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Статті в журналах з теми "Brain interfacing"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Lee, Sungjun, Kyuha Park, Jeungeun Kum, et al. "Stretchable Surface Electrode Arrays Using an Alginate/PEDOT:PSS-Based Conductive Hydrogel for Conformal Brain Interfacing." Polymers 15, no. 1 (2022): 84. http://dx.doi.org/10.3390/polym15010084.

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An electrocorticogram (ECoG) is the electrical activity obtainable from the cerebral cortex and an informative source with considerable potential for future advanced applications in various brain-interfacing technologies. Considerable effort has been devoted to developing biocompatible, conformal, soft, and conductive interfacial materials for bridging devices and brain tissue; however, the implementation of brain-adaptive materials with optimized electrical and mechanical characteristics remains challenging. Herein, we present surface electrode arrays using the soft tough ionic conductive hyd

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2

Jellinger, K. A. "Toward Brain-Computer Interfacing." European Journal of Neurology 16, no. 3 (2009): e52-e52. http://dx.doi.org/10.1111/j.1468-1331.2008.02463.x.

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3

Singh, Satya P., Sachin Mishra, Sukrit Gupta, et al. "Functional Mapping of the Brain for Brain–Computer Interfacing: A Review." Electronics 12, no. 3 (2023): 604. http://dx.doi.org/10.3390/electronics12030604.

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Brain–computer interfacing has been applied in a range of domains including rehabilitation, neuro-prosthetics, and neurofeedback. Neuroimaging techniques provide insight into the structural and functional aspects of the brain. There is a need to identify, map and understand the various structural areas of the brain together with their functionally active roles for the accurate and efficient design of a brain–computer interface. In this review, the functionally active areas of the brain are reviewed by analyzing the research available in the literature on brain–computer interfacing in conjuncti

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4

Jackson, A., and E. E. Fetz. "Interfacing With the Computational Brain." IEEE Transactions on Neural Systems and Rehabilitation Engineering 19, no. 5 (2011): 534–41. http://dx.doi.org/10.1109/tnsre.2011.2158586.

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5

Daly, Ian, Duncan Williams, Alexis Kirke, et al. "Affective brain–computer music interfacing." Journal of Neural Engineering 13, no. 4 (2016): 046022. http://dx.doi.org/10.1088/1741-2560/13/4/046022.

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6

Xia, Kaijian, Yizhang Jiang, Yudong Zhang, and Wen Si. "Guest Editorial: Advanced Machine-Learning Methods for Brain-Machine Interfacing or Brain-Computer Interfacing." IEEE/ACM Transactions on Computational Biology and Bioinformatics 18, no. 5 (2021): 1643–44. http://dx.doi.org/10.1109/tcbb.2021.3078145.

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7

Min, Byoung-Kyong, and Klaus-Robert Müller. "Electroencephalography/sonication-mediated human brain–brain interfacing technology." Trends in Biotechnology 32, no. 7 (2014): 345–46. http://dx.doi.org/10.1016/j.tibtech.2014.04.001.

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8

Valencia, Daniel, Jameson Thies, and Amirhossein Alimohammad. "Frameworks for Efficient Brain-Computer Interfacing." IEEE Transactions on Biomedical Circuits and Systems 13, no. 6 (2019): 1714–22. http://dx.doi.org/10.1109/tbcas.2019.2947130.

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9

Bryan, Matthew J., Stefan A. Martin, Willy Cheung, and Rajesh P. N. Rao. "Probabilistic co-adaptive brain–computer interfacing." Journal of Neural Engineering 10, no. 6 (2013): 066008. http://dx.doi.org/10.1088/1741-2560/10/6/066008.

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10

Sunny, T. D., T. Aparna, P. Neethu, J. Venkateswaran, V. Vishnupriya, and P. S. Vyas. "Robotic Arm with Brain – Computer Interfacing." Procedia Technology 24 (2016): 1089–96. http://dx.doi.org/10.1016/j.protcy.2016.05.241.

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11

Abdulkader, Sarah N., Ayman Atia, and Mostafa-Sami M. Mostafa. "Brain computer interfacing: Applications and challenges." Egyptian Informatics Journal 16, no. 2 (2015): 213–30. http://dx.doi.org/10.1016/j.eij.2015.06.002.

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12

Rao, Rajesh, and Reinhold Scherer. "Brain-Computer Interfacing [In the Spotlight." IEEE Signal Processing Magazine 27, no. 4 (2010): 152–50. http://dx.doi.org/10.1109/msp.2010.936774.

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13

Nijholt, A., and D. Tan. "Brain-Computer Interfacing for Intelligent Systems." IEEE Intelligent Systems 23, no. 3 (2008): 72–79. http://dx.doi.org/10.1109/mis.2008.41.

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14

Himanshu, Lunia Meera Bagdai. "BRAIN MACHINE INTERFACING WITH IoT FUNTIONALITY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 8 (2016): 404–13. https://doi.org/10.5281/zenodo.59673.

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Анотація:

Brain dead people and people having any disability related to brain cannot normally communicate with others and for their betterment some electronic need to be developed and Brain Machine Interfacing (BMI) is one such solution. BMI includes extracting brain signals directly from the skull of the subject and interfacing it with a machine to determine the state of thinking and act accordingly. The brainwaves are collected by non-invasive electrodes and the output is fed to an amplifier and filter circuit which is then fed into an ADC to process and recognize which act the subject wish to perform

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15

Kübler, Andrea. "Brain-computer interfacing: science fiction has come true." Brain 136, no. 6 (2013): 2001–4. http://dx.doi.org/10.1093/brain/awt077.

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16

Paulmurugan, Kogulan, Vimalan Vijayaragavan, Sayantan Ghosh, Parasuraman Padmanabhan, and Balázs Gulyás. "Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)." Biosensors 11, no. 10 (2021): 389. http://dx.doi.org/10.3390/bios11100389.

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Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system originally developed for continuous and non-invasive monitoring of brain function by measuring blood oxygen concentration. Recent advancements in brain–computer interfacing allow us to control the neuron function of the brain by combining it with fNIRS to regulate cognitive function. In this review manuscript, we provide information regarding current advancement in fNIRS and how it provides advantages in developing brain–computer interfacing to enable neuron function. We also briefly discuss about how we ca

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17

Paulmurugan, Kogulan, Vimalan Vijayaragavan, Sayantan Ghosh, Parasuraman Padmanabhan, and Balázs Gulyás. "Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)." Biosensors 11, no. 10 (2021): 389. http://dx.doi.org/10.3390/bios11100389.

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Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system originally developed for continuous and non-invasive monitoring of brain function by measuring blood oxygen concentration. Recent advancements in brain–computer interfacing allow us to control the neuron function of the brain by combining it with fNIRS to regulate cognitive function. In this review manuscript, we provide information regarding current advancement in fNIRS and how it provides advantages in developing brain–computer interfacing to enable neuron function. We also briefly discuss about how we ca

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18

Ms., Dhiviyaa S., Sri Priyanka J. Ms., and Meghala S. Ms. "Brain Computer Interfacing using Electroencephalography and Convolutional Neural Networks." International Journal of Trend in Scientific Research and Development 4, no. 3 (2020): 878–80. https://doi.org/10.5281/zenodo.3892747.

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A brain computer interface BCI is a communication system that interprets brain activity into commands for a computer or other devices .This technology is the emphasis of a rapidly growing research and development initiative that is greatly exciting experts, and the public in general. Today, humans can use the electrical signals from brain activity to interrelate with, influence, or change their surroundings. The evolving field of BCI technology may allow persons in capable of speaking and or use their limbs to yet again interconnect or operate assistive devices for walking and functional objec

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19

Clausen, Jens. "Ethische Aspekte von Gehirn-Computer-Schnittstellen in motorischen Neuroprothesen." International Review of Information Ethics 5 (September 1, 2009): 25–32. http://dx.doi.org/10.29173/irie192.

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Title: Ethical Aspects of Brain-Computer Interfacing in Neuronal Motor Prostheses Brain-Computer interfacing is a highly promising and fast developing field of modern life sciences. Recent advances in neuroscience together with progressing miniaturization in micro systems provide insights in structure and functioning of the human brain and enable connections of technical components to neuronal structures as well. This possibly offers a future therapy for paralysed patients through neuronal motor prostheses. This paper identifies central ethical aspects which have to be considered in further pr

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20

Fazli, Siamac, and Seong-Whan Lee. "Brain Computer Interfacing: A Multi-Modal Perspective." Journal of Computing Science and Engineering 7, no. 2 (2013): 132–38. http://dx.doi.org/10.5626/jcse.2013.7.2.132.

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21

Chatelle, Camille, Srivas Chennu, Quentin Noirhomme, Damian Cruse, Adrian M. Owen, and Steven Laureys. "Brain–computer interfacing in disorders of consciousness." Brain Injury 26, no. 12 (2012): 1510–22. http://dx.doi.org/10.3109/02699052.2012.698362.

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22

Krauledat, Matthias, Michael Tangermann, Benjamin Blankertz, and Klaus-Robert Müller. "Towards Zero Training for Brain-Computer Interfacing." PLoS ONE 3, no. 8 (2008): e2967. http://dx.doi.org/10.1371/journal.pone.0002967.

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23

Stokes, M., and C. J. James. "W9 Brain-Computer Interfacing (BCI) in rehabilitation." Clinical Neurophysiology 117 (September 2006): 28. http://dx.doi.org/10.1016/j.clinph.2006.07.077.

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24

Mykhalchuk, Vitalii. "BRAIN-COMPUTER INTERFACING PROSPECTS IN E-SOCIETY." Management of Development of Complex Systems, no. 50 (June 27, 2022): 39–43. http://dx.doi.org/10.32347/2412-9933.2022.50.39-43.

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One of the achievements of modern society is the brain-computer interface (BCI) as a way to organize an intelligent environment. Such systems have already been successfully implemented and contribute to integrating people with disabilities into society, overcoming disorders, diagnosis, and monitoring well-being. Studying mental activity in business interaction, training, consumer activity, and entertainment is becoming increasingly popular. The study of the peculiarities of the functioning of human consciousness contributes to the successful integration of brain-computer interfaces into modern

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25

Viorel, Gaftea. "BRAIN Journal - Computational Intelligence in a Human Brain Model." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 7, no. 2 (2016): 17–24. https://doi.org/10.5281/zenodo.1044298.

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ABSTRACT This paper focuses on the current trends in the domain of brain research and on the current stage of development of the research for software and hardware solutions, communication capabilities between human beings and machines, new technologies, nanoscience and Internet of Things (IoT) devices. The proposed model for the Human Brain assumes the main similarities between human intelligence and the chess game thinking process. Tactical and strategic reasoning and the need to follow the rules of the chess game are all very similar to the activities of the human brain. The main objective

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26

Hussein, Al-Huraibi, and Prahlad Rao K. "Brain-Computer Interfacing and Classification of Cognitive Activities." Journal of Technological Science & Engineering (JTSE) 1, no. 2 (2020): 26–29. https://doi.org/10.5281/zenodo.4018557.

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Human intellect can be straightforwardly associated with the computers through a modern innovation known as Brain Computer Interface (BCI). Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS) based BCI empowers to associate the individuals with the encompassing world through brain signals noninvasively. This strategy of perusing the intellect through physiological signals by EEG and fNIRS sensors has made critical advance in neurological science and engine control inquire about. The BCI framework can record, analyze and decipher the framework input, procured from the

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27

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

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The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brai

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28

Brijesh, K. Soni* Deepak Mishra. "BRAIN-COMPUTER-INTERFACE: A CONCEPTUAL WORKING APPROACHES FOR NEUROTECHNOLOGY." Global Journal of Engineering Science and Research Management 3, no. 7 (2016): 113–17. https://doi.org/10.5281/zenodo.58280.

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This article addresses to the core part of a neurotechnology known as Brain-Computer-Interface and abbreviated as BCI. It is most growing research area in this era for neurotechnologists. Whole portion of this article broadly describe three working stages of BCI-System such as signal acquisition, signal processing and signal application, and signal processing further categorized as signal preprocessing, feature extraction, feature classification and feature translation. However working functionality varies according to its interfacing technique used as invasive-interface, semi-invasive-interfa

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29

Samek, Wojciech, Frank C. Meinecke, and Klaus-Robert Muller. "Transferring Subspaces Between Subjects in Brain--Computer Interfacing." IEEE Transactions on Biomedical Engineering 60, no. 8 (2013): 2289–98. http://dx.doi.org/10.1109/tbme.2013.2253608.

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30

Samek, Wojciech, Shinichi Nakajima, Motoaki Kawanabe, and Klaus-Robert Müller. "On robust parameter estimation in brain–computer interfacing." Journal of Neural Engineering 14, no. 6 (2017): 061001. http://dx.doi.org/10.1088/1741-2552/aa8232.

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31

Brandl, Stephanie, Laura Frølich, Johannes Höhne, Klaus-Robert Müller, and Wojciech Samek. "Brain–computer interfacing under distraction: an evaluation study." Journal of Neural Engineering 13, no. 5 (2016): 056012. http://dx.doi.org/10.1088/1741-2560/13/5/056012.

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32

Samek, Wojciech, Carmen Vidaurre, Klaus-Robert Müller, and Motoaki Kawanabe. "Stationary common spatial patterns for brain–computer interfacing." Journal of Neural Engineering 9, no. 2 (2012): 026013. http://dx.doi.org/10.1088/1741-2560/9/2/026013.

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33

Chavarriaga, Ricardo. "Standards for Neurotechnologies and Brain-Machine Interfacing [Standards]." IEEE Systems, Man, and Cybernetics Magazine 6, no. 3 (2020): 50–51. http://dx.doi.org/10.1109/msmc.2020.2995438.

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34

Kim, Song Dong, Kyuha Park, Sungjun Lee, et al. "Injectable and tissue-conformable conductive hydrogel for MRI-compatible brain-interfacing electrodes." Soft Science 3, no. 2 (2023): 18. http://dx.doi.org/10.20517/ss.2023.08.

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The development of flexible and stretchable materials has led to advances in implantable bio-integrated electronic devices that can sense physiological signals or deliver electrical stimulation to various organs in the human body. Such devices are particularly useful for neural interfacing systems that monitor neurodegenerative diseases such as Parkinson’s disease or epilepsy in real time. However, coupling current brain-interfacing devices with magnetic resonance imaging (MRI) remains a practical challenge due to resonance frequency variations from inorganic metal-based devices. Thus, organic

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35

Ienca, Marcello, and Pim Haselager. "Hacking the brain: brain–computer interfacing technology and the ethics of neurosecurity." Ethics and Information Technology 18, no. 2 (2016): 117–29. http://dx.doi.org/10.1007/s10676-016-9398-9.

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36

Mirabella, Giovanni, and Mikhail А. Lebedev. "Interfacing to the brain’s motor decisions." Journal of Neurophysiology 117, no. 3 (2017): 1305–19. http://dx.doi.org/10.1152/jn.00051.2016.

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It has been long known that neural activity, recorded with electrophysiological methods, contains rich information about a subject’s motor intentions, sensory experiences, allocation of attention, action planning, and even abstract thoughts. All these functions have been the subject of neurophysiological investigations, with the goal of understanding how neuronal activity represents behavioral parameters, sensory inputs, and cognitive functions. The field of brain-machine interfaces (BMIs) strives for a somewhat different goal: it endeavors to extract information from neural modulations to cre

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37

Gharabaghi, Alireza, Georgios Naros, Armin Walter, et al. "From assistance towards restoration with epidural brain-computer interfacing." Restorative Neurology and Neuroscience 32, no. 4 (2014): 517–25. http://dx.doi.org/10.3233/rnn-140387.

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38

Zheng, Honghui, Yilin Feng, Jiyuan Tang, and Shaohua Ma. "Interfacing brain organoids with precision medicine and machine learning." Cell Reports Physical Science 3, no. 7 (2022): 100974. http://dx.doi.org/10.1016/j.xcrp.2022.100974.

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39

Kindermans, Pieter-Jan, Martijn Schreuder, Benjamin Schrauwen, Klaus-Robert Müller, and Michael Tangermann. "True Zero-Training Brain-Computer Interfacing – An Online Study." PLoS ONE 9, no. 7 (2014): e102504. http://dx.doi.org/10.1371/journal.pone.0102504.

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40

Nazarpour, Kianoush, Peter Praamstra, R. Chris Miall, and Saeid Sanei. "Steady-State Movement Related Potentials for Brain–Computer Interfacing." IEEE Transactions on Biomedical Engineering 56, no. 8 (2009): 2104–13. http://dx.doi.org/10.1109/tbme.2009.2021529.

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41

Jayaram, Vinay, Matthias Hohmann, Jennifer Just, Bernhard Schölkopf, and Moritz Grosse-Wentrup. "Task-induced frequency modulation features for brain-computer interfacing." Journal of Neural Engineering 14, no. 5 (2017): 056015. http://dx.doi.org/10.1088/1741-2552/aa7778.

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42

Dijkstra, K. V., J. D. R. Farquhar, and P. W. M. Desain. "The N400 for brain computer interfacing: complexities and opportunities." Journal of Neural Engineering 17, no. 2 (2020): 022001. http://dx.doi.org/10.1088/1741-2552/ab702e.

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43

Gopalakrishnaiah, Shubratha Koralagundi, Kevin Joseph, and Ulrich G. Hofmann. "Microfluidic drive for flexible brain implants." Current Directions in Biomedical Engineering 3, no. 2 (2017): 675–78. http://dx.doi.org/10.1515/cdbme-2017-0142.

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AbstractFlexible polyimide probes, used for neuronal signal acquisition, are thought to reduce signal deteriorating gliosis, improving the quality of recordings in brain machine interfacing applications. These probes suffer from the disadvantage that they cannot penetrate brain tissue on their own, owing to their limited stiffness and low buckling forces. A microfluidic device as an external micro-drive which aids in the insertion of flexible polyimide neural probes in 0.6% Agarose gel is presented here.

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44

Wang, Suogang, and Christopher J. James. "Extracting Rhythmic Brain Activity for Brain-Computer Interfacing through Constrained Independent Component Analysis." Computational Intelligence and Neuroscience 2007 (2007): 1–9. http://dx.doi.org/10.1155/2007/41468.

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Анотація:

We propose a technique based on independent component analysis (ICA) with constraints, applied to the rhythmic electroencephalographic (EEG) data recorded from a brain-computer interfacing (BCI) system. ICA is a technique that can decompose the recorded EEG into its underlying independent components and in BCI involving motor imagery, the aim is to isolate rhythmic activity over the sensorimotor cortex. We demonstrate that, through the technique of spectrally constrained ICA, we can learn a spatial filter suited to each individual EEG recording. This can effectively extract discriminatory info

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45

Zander, Thorsten O., and Laurens R. Krol. "Team PhyPA: Brain-Computer Interfacing for Everyday Human-Computer Interaction." Periodica Polytechnica Electrical Engineering and Computer Science 61, no. 2 (2017): 209. http://dx.doi.org/10.3311/ppee.10435.

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Анотація:

Brain-computer interfaces can provide an input channel from humans to computers that depends only on brain activity, bypassing traditional means of communication and interaction. This input channel can be used to send explicit commands, but also to provide implicit input to the computer. As such, the computer can obtain information about its user that not only bypasses, but also goes beyond what can be communicated using traditional means. In this form, implicit input can potentially provide significant improvements to human-computer interaction. This paper describes a selection of work done b

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46

Tourigny, K., and T. Denison. "P.077 Reducing artifact during in bi-directional brain interfacing." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 51, s1 (2024): S37. http://dx.doi.org/10.1017/cjn.2024.183.

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Background: Bi-directional brain interfacing (closed loop DBS) is a modern focus of neuroengineering research. Most current clinical systems are open loop, allowing one way communication from the IPG battery to the brain. Bi-directional systems allow both stimulation and recording of neural activity (local field potential, LFP). The system algorithm can measure known pathologic LFPs to guide change in stimulation. However, recording LFPs from the brain encounters electrical artifact from the heart. Reducing artifact is imperative to accurate measurement of neural activity. Artifact will cause

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47

Pathre, Ayonija. "A Prefatory Analysis of Brain Computer Interfacing Based on EEG." ECS Transactions 107, no. 1 (2022): 6789–99. http://dx.doi.org/10.1149/10701.6789ecst.

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Анотація:

An extremely developing area of application systems science is defined by brain programming interface technology. In health fields, its contributions range from treatment to synaptic healing for severe injuries. The special fingerprint of mind reading and remote contact in several areas, such as education, self-regulation, manufacturing, marketing, protection, entertainment, and games. It induces shared trust between consumers and systems around them. Deep learning has already received mainstream recognition and has been used in numerous applications, like natural language processing (NLP), co

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48

Abadin, A. F. M. Zainul, Ahmed Imtiaz, Md Manik Ahmed, and Mithun Dutta. "A Brief Study of Binaural Beat: A Means of Brain-Computer Interfacing." Advances in Human-Computer Interaction 2021 (December 23, 2021): 1–8. http://dx.doi.org/10.1155/2021/6814208.

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The human brain tends to follow a rhythm. Sound has a significant impact on our physical and mental health. This sound technology uses binaural beat by generating two tones of marginally different frequencies in each individual ear to facilitate the improved focus of attention, emotion, calming, and sensory organization. Binaural beat helps in memory boosting, relaxation, and work performance. Again because of hearing a binaural beat sound, brainwave stimuli can be diagnosed to pick up a person’s sensitive information. Using this technology in brain-computer interfacing, it is possible to esta

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Umair, Aroosa, Ureeba Ashfaq, and Muhammad Gufran Khan. "Recent Trends, Applications, and Challenges of Brain-Computer Interfacing (BCI)." International Journal of Intelligent Systems and Applications 9, no. 2 (2017): 58–65. http://dx.doi.org/10.5815/ijisa.2017.02.08.

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Mecklinger, Axel. "Interfacing mind and brain: A neurocognitive model of recognition memory." Psychophysiology 37, no. 5 (2000): 565–82. http://dx.doi.org/10.1111/1469-8986.3750565.

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