibrate BCIs using similar low-cost equipment as well as study the signals generated by such equipment.
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Li, Jinning, Yuhang Cheng, Minling Gu, Zhen Yang, Lisi Zhan, and Zhanhong Du. "Sensing and Stimulation Applications of Carbon Nanomaterials in Implantable Brain-Computer Interface." International Journal of Molecular Sciences 24, no. 6 (March 8, 2023): 5182. http://dx.doi.org/10.3390/ijms24065182.
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Implantable brain–computer interfaces (BCIs) are crucial tools for translating basic neuroscience concepts into clinical disease diagnosis and therapy. Among the various components of the technological chain that increases the sensing and stimulation functions of implanted BCI, the interface materials play a critical role. Carbon nanomaterials, with their superior electrical, structural, chemical, and biological capabilities, have become increasingly popular in this field. They have contributed significantly to advancing BCIs by improving the sensor signal quality of electrical and chemical signals, enhancing the impedance and stability of stimulating electrodes, and precisely modulating neural function or inhibiting inflammatory responses through drug release. This comprehensive review provides an overview of carbon nanomaterials’ contributions to the field of BCI and discusses their potential applications. The topic is broadened to include the use of such materials in the field of bioelectronic interfaces, as well as the potential challenges that may arise in future implantable BCI research and development. By exploring these issues, this review aims to provide insight into the exciting developments and opportunities that lie ahead in this rapidly evolving field.