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Journal articles on the topic 'Brain Machine Interface'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 June 2025

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1

Sasne, Ajinkya, Ashutosh Banait, Apurva Raut, and Vishal Raut. "Brain Machine Interface." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3641–42. http://dx.doi.org/10.22214/ijraset.2022.43218.

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Abstract— Brain Machine Interface is also known as ‘A brain-computer inteface’.A brain-computer interface (BCI), sometimes called a direct neural interface or a brain-machine interface, is a direct communication pathway between a human or animal brain and an external device. In one-way BCIs, computers either accept commands from the brain or send signals to it (for example, to restore vision) but not both. Two-way BCIs would allow brains and external devices to exchange information in both directions but have yet to be successfully implanted in animals or humans. In this definition, the word brain means the brain or nervous system of an organic life form rather than the mind. Computer means any processing or computational device, from simple circuits to silicon chips. Research on BCIs began in the 1970s, but it wasn't until the mid1990s that the first working experimental implants in humans appeared. Following years of animal experimentation, early working implants in humans now exist, designed to restore damaged hearing, sight and movement. With recent advances in technology and knowledge, pioneering researchers could now conceivably attempt to produce BCIs that augment human functions rather than simply restoring them, previously only a possibility in science fiction.

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2

Kamalakannan, R., and N. Ravi Kumar. "Brain Machine Interface." International Journal of Advanced Scientific Technologies in Engineering and Management Sciences 2, no. 12 (2016): 1. http://dx.doi.org/10.22413/ijastems/2016/v2/i12/41279.

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3

Nair, P. "Brain-machine interface." Proceedings of the National Academy of Sciences 110, no. 46 (2013): 18343. http://dx.doi.org/10.1073/pnas.1319310110.

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4

Galiautdinov, Rinat. "Brain Machine Interface." International Journal of Applied Research in Bioinformatics 10, no. 1 (2020): 26–36. http://dx.doi.org/10.4018/ijarb.2020010102.

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The main purpose of the article is to provide the solution which allows the muscles to work in a situation when neural connection is corrupted either due to illness or injury, which usually causes paralysis. The research is on the interpretation of the brain signals based on the analysis of neurotransmitters and the transformation of this analysis into the electric signals effecting on the muscle in the situation when neural circuit between a sensor/inter neuron and a motor neuron is broken. This method would allow paralyzed people to move their limbs and potentially to walk.

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5

Sanchez, Justin C., and José C. Principe. "Brain–Machine Interface Engineering." Synthesis Lectures on Biomedical Engineering 2, no. 1 (2007): 1–234. http://dx.doi.org/10.2200/s00053ed1v01y200710bme017.

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6

Trajkovic, Ljiljana. "Brain-Machine Interface Systems." IEEE Systems, Man, and Cybernetics Magazine 6, no. 3 (2020): 4–8. http://dx.doi.org/10.1109/msmc.2020.2995186.

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7

Yin, Jing Hai, Zheng Dong Mu, and Jian Feng Hu. "The Application of BCI Technology in Android RPG Game." Applied Mechanics and Materials 496-500 (January 2014): 2015–18. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.2015.

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To enhance human interaction with machines, research interest is growing to develop a Brain-Computer Interface (BCI), which allows communication of a human with a machine only by use of brain signals. In this paper, one type of android RPG game was designed for application of brain computer interfaces.

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8

Patel, Prachi. "The Brain-Machine Interface, Unplugged." IEEE Spectrum 46, no. 10 (2009): 13–14. http://dx.doi.org/10.1109/mspec.2009.5267979.

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9

Shanechi, Maryam M. "Brain–Machine Interface Control Algorithms." IEEE Transactions on Neural Systems and Rehabilitation Engineering 25, no. 10 (2017): 1725–34. http://dx.doi.org/10.1109/tnsre.2016.2639501.

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10

Shindo, Keiichiro, Junichi Ushiba, and Meigen Liu. "Neurorehabilitation with brain–machine interface." Neuroscience Research 68 (January 2010): e45. http://dx.doi.org/10.1016/j.neures.2010.07.444.

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11

Yoshimine, Toshiki, Masayuki Hirata, Takuhumi Yanagisawa, et al. "ECoG-based Brain–Machine Interface." Neuroscience Research 65 (January 2009): S33. http://dx.doi.org/10.1016/j.neures.2009.09.1686.

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12

Thakor, N. V. "Translating the Brain-Machine Interface." Science Translational Medicine 5, no. 210 (2013): 210ps17. http://dx.doi.org/10.1126/scitranslmed.3007303.

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13

Yokoi, Hiroshi. "Cyborg (Brain–Machine/Computer Interface)." Advanced Robotics 23, no. 11 (2009): 1451–54. http://dx.doi.org/10.1163/016918609x12469657764904.

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14

Ramesh, B., Anandhi R J, Vanya Arun, et al. "A Review on Biomaterials for Neural Interfaces: Enhancing Brain-Machine Interfaces." E3S Web of Conferences 505 (2024): 01005. http://dx.doi.org/10.1051/e3sconf/202450501005.

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Biomaterials are essential to the development of neural interfaces, including brainmachine interfaces. Biomaterial methods improve neural interface functionality, compatibility, and longevity, enabling brain-device communication. An extensive investigation of biomaterials utilized in brain electrode arrays, neural probes, & implantable devices rely on how materials affect neural signals recording, stimulation, & tissue contact. It also investigates how biomaterials, bioelectronics and 3D printing could improve neural interfaces. Biomaterials modulate neuroinflammatory responses, enhance brain tissue regeneration, and promote neural interface longevity. This study shows the potential for change of biomaterial-based neural interfaces in neuroprosthetics, neurological rehabilitation, and fundamental neuroscience research, addressing the need for brain-machine relationship and neurotechnology innovation. These findings suggest expanding biomaterials research and development to advance and sustain neural interface technologies for future use.

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15

Chung, Jaeho, Jae-hwan Bong, Suhun Jung, and Shinsuk Park. "1P1-B08 Feasibility of EEG as Human-Machine Interface Modality Analysis of EEG data from Brain-Machine Interface(Neurorobotics & Cognitive Robotics)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2013 (2013): _1P1—B08_1—_1P1—B08_3. http://dx.doi.org/10.1299/jsmermd.2013._1p1-b08_1.

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16

Suzuki, Takafumi. "Motor output-type Brain-Machine Interface." Brain & Neural Networks 19, no. 3 (2012): 112–17. http://dx.doi.org/10.3902/jnns.19.112.

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17

YOKOI, Hiroshi, and Yinlai JIANG. "Output Device for Brain Machine Interface." Journal of the Japan Society for Precision Engineering 83, no. 11 (2017): 1000–1005. http://dx.doi.org/10.2493/jjspe.83.1000.

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18

Harris, Kenneth D. "Sleep replay meets brain–machine interface." Nature Neuroscience 17, no. 8 (2014): 1019–21. http://dx.doi.org/10.1038/nn.3769.

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19

yasyam, M.Kishore Babu, G. Suresh, S. V. S. Ja. "Semi Autonomous Hybrid Brain Machine Interface." International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 1, no. 1 (2012): 41–45. http://dx.doi.org/10.15662/ijareeie.2012.0101007.

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20

Peck, Morgen. "Update: Standardizing the Brain-Machine Interface." IEEE Spectrum 45, no. 4 (2008): 16. http://dx.doi.org/10.1109/mspec.2008.4476435.

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21

Graf, Arnulf B. A., and Richard A. Andersen. "Brain–machine interface for eye movements." Proceedings of the National Academy of Sciences 111, no. 49 (2014): 17630–35. http://dx.doi.org/10.1073/pnas.1419977111.

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22

Chaudhary, U., N. Birbaumer, and M. R. Curado. "Brain-Machine Interface (BMI) in paralysis." Annals of Physical and Rehabilitation Medicine 58, no. 1 (2015): 9–13. http://dx.doi.org/10.1016/j.rehab.2014.11.002.

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23

RHEE*, Chunghi. "Review of Brain-Machine Interface Technology." New Physics: Sae Mulli 60, no. 1 (2010): 1–22. http://dx.doi.org/10.3938/npsm.60.1.

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24

Machado, Andre. "New Frontier: The Brain Machine Interface." Neuromodulation: Technology at the Neural Interface 16, no. 1 (2013): 6–7. http://dx.doi.org/10.1111/ner.12021.

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25

Liu, Yiqun, Jiaxin Mao, Xiaohui Xie, Min Zhang, and Shaoping Ma. "Challenges in designing a brain-machine search interface." ACM SIGIR Forum 54, no. 2 (2020): 1–13. http://dx.doi.org/10.1145/3483382.3483387.

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While search engines have reshaped how human beings learn and think, the interaction paradigm of search has remained relatively stable for decades. With the development of neural science and biomedical engineering, it is possible to build a direct communication pathway between a computing device and the human brain via Brain-machine Interfaces (BMIs), which may revolutionize the search paradigm in a predictable future. Therefore, in this paper, we extensively discuss the possibility, benefits, and potential challenges in using BMI as a new interface for search, and call for more research efforts in this promising direction.

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26

Musk, Elon. "An Integrated Brain-Machine Interface Platform With Thousands of Channels." Journal of Medical Internet Research 21, no. 10 (2019): e16194. http://dx.doi.org/10.2196/16194.

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Brain-machine interfaces hold promise for the restoration of sensory and motor function and the treatment of neurological disorders, but clinical brain-machine interfaces have not yet been widely adopted, in part, because modest channel counts have limited their potential. In this white paper, we describe Neuralink’s first steps toward a scalable high-bandwidth brain-machine interface system. We have built arrays of small and flexible electrode “threads,” with as many as 3072 electrodes per array distributed across 96 threads. We have also built a neurosurgical robot capable of inserting six threads (192 electrodes) per minute. Each thread can be individually inserted into the brain with micron precision for avoidance of surface vasculature and targeting specific brain regions. The electrode array is packaged into a small implantable device that contains custom chips for low-power on-board amplification and digitization: The package for 3072 channels occupies less than 23×18.5×2 mm3. A single USB-C cable provides full-bandwidth data streaming from the device, recording from all channels simultaneously. This system has achieved a spiking yield of up to 70% in chronically implanted electrodes. Neuralink’s approach to brain-machine interface has unprecedented packaging density and scalability in a clinically relevant package.

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27

O’Doherty, Joseph E., Mikhail A. Lebedev, Peter J. Ifft, et al. "Active tactile exploration using a brain–machine–brain interface." Nature 479, no. 7372 (2011): 228–31. http://dx.doi.org/10.1038/nature10489.

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28

Datar, Dinesh, and R. N. Khobragade. "Mental State Prediction Using Machine Learning and EEG Signal." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (2023): 07–12. http://dx.doi.org/10.17762/ijritcc.v11i4.6374.

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One of the most exciting areas of computer science right now is brain-computer interface (BCI) research. A conduit for data flow between both the brain as well as an electronic device is the brain-computer interface (BCI). Researchers in several disciplines have benefited from the advancements made possible by brain-computer interfaces. Primary fields of study include healthcare and neuroergonomics. Brain signals could be used in a variety of ways to improve healthcare at every stage, from diagnosis to rehabilitation to eventual restoration. In this research, we demonstrate how to classify EEG signals of brain waves using machine learning algorithms for predicting mental health states. The XGBoost algorithm's results have an accuracy of 99.62%, which is higher than that of any other study of its kind and the best result to date for diagnosing people's mental states from their EEG signals. This discovery will aid in taking efforts [1] to predict mental state using EEG signals to the next level.

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29

Allen, Machel M. A. "Brain Machine Interface (BMI) for Spinal Injuries." International Journal of Advanced Engineering Research and Science 7, no. 9 (2020): 351–67. http://dx.doi.org/10.22161/ijaers.79.42.

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30

SUZUKI, Takafumi. "Neural Recording System for Brain-Machine Interface." Journal of the Japan Society for Precision Engineering 83, no. 11 (2017): 996–99. http://dx.doi.org/10.2493/jjspe.83.996.

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31

Nikolic, Nemanja, Ljubisa Bojic, and Lana Tucakovic. "Brain-machine interface: New challenge for humanity." Filozofija i drustvo 33, no. 2 (2022): 283–96. http://dx.doi.org/10.2298/fid2202283n.

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The aim of this paper is to clarify specific aspects of the impact of the brain-machine interface on our understanding of subjectivity. The brain-machine interface is presented as a phase of cyborgization of humans. Some projects in the field of brain-machine interface are aimed at enabling consensual telepathy - communication without symbolic mediation. Consensual telepathy refers to one of potential ways of transmission of information within singularity. Therefore, consensual telepathy is an important aspect of singularity. Singularity or human-machine symbiosis shows some similarities with child-mother unity. Therefore, the psychodynamic perspective might be considered useful in thinking about human-machine symbiosis. Knowledge from developmental psychodynamic psychology combined with insights by Slavoj Zizek and Jean Baudrillard provides an additional perspective looking at human-machine symbiosis. The paper claims that if consensual telepathy becomes another way of communication, it will have the potential to annihilate subjectivity making it schizophrenic. At the same time, we look at the possibility of an escape from our inner world through the prism of addictions.

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32

Wu, Zhao-hui. "Brain-machine interface (BMI) and cyborg intelligence." Journal of Zhejiang University SCIENCE C 15, no. 10 (2014): 805–6. http://dx.doi.org/10.1631/jzus.c1400325.

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33

Greenberg, Anastasia, Alexis Cohen, and Monica Grewal. "Patent landscape of brain–machine interface technology." Nature Biotechnology 39, no. 10 (2021): 1194–99. http://dx.doi.org/10.1038/s41587-021-01071-7.

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34

SHIMATANI, Yuichi. "Technological Trend of Brain^|^mdash;Machine Interface." Journal of The Institute of Electrical Engineers of Japan 132, no. 7 (2012): 421–24. http://dx.doi.org/10.1541/ieejjournal.132.421.

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35

Lebedev, Mikhail A., Andrew J. Tate, Timothy L. Hanson, et al. "Future developments in brain-machine interface research." Clinics 66 (2011): 25–32. http://dx.doi.org/10.1590/s1807-59322011001300004.

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36

Oki, Keisuke. ""Brain Wave Rider": A Human-Machine Interface." Leonardo 28, no. 4 (1995): 307. http://dx.doi.org/10.2307/1576195.

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37

DiGiovanna, J., B. Mahmoudi, J. Fortes, J. C. Principe, and J. C. Sanchez. "Coadaptive Brain–Machine Interface via Reinforcement Learning." IEEE Transactions on Biomedical Engineering 56, no. 1 (2009): 54–64. http://dx.doi.org/10.1109/tbme.2008.926699.

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38

Takano, Kouji, Shigeru Toyama, Tomoaki Komatsu, Yasoichi Nakajima, and Kenji Kansaku. "Metal pin electrode for brain–machine interface." Neuroscience Research 71 (September 2011): e201. http://dx.doi.org/10.1016/j.neures.2011.07.870.

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39

Demetriades, Andreas K., Christina K. Demetriades, Colin Watts, and Keyoumars Ashkan. "Brain-machine interface: The challenge of neuroethics." Surgeon 8, no. 5 (2010): 267–69. http://dx.doi.org/10.1016/j.surge.2010.05.006.

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40

Courtine, Grégoire, Silvestro Micera, Jack DiGiovanna, and José del R Millán. "Brain–machine interface: closer to therapeutic reality?" Lancet 381, no. 9866 (2013): 515–17. http://dx.doi.org/10.1016/s0140-6736(12)62164-3.

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41

Joseph, Anthony B. "Design considerations for the brain-machine interface." Medical Hypotheses 17, no. 3 (1985): 191–95. http://dx.doi.org/10.1016/0306-9877(85)90124-0.

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42

Wang, Yufan. "Assistive technology implementation with brain-machine interface." Theoretical and Natural Science 5, no. 1 (2023): 640–48. http://dx.doi.org/10.54254/2753-8818/5/20230410.

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As the development of the Brain Computer Interface, it can be a possible way for human to control the external devices by the mind. This can be good news for the handicapped people whose life qualities are greatly decreased. In this paper, designed an assistive robot arm for those disabled. The assistive robot arm mainly contains a filter, a microcontroller and a motor drive system. The filter can filter out the noises while the microcontroller judging the brain signals and turn it into an actual control signal. The actual control signal will finally drive the motor and for the sake of safety and stability, a PID circuit was add in the motor drive system. The mechanic arm designed can turn what people think to the actual robot behaviour and this will obviously increase the life quality of the disabled people. Its also a light weight system with a wide range of versatility and will bring benefits to all the handicapped.

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43

Ito, Tomotaka, Satoshi Ushii, Takafumi Sameshima, Yoshihiro Mitsui, Shohei Ohgi, and Chihiro Mizuike. "Design of Brain-Machine Interface Using Near-Infrared Spectroscopy." Journal of Robotics and Mechatronics 25, no. 6 (2013): 1000–1010. http://dx.doi.org/10.20965/jrm.2013.p1000.

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In recent years, the fields of robotics and medical science have been paying close attention to brainmachine interface (BMI) systems. BMI observes human cerebral activity and use the collected data as the input to various instruments. If such a systemcould be effectively realized, it could be used as a new intuitive input interface for application to human-robot interactions, welfare scenarios, etc. In this paper, we discussed a design problem related to a BMI system using near-infrared spectroscopy (NIRS). We developed a brain state classifier based on the learning vector quantization (LVQ) method. The proposed method classifies the cerebral blood flow patterns and outputs the brain state estimate. The classification experiments showed that the proposed method can successfully classify not only human physical motions and motor imageries, but also human emotions and human mental commands issued to a robot. Especially, in the classification of “the mental commands to a robot,” we successfully realized the imagery classification of five different mental commands. The results point to the potential of NIRS-based brain machine interfaces.

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44

Kirsch, Robert F., A. Bolu Ajiboye, and Jonathan P. Miller. "The Reconnecting the Hand and Arm with Brain (ReHAB) Commentary on “An Integrated Brain-Machine Interface Platform With Thousands of Channels”." Journal of Medical Internet Research 21, no. 10 (2019): e16339. http://dx.doi.org/10.2196/16339.

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Intracortical brain-machine interfaces are a promising technology for allowing people with chronic and severe neurological disorders that resulted in loss of function to potentially regain those functions through neuroprosthetic devices. The penetrating microelectrode arrays used in almost all previous studies of intracortical brain-machine interfaces in people had a limited recording life (potentially due to issues with long-term biocompatibility), as well as a limited number of recording electrodes with limited distribution in the brain. Significant advances are required in this array interface to deal with the issues of long-term biocompatibility and lack of distributed recordings. The Musk and Neuralink manuscript proposes a novel and potentially disruptive approach to advancing the brain-electrode interface technology, with the potential of addressing many of these hurdles. Our commentary addresses the potential advantages of the proposed approach, as well as the remaining challenges to be addressed.

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45

Kim, Gui-Jung, and Jung-Soo Han. "Unsupervised Machine Learning based on Neighborhood Interaction Function for BCI(Brain-Computer Interface)." Journal of Digital Convergence 13, no. 8 (2015): 289–94. http://dx.doi.org/10.14400/jdc.2015.13.8.289.

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46

Valle, Giacomo. "The Connection Between the Nervous System and Machines: Commentary." Journal of Medical Internet Research 21, no. 11 (2019): e16344. http://dx.doi.org/10.2196/16344.

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Decades of technological developments have populated the field of brain-machine interfaces and neuroprosthetics with several replacement strategies, neural modulation treatments, and rehabilitation techniques to improve the quality of life for patients affected by sensory and motor disabilities. This field is now quickly expanding thanks to advances in neural interfaces, machine learning techniques, and robotics. Despite many clinical successes, and multiple innovations in animal models, brain-machine interfaces remain mainly confined to sophisticated laboratory environments indicating a necessary step forward in the used technology. Interestingly, Elon Musk and Neuralink have recently presented a new brain-machine interface platform with thousands of channels, fast implantation, and advanced signal processing. Here, how their work takes part in the context of the restoration of sensory-motor functions through neuroprostheses is commented.

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47

Wang, Xinyuan. "Intracortical Brain-machine Interface for Restoring Sensory Motor Function: Progress and Challenges." International Journal of Biology and Life Sciences 3, no. 2 (2023): 31–38. http://dx.doi.org/10.54097/ijbls.v3i2.10514.

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Limb loss or paralysis due to spinal cord injury has a devastating impact on quality of life. One way to restore the sensory and motor abilities lost by amputees and quadriplegics is to provide them with implants that interface directly with the central nervous system. Such Brain-machine interfaces could enable patients to exert active control over the electrical contractions of prosthetic limbs or paralysed muscles. The parallel interface can transmit sensory information about these motor outcomes back to the patient. Recent developments in algorithms for decoding motor intention from neuronal activity, using biomimetic and adaptation-based approaches and methods for delivering sensory feedback through electrical stimulation of neurons have shown promise for invasive interfaces with sensorimotor cortex, although significant challenges remain.

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48

Chandran, Kalyana Sundaram, and T. Kiruba Angeline. "Identification of Disease Symptoms Using Taste Disorders in Electroencephalogram Signal." Journal of Computational and Theoretical Nanoscience 17, no. 5 (2020): 2051–56. http://dx.doi.org/10.1166/jctn.2020.8848.

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A Brain Computer Interface (BCI) is the one which converts the activity of the brain signals into useful and understandable signal. Brain computer interface is also called as Neural-Control Interface (NCI), Direct Neural Interface (DCI) or Brain Interface Machine (BMI). Electroencephalogram (EEG) based brain computer interfaces (BCI) is the technique used to measure the activity of the brain. Electroencephalography (EEG) is a brain wave monitoring and diagnosis. It is the measurement of electrical activity of the brain from the scalp. Taste sensations are important for our body to digest food. Identification of disease symptoms is based on the inhibition of different types of taste and by testing them to find the normality and abnormality of taste. The information is used in detection of disorder such as Parkinson’s disease etc. It is a source of reimbursement for better clinical diagnosis. Our brain continuously produces electrical signals when it operates. Those signals are measured with the equipment called Neurosky Mindwave Mobile headset. It is used to collect the real time brain signal samples. Neurosky is the equipment used in proposed work. Here the pre-processing technique is executed with median filtering. Feature extraction and classification is done with Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM). It increases the performance accuracy. The SVM classification accuracy achieved by this work is 90%. The sensitivity achieved is higher and the specificity is about 80%. We can able to predict the taste disorders using this methodology.

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49

Virdi, Kulsheet Kaur, and Satish Pawar. "A Comprehensive Review on Brain-Computer Interface Controlled Movements." SMART MOVES JOURNAL IJOSCIENCE 5, no. 6 (2019): 3. http://dx.doi.org/10.24113/ijoscience.v5i6.243.

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A brain-computer interface (BCI), also referred to as a mind-machine interface (MMI) or a brain-machine interface (BMI), provides a non-muscular channel of communication between the human brain and a computer system. With the advancements in low-cost electronics and computer interface equipment, as well as the need to serve people suffering from disabilities of neuromuscular disorders, a new field of research has emerged by understanding different functions of the brain. The electroencephalogram (EEG) is an electrical activity generated by brain structures and recorded from the scalp surface through electrodes. Researchers primarily rely on EEG to characterize the brain activity, because it can be recorded noninvasively by using portable equipment. The EEG or the brain activity can be used in real time to control external devices via a complete BCI system. For these applications there is need of such machine learning application which can be efficiently applied on these EEG signals. The aim of this research is review different research work in the field of brain computer interface related to body parts movements.

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50

Akanksha, Gunda, Kaipa Sahithi, and Lavanya Maddisetti. "Efficient Neural Recording Amplifier for Brain Machine Interface." International Journal of Current Research and Review 13, no. 08 (2021): 54–57. http://dx.doi.org/10.31782/ijcrr.2021.13801.

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