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{{Use dmy dates|date=December 2022}}
{{Short description|Direct communication pathway between an enhanced or wired brain and an external device}}
[[File:Photograph-by-mikeCaiChen.jpg|alt=Participant in a brain-computer interface is Getting connected to a computer|thumb|Participant in a brain-computer interface is getting connected to a computer ]]
[[File:BrainGate.jpg|thumb|Dummy unit illustrating the design of a [[BrainGate]] interface]]
A '''brain–computer interface''' ('''BCI'''), sometimes called a '''brain–machine interface''' ('''BMI'''), is a direct communication link between the [[brain]]'s electrical activity and an external device, most commonly a computer or robotic limb. BCIs are often directed at researching, [[Brain mapping|mapping]], assisting, [[Augmented cognition|augmenting]], or repairing human [[Cognitive skill|cognitive]] or [[Sensory-motor coupling|sensory-motor functions]].<ref name="Krucoff 584">{{cite journal | vauthors = Krucoff MO, Rahimpour S, Slutzky MW, Edgerton VR, Turner DA | title = Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation | journal = Frontiers in Neuroscience | volume = 10 | page = 584 |year=2016 | pmid = 28082858 | pmc = 5186786 | doi = 10.3389/fnins.2016.00584 | doi-access = free }}</ref> They are often conceptualized as a [[human–machine interface]] that skips the intermediary of moving body parts (e.g. hands or feet). BCI implementations range from non-invasive ([[EEG]], [[Magnetoencephalography|MEG]], [[MRI]]) and partially invasive ([[ECoG]] and endovascular) to invasive ([[microelectrode array]]), based on how physically close electrodes are to brain tissue.<ref name=":7">{{Cite journal |last1=Martini |first1=Michael L. |last2=Oermann |first2=Eric Karl |last3=Opie |first3=Nicholas L. |last4=Panov |first4=Fedor |last5=Oxley |first5=Thomas |last6=Yaeger |first6=Kurt |date=February 2020 |title=Sensor Modalities for Brain-Computer Interface Technology: A Comprehensive Literature Review |url=https://journals.lww.com/neurosurgery/abstract/2020/02000/sensor_modalities_for_brain_computer_interface.22.aspx |journal=Neurosurgery |language=en-US |volume=86 |issue=2 |pages=E108–E117 |doi=10.1093/neuros/nyz286 |pmid=31361011 |issn=0148-396X|url-access=subscription }}</ref>
Research on BCIs began in the 1970s by Jacques Vidal at the [[University of California, Los Angeles]] (UCLA) under a grant from the [[National Science Foundation]], followed by a contract from the
Due to the [[cortical plasticity]] of the brain, signals from implanted [[prostheses]] can, after adaptation, be handled by the brain like natural sensor or effector channels.<ref>{{cite journal | vauthors = Levine SP, Huggins JE, BeMent SL, Kushwaha RK, Schuh LA, Rohde MM, Passaro EA, Ross DA, Elisevich KV, Smith BJ | display-authors = 6 | title = A direct brain interface based on event-related potentials | journal = IEEE Transactions on Rehabilitation Engineering | volume = 8 | issue = 2 | pages = 180–185 | date = June 2000 | pmid = 10896180 | doi = 10.1109/86.847809 }}</ref> Following years of animal experimentation, the first [[neuroprosthetic]] devices were implanted in humans in the mid-1990s.
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Although the term had not yet been coined, one of the earliest examples of a working brain-machine interface was the piece ''Music for Solo Performer'' (1965) by American composer [[Alvin Lucier]]. The piece makes use of EEG and [[analog signal processing]] hardware (filters, amplifiers, and a mixing board) to stimulate acoustic percussion instruments. Performing the piece requires producing [[alpha waves]] and thereby "playing" the various instruments via loudspeakers that are placed near or directly on the instruments.<ref>{{cite journal | vauthors = Straebel V, Thoben W | author-link1 = Volker Straebel |title = Alvin Lucier's music for solo performer: experimental music beyond sonification |url= https://depositonce.tu-berlin.de//handle/11303/7085|journal = Organised Sound |volume = 19 |issue =1 |year = 2014 |pages = 17–29|doi = 10.1017/S135577181300037X |s2cid = 62506825 }}</ref>
[[Jacques Vidal]] coined the term "BCI" and produced the first peer-reviewed publications on this topic.<ref name="Vidal1"/><ref name="Vidal2"/> He is widely recognized as the inventor of BCIs.<ref name="Wolpaw, J.R 2012">Wolpaw, J.R. and Wolpaw, E.W. (2012). "Brain-Computer Interfaces: Something New Under the Sun". In: ''Brain-Computer Interfaces: Principles and Practice'', Wolpaw, J.R. and Wolpaw (eds.), E.W. Oxford University Press.</ref><ref>{{cite journal | vauthors = Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM | title = Brain-computer interfaces for communication and control | journal = Clinical Neurophysiology | volume = 113 | issue = 6 | pages = 767–791 | date = June 2002 | pmid = 12048038 | doi = 10.1016/s1388-2457(02)00057-3 | s2cid = 17571592 }}</ref><ref>{{cite journal | vauthors = Allison BZ, Wolpaw EW, Wolpaw JR | title = Brain-computer interface systems: progress and prospects | journal = Expert Review of Medical Devices | volume = 4 | issue = 4 | pages = 463–474 | date = July 2007 | pmid = 17605682 | doi = 10.1586/17434440.4.4.463 | s2cid = 4690450 }}</ref> A review pointed out that Vidal's 1973 paper stated the "BCI challenge"<ref name="Bozinovski1">{{cite journal | vauthors = Bozinovski S, Bozinovska L | year = 2019 | title = Brain-computer interface in Europe: The thirtieth anniversary | journal = Automatika | volume = 60 | issue = 1| pages = 36–47 | doi = 10.1080/00051144.2019.1570644 | doi-access = free }}</ref> of controlling external objects using EEG signals, and especially use of [[Contingent negative variation|Contingent Negative Variation (CNV)]] potential as a challenge for BCI control. Vidal's 1977 experiment was the first application of BCI after his 1973 BCI challenge. It was a noninvasive EEG (actually [[Evoked potential|Visual Evoked Potentials]] (VEP)) control of a cursor-like graphical object on a computer screen. The demonstration was movement in a maze.<ref>{{cite journal |last1=Vidal |first1=Jacques J. |title=Real-time detection of brain events in EEG |journal=Proceedings of the IEEE |date=1977 |volume=65 |issue=5 |pages=633–641 |doi=10.1109/PROC.1977.10542 |s2cid=7928242 |url=http://web.cs.ucla.edu/~vidal/Real_Time_Detection.pdf| url-status=dead |access-date=4 November 2022 |language=en |archive-url=https://web.archive.org/web/20150719005915/http://web.cs.ucla.edu/~vidal/Real_Time_Detection.pdf |archive-date=19 July 2015}}</ref>
1988 was the first demonstration of noninvasive EEG control of a physical object, a robot. The experiment demonstrated EEG control of multiple start-stop-restart cycles of movement, along an arbitrary trajectory defined by a line drawn on a floor. The line-following behavior was the default robot behavior, utilizing autonomous intelligence and an autonomous energy source.<ref>S. Bozinovski, M. Sestakov, L. Bozinovska: Using EEG alpha rhythm to control a mobile robot, In G. Harris, C. Walker (eds.) ''Proc. IEEE Annual Conference of Medical and Biological Society'', p. 1515-1516, New Orleans, 1988</ref><ref>S. Bozinovski: Mobile robot trajectory control: From fixed rails to direct bioelectric control, In O. Kaynak (ed.) ''Proc. IEEE Workshop on Intelligent Motion Control'', p. 63-67, Istanbul, 1990</ref><ref>M. Lebedev: Augmentation of sensorimotor functions with neural prostheses. Opera Medica and Physiologica. Vol. 2 (3): 211-227, 2016</ref><ref>M. Lebedev, M. Nicolelis: Brain-machine interfaces: from basic science to neuroprostheses and neurorehabilitation, Physiological Review 97:737-867, 2017</ref>
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Studies in the 2010s suggested neural stimulation's potential to restore functional connectivity and associated behaviors through modulation of molecular mechanisms.<ref>{{cite journal | vauthors = Miranda RA, Casebeer WD, Hein AM, Judy JW, Krotkov EP, Laabs TL, Manzo JE, Pankratz KG, Pratt GA, Sanchez JC, Weber DJ, Wheeler TL, Ling GS | display-authors = 6 | title = DARPA-funded efforts in the development of novel brain-computer interface technologies | journal = Journal of Neuroscience Methods | volume = 244 | pages = 52–67 | date = April 2015 | pmid = 25107852 | doi = 10.1016/j.jneumeth.2014.07.019 | s2cid = 14678623 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Jacobs M, Premji A, Nelson AJ | title = Plasticity-inducing TMS protocols to investigate somatosensory control of hand function | journal = Neural Plasticity | volume = 2012 | pages = 350574 | date = 16 May 2012 | pmid = 22666612 | pmc = 3362131 | doi = 10.1155/2012/350574 | doi-access = free }}</ref> This opened the door for the concept that BCI technologies may be able to restore function.
Beginning in 2013, [[DARPA]] funded BCI technology through the BRAIN initiative, which supported work out of teams including [[University of Pittsburgh Medical Center]],<ref>{{cite web |last=Fox |first=Maggie |title=Brain Chip Helps Paralyzed Man Feel His Fingers |url=https://www.nbcnews.com/health/health-news/brain-chip-helps-paralyzed-man-feel-his-fingers-n665881 |website=NBC News |date=October 13, 2016 |access-date=23 March 2021}}</ref> Paradromics,<ref>{{cite web |last=Hatmaker |first=Taylor |title=DARPA awards $65 million to develop the perfect, tiny two-way brain-computer
==Neuroprosthetics==
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====Donoghue, Schwartz, and Andersen====
Other laboratories that have developed BCIs and algorithms that decode neuron signals include [[John Donoghue (neuroscientist)|John Donoghue]] at the [[Carney Institute for Brain Science]] at [[Brown University]], Andrew Schwartz at the [[University of Pittsburgh]], and [[Richard A. Andersen (neuroscientist)|Richard Andersen]] at [[Caltech]]. These researchers produced working BCIs using recorded signals from far fewer neurons than Nicolelis (15–30 neurons versus 50–200 neurons).
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Nicolelis and colleagues demonstrated that large neural ensembles can predict arm position. This work allowed BCIs to read arm movement intentions and translate them into actuator movements. Carmena and colleagues<ref name=carmena2003/> programmed a BCI that allowed a monkey to control reaching and grasping movements by a robotic arm. Lebedev and colleagues argued that brain networks reorganize to create a new representation of the robotic appendage in addition to the representation of the animal's own limbs.<ref name="lebedev2005" />
In 2019,
The biggest impediment to BCI technology is the lack of a sensor modality that provides safe, accurate and robust access to brain signals. The use of a better sensor expands the range of communication functions that can be provided using a BCI.
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BCIs led to a deeper understanding of neural networks and the [[central nervous system]]. Research has reported that despite neuroscientists' inclination to believe that neurons have the most effect when working together, single neurons can be conditioned through the use of BCIs to fire in a pattern that allows primates to control motor outputs. BCIs led to development of the single neuron insufficiency principle that states that even with a well-tuned firing rate, single neurons can only carry limited information and therefore the highest level of accuracy is achieved by recording ensemble firings. Other principles discovered with BCIs include the neuronal multitasking principle, the neuronal mass principle, the neural degeneracy principle, and the plasticity principle.<ref>{{cite journal | vauthors = Nicolelis MA, Lebedev MA | title = Principles of neural ensemble physiology underlying the operation of brain-machine interfaces | journal = Nature Reviews. Neuroscience | volume = 10 | issue = 7 | pages = 530–540 | date = July 2009 | pmid = 19543222 | doi = 10.1038/nrn2653 | s2cid = 9290258 }}</ref>
BCIs are proposed to be applied by users without disabilities. Passive BCIs allow for assessing and interpreting changes in the user state during
BCI systems can potentially be used to encode signals from the periphery. These sensory BCI devices enable real-time, behaviorally-relevant decisions based upon closed-loop neural stimulation.<ref>{{cite journal | vauthors = Richardson AG, Ghenbot Y, Liu X, Hao H, Rinehart C, DeLuccia S, Torres Maldonado S, Boyek G, Zhang M, Aflatouni F, Van der Spiegel J, Lucas TH | display-authors = 6 | title = Learning active sensing strategies using a sensory brain-machine interface | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 116 | issue = 35 | pages = 17509–17514 | date = August 2019 | pmid = 31409713 | pmc = 6717311 | doi = 10.1073/pnas.1909953116 | bibcode = 2019PNAS..11617509R | doi-access = free }}</ref>
==Human research==
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====Communication====
In May 2021, a Stanford University team reported a successful proof-of-concept test that enabled a quadraplegic participant to produce English sentences at about 86 characters per minute and 18 words per minute. The participant imagined moving his hand to write letters, and the system performed handwriting recognition on electrical signals detected in the motor cortex, utilizing [[Hidden Markov models]] and [[recurrent neural networks]].<ref>{{cite journal | vauthors = Willett FR, Avansino DT, Hochberg LR, Henderson JM, Shenoy KV | title = High-performance brain-to-text communication via handwriting | journal = Nature | volume = 593 | issue = 7858 | pages = 249–254 | date = May 2021 | pmid = 33981047 | pmc = 8163299 | doi = 10.1038/s41586-021-03506-2 | bibcode = 2021Natur.593..249W }}</ref><ref>{{cite book | vauthors = Willett FR |title=Brain-Computer Interface Research: A State-of-the-Art Summary 10|chapter=A High-Performance Handwriting BCI|date=2021 |pages=105–109| veditors = Guger C, Allison BZ, Gunduz A |series=SpringerBriefs in Electrical and Computer Engineering|place=Cham|publisher=Springer International Publishing|language=en|doi=10.1007/978-3-030-79287-9_11|isbn=978-3-030-79287-9 |s2cid=239736609}}</ref>
Since researchers from [[University of California, San Francisco|UCSF]] initiated a brain-computer interface (BCI) study, numerous reports have been made. In 2021, they reported that a paralyzed and with [[Dysarthria|anarthria]] man was able to communicate fifteen words per minute using an implanted device that examined nerve cells controlling the muscles of the vocal tract.<ref>{{Cite journal |last1=Moses |first1=David A. |last2=Metzger |first2=Sean L. |last3=Liu |first3=Jessie R. |last4=Anumanchipalli |first4=Gopala K. |last5=Makin |first5=Joseph G. |last6=Sun |first6=Pengfei F. |last7=Chartier |first7=Josh |last8=Dougherty |first8=Maximilian E. |last9=Liu |first9=Patricia M. |last10=Abrams |first10=Gary M. |last11=Tu-Chan |first11=Adelyn |last12=Ganguly |first12=Karunesh |last13=Chang |first13=Edward F. |date=2021-07-14 |title=Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria |journal=New England Journal of Medicine |volume=385 |issue=3 |pages=217–227 |doi=10.1056/NEJMoa2027540 |issn=0028-4793 |pmc=8972947 |pmid=34260835}}</ref><ref>{{Cite journal |last1=Maiseli |first1=Baraka |last2=Abdalla |first2=Abdi T. |last3=Massawe |first3=Libe V. |last4=Mbise |first4=Mercy |last5=Mkocha |first5=Khadija |last6=Nassor |first6=Nassor Ally |last7=Ismail |first7=Moses |last8=Michael |first8=James |last9=Kimambo |first9=Samwel |date=2023-08-04 |title=Brain–computer interface: trend, challenges, and threats |journal=Brain Informatics |volume=10 |issue=1 |pages=20 |doi=10.1186/s40708-023-00199-3 |doi-access=free |issn=2198-4026 |pmc=10403483 |pmid=37540385}}</ref> In addition in 2022 it was announced that their implant could also be used to spell out words and entire sentences without speaking aloud. The first bilingual speech neuroprosthesis was reported to have been developed by the same team at the University of San Francisco, in 2024.<ref>{{Cite journal |last=Matsiko |first=Amos |date=2024-08-21 |title=Bilingual speech neuroprosthesis |url=https://www.science.org/doi/10.1126/scirobotics.ads4122 |journal=Science Robotics |volume=9 |issue=93 |pages=eads4122 |doi=10.1126/scirobotics.ads4122|url-access=subscription }}</ref><ref>{{Cite journal |last1=Silva |first1=Alexander B. |last2=Liu |first2=Jessie R. |last3=Metzger |first3=Sean L. |last4=Bhaya-Grossman |first4=Ilina |last5=Dougherty |first5=Maximilian E. |last6=Seaton |first6=Margaret P. |last7=Littlejohn |first7=Kaylo T. |last8=Tu-Chan |first8=Adelyn |last9=Ganguly |first9=Karunesh |last10=Moses |first10=David A. |last11=Chang |first11=Edward F. |date=August 2024 |title=A bilingual speech neuroprosthesis driven by cortical articulatory representations shared between languages |journal=Nature Biomedical Engineering |language=en |volume=8 |issue=8 |pages=977–991 |doi=10.1038/s41551-024-01207-5 |pmid=38769157 |pmc=11554235 |issn=2157-846X}}</ref><ref>{{Cite web |date=2024-05-28 |title=Bilingual AI brain implant helps stroke survivor communicate in Spanish and English |url=https://www.nbcnews.com/news/latino/bilingual-ai-brain-implant-spanish-english-stroke-patient-rcna154295 |access-date=2025-06-23 |website=NBC News |language=en}}</ref>
In a review article, authors wondered whether human information transfer rates can surpass that of language with BCIs. Language research has reported that information transfer rates are relatively constant across many languages. This may reflect the brain's information processing limit. Alternatively, this limit may be intrinsic to language itself, as a modality for information transfer.<ref name=":5">{{cite journal | vauthors = Pandarinath C, Bensmaia SJ | title = The science and engineering behind sensitized brain-controlled bionic hands | journal = Physiological Reviews | date = September 2021 | volume = 102 | issue = 2 | pages = 551–604 | pmid = 34541898 | doi = 10.1152/physrev.00034.2020 | pmc = 8742729 | s2cid = 237574228 }}</ref>
In 2023 two studies used BCIs with recurrent neural network to decode speech at a record rate of 62 words per minute and 78 words per minute.<ref>{{Cite journal |last1=Willett |first1=Francis R. |last2=Kunz |first2=Erin M. |last3=Fan |first3=Chaofei |last4=Avansino |first4=Donald T. |last5=Wilson |first5=Guy H. |last6=Choi |first6=Eun Young |last7=Kamdar |first7=Foram |last8=Glasser |first8=Matthew F. |last9=Hochberg |first9=Leigh R. |last10=Druckmann |first10=Shaul |last11=Shenoy |first11=Krishna V. |last12=Henderson |first12=Jaimie M. |date=2023-08-23 |title=A high-performance speech neuroprosthesis |journal=Nature |volume=620 |issue=7976 |language=en |pages=1031–1036 |doi=10.1038/s41586-023-06377-x |pmid=37612500 |pmc=10468393 |bibcode=2023Natur.620.1031W |issn=1476-4687}}</ref><ref>{{Cite journal |last1=Metzger |first1=Sean L. |last2=Littlejohn |first2=Kaylo T. |last3=Silva |first3=Alexander B. |last4=Moses |first4=David A. |last5=Seaton |first5=Margaret P. |last6=Wang |first6=Ran |last7=Dougherty |first7=Maximilian E. |last8=Liu |first8=Jessie R. |last9=Wu |first9=Peter |last10=Berger |first10=Michael A. |last11=Zhuravleva |first11=Inga |last12=Tu-Chan |first12=Adelyn |last13=Ganguly |first13=Karunesh |last14=Anumanchipalli |first14=Gopala K. |last15=Chang |first15=Edward F. |date=2023-08-23 |title=A high-performance neuroprosthesis for speech decoding and avatar control |journal=Nature |volume=620 |issue=7976 |language=en |pages=1037–1046 |doi=10.1038/s41586-023-06443-4 |pmid=37612505 |pmc=10826467 |bibcode=2023Natur.620.1037M |s2cid=261098775 |issn=1476-4687}}</ref><ref>{{Cite journal |last=Naddaf |first=Miryam |date=2023-08-23 |title=Brain-reading devices allow paralysed people to talk using their thoughts |url=https://www.nature.com/articles/d41586-023-02682-7 |journal=Nature |volume=620 |issue=7976 |pages=930–931 |language=en |doi=10.1038/d41586-023-02682-7|pmid=37612493 |bibcode=2023Natur.620..930N |s2cid=261099321 |url-access=subscription }}</ref>
==== Technical challenges ====
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==== Electrocorticography ====
[[Electrocorticography]] (ECoG) measures brain electrical activity from beneath the skull in a way similar to non-invasive electroencephalography, using electrodes embedded in a thin plastic pad placed above the cortex, beneath the [[dura mater]].<ref>{{cite book | last1=Serruya | first1=Mijail | last2=Donoghue | first2=John | chapter = Chapter III: Design Principles of a Neuromotor Prosthetic Device | title = Neuroprosthetics: Theory and Practice | veditors = Horch KW, Dhillon GS | publisher = Imperial College Press | year=2004 |pages=1158–1196 | doi=10.1142/9789812561763_0040 | archive-url=https://web.archive.org/web/20050404155139/http://donoghue.neuro.brown.edu/pubs/2003-SerruyaDonoghue-Chap3-preprint.pdf | archive-date=4 April 2005 |chapter-url=
Signals can be either subdural or epidural, but are not taken from within the brain [[parenchyma]]. Patients are required to have invasive monitoring for localization and resection of an epileptogenic focus.{{Citation needed|date=May 2024}}
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ECoG offers higher spatial resolution, better signal-to-noise ratio, wider frequency range, and less training requirements than scalp-recorded EEG, and at the same time has lower technical difficulty, lower clinical risk, and may have superior long-term stability than intracortical single-neuron recording.<ref>{{cite journal | vauthors = Nicolas-Alonso LF, Gomez-Gil J | title = Brain computer interfaces, a review | journal = Sensors | volume = 12 | issue = 2 | pages = 1211–1279 | date = 2012-01-31 | pmid = 22438708 | pmc = 3304110 | doi = 10.3390/s120201211 | bibcode = 2012Senso..12.1211N | doi-access = free }}</ref> This feature profile and evidence of the high level of control with minimal training requirements shows potential for real world application for people with motor disabilities.<ref name=Mondeofuse>{{cite news | vauthors = Yanagisawa T |title=Electrocorticographic Control of Prosthetic Arm in Paralyzed Patients |doi=10.1002/ana.22613 |quote= ECoG- Based BCI has advantage in signal and durability that are absolutely necessary for clinical application|work=[[American Neurological Association]] |year= 2011 |volume=71 |issue=3 |pages=353–361 }}</ref><ref name=TelepathicCommVowel>{{cite news | vauthors = Pei X |title=Decoding Vowels and Consonants in Spoken and Imagined Words Using Electrocorticographic Signals in Humans |pmid=21750369 |quote= Justin Williams, a biomedical engineer at the university, has already transformed the ECoG implant into a micro device that can be installed with a minimum of fuss. It has been tested in animals for a long period of time – the micro ECoG stays in place and doesn't seem to negatively affect the immune system.|work=J Neural Eng 046028th ser. 8.4 |year=2011 }}</ref>
[[Edward Chang (neurosurgeon)|Edward Chang]] and Joseph Makin from [[UCSF Medical Center|UCSF]] reported that ECoG signals could be used to decode speech from epilepsy patients implanted with high-density ECoG arrays over the peri-Sylvian cortices.<ref>{{cite book | vauthors = Makin JG, Moses DA, Chang EF | title = Brain-Computer Interface Research | veditors = Guger C, Allison BZ, Gunduz A | chapter = Speech Decoding as Machine Translation|date=2021 |pages=23–33 |series=SpringerBriefs in Electrical and Computer Engineering|place=Cham|publisher=Springer International Publishing |language=en |doi=10.1007/978-3-030-79287-9_3 |isbn=978-3-030-79287-9 | s2cid = 239756345 }}</ref><ref>{{cite journal | vauthors = Makin JG, Moses DA, Chang EF | title = Machine translation of cortical activity to text with an encoder-decoder framework | journal = Nature Neuroscience | volume = 23 | issue = 4 | pages = 575–582 | date = April 2020 | pmid = 32231340 | doi = 10.1038/s41593-020-0608-8 | pmc = 10560395 | s2cid = 214704481 }}</ref> They reported word error rates of 3% (a marked improvement from prior efforts) utilizing an encoder-decoder [[neural network]], which translated ECoG data into one of fifty sentences composed of 250 unique words.{{cn|date=April 2025}}
====Functional near-infrared spectroscopy====
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A 2019 study reported that the application of evolutionary algorithms could improve EEG mental state classification with a non-invasive [[Muse (headband)|Muse]] device, enabling classification of data acquired by a consumer-grade sensing device.<ref>{{cite journal |vauthors=Bird JJ, Faria DR, Manso LJ, Ekárt A, Buckingham CD |date=2019-03-13 |title=A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction |journal=Complexity |publisher=Hindawi Limited |volume=2019 |pages=1–14 |arxiv=1908.04784 |doi=10.1155/2019/4316548 |issn=1076-2787 |doi-access=free}}</ref>
In a 2021 systematic review of [[randomized controlled trials]] using BCI for post-stroke upper-limb rehabilitation, EEG-based BCI was reported to have efficacy in improving upper-limb motor function compared to control therapies. More specifically, BCI studies that utilized band power features, [[motor imagery]], and [[functional electrical stimulation]] were reported to be more effective than alternatives.<ref>{{cite journal |vauthors=Mansour S, Ang KK, Nair KP, Phua KS, Arvaneh M |date=January 2022 |title=Efficacy of Brain-Computer Interface and the Impact of Its Design Characteristics on Poststroke Upper-limb Rehabilitation: A Systematic Review and Meta-analysis of Randomized Controlled Trials |journal=Clinical EEG and Neuroscience |volume=53 |issue=1 |pages=79–90 |doi=10.1177/15500594211009065 |pmc=8619716 |pmid=33913351 |s2cid=233446181}}</ref> Another 2021 systematic review focused on post-stroke robot-assisted EEG-based BCI for hand rehabilitation. Improvement in motor assessment scores was observed in three of eleven studies.<ref>{{cite journal |display-authors=6 |vauthors=Baniqued PD, Stanyer EC, Awais M, Alazmani A, Jackson AE, Mon-Williams MA, Mushtaq F, Holt RJ |date=January 2021 |title=Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review |journal=Journal of Neuroengineering and Rehabilitation |volume=18 |issue=1 |
====Dry active electrode arrays====
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In 2008 research developed in the Advanced Telecommunications Research (ATR) [[Computational Neuroscience]] Laboratories in [[Kyoto]], Japan, allowed researchers to reconstruct images from brain signals at a [[Display resolution|resolution]] of 10x10 [[pixels]].<ref>{{cite journal | vauthors = Miyawaki Y, Uchida H, Yamashita O, Sato MA, Morito Y, Tanabe HC, Sadato N, Kamitani Y | display-authors = 6 | title = Visual image reconstruction from human brain activity using a combination of multiscale local image decoders | journal = Neuron | volume = 60 | issue = 5 | pages = 915–929 | date = December 2008 | pmid = 19081384 | doi = 10.1016/j.neuron.2008.11.004 | s2cid = 17327816 | doi-access = free }}</ref>
A 2011 study reported second-by-second reconstruction of videos watched by the study's subjects, from fMRI data.<ref>{{cite journal |vauthors=Nishimoto S, Vu AT, Naselaris T, Benjamini Y, Yu B, Gallant JL |date=October 2011 |title=Reconstructing visual experiences from brain activity evoked by natural movies |journal=Current Biology |volume=21 |issue=19 |pages=1641–1646 |doi=10.1016/j.cub.2011.08.031 |pmc=3326357 |pmid=21945275|bibcode=2011CBio...21.1641N }}</ref> This was achieved by creating a statistical model relating videos to brain activity. This model was then used to look up 100 one-second video segments, in a database of 18 million seconds of random [[YouTube]] videos, matching visual patterns to brain activity recorded when subjects watched a video. These 100 one-second video extracts were then combined into a mash-up image that resembled the video.<ref>{{cite magazine | url = http://blogs.scientificamerican.com/observations/2011/09/22/breakthrough-could-enable-others-to-watch-your-dreams-and-memories-video/ | title= Breakthrough Could Enable Others to Watch Your Dreams and Memories | last = Yam |first=Philip | date = 22 September 2011 | magazine = Scientific American | access-date = 25 September 2011}}</ref><ref>{{cite web | url = https://sites.google.com/site/gallantlabucb/publications/nishimoto-et-al-2011 | title = Reconstructing visual experiences from brain activity evoked by natural movies (Project page) | publisher = The Gallant Lab at [[UC Berkeley]] | access-date = 25 September 2011 |url-status=dead |archiveurl=https://web.archive.org/web/20110925024037/https://sites.google.com/site/gallantlabucb/publications/nishimoto-et-al-2011 |archivedate=2011-09-25}}</ref><ref>{{cite web | url= http://newscenter.berkeley.edu/2011/09/22/brain-movies/| title= Scientists use brain imaging to reveal the movies in our mind |last=Anwar |first=Yasmin | date= 22 September 2011 | publisher = [[UC Berkeley]] News Center| access-date = 25 September 2011}}</ref>
====BCI control strategies in neurogaming====
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==== Non-brain-based human–computer interface (physiological computing) ====
Human-computer interaction can exploit other recording modalities, such as [[electrooculography]] and eye-tracking. These modalities do not record brain activity and therefore do not qualify as BCIs.<ref>{{Cite journal |last=Fairclough |first=Stephen H. |date=January 2009 |title=Fundamentals of physiological computing |url=https://academic.oup.com/iwc/article-lookup/doi/10.1016/j.intcom.2008.10.011 |journal=Interacting with Computers |language=en |volume=21 |issue=1–2 |pages=133–145 |doi=10.1016/j.intcom.2008.10.011|s2cid=16314534 |url-access=subscription }}</ref>
=====Electrooculography (EOG)=====
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===Brain-to-brain communication===
In the 1960s a researcher after training used EEG to create [[Morse code]] using alpha waves.<ref name="Telepathy">{{cite news |last=Bland |first=Eric |date=13 October 2008 |title=Army Developing 'synthetic telepathy' |url=https://www.nbcnews.com/id/wbna27162401 |access-date=13 October 2008 |newspaper=Discovery News}}</ref> On 27 February 2013 [[Miguel Nicolelis]]'s group at [[Duke University]] and IINN-ELS connected the brains of two rats, allowing them to share information, in [[Miguel Nicolelis#Brain to brain|the first-ever direct brain-to-brain interface]].<ref name="srep01319">{{cite journal |vauthors=Pais-Vieira M, Lebedev M, Kunicki C, Wang J, Nicolelis MA |date=28 February 2013 |title=A brain-to-brain interface for real-time sharing of sensorimotor information |journal=Scientific Reports |volume=3 |
Gerwin Schalk reported that ECoG signals can discriminate vowels and consonants embedded in spoken and imagined words, shedding light on the mechanisms associated with their production and could provide a basis for brain-based communication using imagined speech.<ref name="TelepathicCommVowel" /><ref name="TelepathicComm">{{cite news |last=Kennedy |first=Pagan |title=The Cyborg in Us All |url=https://www.nytimes.com/2011/09/18/magazine/the-cyborg-in-us-all.html |work=[[The New York Times]] |date=18 September 2011 |access-date=28 January 2012 }}</ref>
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In 2004 Thomas DeMarse at the [[University of Florida]] used a culture of 25,000 neurons taken from a rat's brain to fly a [[F-22]] fighter jet [[aircraft simulator]]. After collection, the cortical neurons were cultured in a [[petri dish]] and reconnected themselves to form a living neural network. The cells were arranged over a grid of 60 electrodes and used to control the [[Aircraft principal axes|pitch]] and [[Aircraft principal axes|yaw]] functions of the simulator. The study's focus was on understanding how the human brain performs and learns computational tasks at a cellular level.<ref>{{Cite news |url=http://www.cnn.com/2004/TECH/11/02/brain.dish/ |title='Brain' in a dish flies flight simulator |work=CNN |date=4 November 2004}}</ref>
== Ethical considerations==
Concerns center on the safety and long-term effects on users. These include obtaining [[informed consent]] from individuals with communication difficulties, the impact on patients' and families' quality of life, health-related side effects, misuse of therapeutic applications, safety risks, and the non-reversible nature of some BCI-induced changes. Additionally, questions arise about access to maintenance, repair, and spare parts, particularly in the event of a company's bankruptcy.<ref>{{Cite web |title=Paralyzed Again |url=https://www.technologyreview.com/2015/04/09/168424/paralyzed-again/ |access-date=2023-12-08 |website=MIT Technology Review |language=en}}</ref>
The legal and social aspects of BCIs complicate mainstream adoption. Concerns include issues of accountability and responsibility, such as claims that BCI influence overrides free will and control over actions, inaccurate translation of cognitive intentions, personality changes resulting from deep-brain stimulation, and the blurring of the line between human and machine.<ref>{{Cite web |title=Gale - Product Login |url=https://galeapps.gale.com/apps/auth?userGroupName=nysl_ca_arg&sid=googleScholar&da=true&origURL=https%3A%2F%2Fgo.gale.com%2Fps%2Fi.do%3Fid%3DGALE%257CA594456959%26sid%3DgoogleScholar%26v%3D2.1%26it%3Dr%26linkaccess%3Dabs%26issn%3D00280836%26p%3DAONE%26sw%3Dw%26userGroupName%3Dnysl_ca_arg%26aty%3Dip&prodId=AONE |access-date=2023-12-08 |website=galeapps.gale.com}}</ref> Other concerns involve the use of BCIs in advanced interrogation techniques, unauthorized access ("brain hacking"),<ref>{{Cite journal |last1=Ienca |first1=Marcello |last2=Haselager |first2=Pim |date=June 2016 |title=Hacking the brain: brain-computer interfacing technology and the ethics of neurosecurity |url=https://dx.doi.org/10.1007/s10676-016-9398-9 |journal=Ethics & Information Technology |volume=18 |issue=2 |pages=117–129 |doi=10.1007/s10676-016-9398-9 |s2cid=5132634|hdl=2066/157644 |hdl-access=free |url-access=subscription }}</ref> social stratification through selective enhancement, privacy issues related to mind-reading, tracking and "tagging" systems, and the potential for mind, movement, and emotion control.<ref>{{Cite journal |last1=Steinert |first1=Steffen |last2=Friedrich |first2=Orsolya |date=2020-02-01 |title=Wired Emotions: Ethical Issues of Affective Brain–Computer Interfaces |url=https://doi.org/10.1007/s11948-019-00087-2 |journal=Science and Engineering Ethics |language=en |volume=26 |issue=1 |pages=351–367 |doi=10.1007/s11948-019-00087-2 |issn=1471-5546 |pmc=6978299 |pmid=30868377}}</ref>
In their current form, most BCIs are more akin to corrective therapies that engage few of such ethical issues. Bioethics is well-equipped to address the challenges posed by BCI technologies, with Clausen suggesting in 2009 that "BCIs pose ethical challenges, but these are conceptually similar to those that bioethicists have addressed for other realms of therapy."<ref>{{Cite journal |last=Clausen |first=Jens |date=2009-02-01 |title=Man, machine and in between |url=https://ui.adsabs.harvard.edu/abs/2009Natur.457.1080C |journal=Nature |volume=457 |issue=7233 |pages=1080–1081 |bibcode=2009Natur.457.1080C |doi=10.1038/4571080a |issn=0028-0836 |pmid=19242454 |s2cid=205043226}}</ref> Haselager and colleagues highlighted the importance of managing expectations and value.<ref>{{Cite journal |last1=Haselager |first1=Pim |last2=Vlek |first2=Rutger |last3=Hill |first3=Jeremy |last4=Nijboer |first4=Femke |date=2009-11-01 |title=A note on ethical aspects of BCI |url=https://www.sciencedirect.com/science/article/pii/S0893608009001531 |journal=Neural Networks |series=Brain-Machine Interface |volume=22 |issue=9 |pages=1352–1357 |doi=10.1016/j.neunet.2009.06.046 |issn=0893-6080 |pmid=19616405 |hdl-access=free |hdl=2066/77533}}</ref>
The evolution of BCIs mirrors that of pharmaceutical science, which began as a means to address impairments and now enhances focus and reduces the need for sleep. As BCIs progress from therapies to enhancements, the BCI community is working to create consensus on ethical guidelines for research, development, and dissemination.<ref>{{Cite journal |last1=Attiah |first1=Mark A. |last2=Farah |first2=Martha J. |date=2014-05-15 |title=Minds, motherboards, and money: futurism and realism in the neuroethics of BCI technologies |journal=Frontiers in Systems Neuroscience |volume=8 |pages=86 |doi=10.3389/fnsys.2014.00086 |issn=1662-5137 |pmc=4030132 |pmid=24860445 |doi-access=free}}</ref><ref>{{Cite journal |last1=Nijboer |first1=Femke |last2=Clausen |first2=Jens |last3=Allison |first3=Brendan Z. |last4=Haselager |first4=Pim |date=2013 |title=The Asilomar Survey: Stakeholders' Opinions on Ethical Issues Related to Brain-Computer Interfacing |journal=Neuroethics |volume=6 |issue=3 |pages=541–578 |doi=10.1007/s12152-011-9132-6 |issn=1874-5490 |pmc=3825606 |pmid=24273623}}</ref>
==Low-cost systems==
Various companies are developing inexpensive BCIs for research and entertainment. Toys such as the NeuroSky and Mattel MindFlex have seen some commercial success.
* In 2006, [[Sony]] patented a neural interface system allowing radio waves to affect signals in the neural cortex.<ref name="Sony patent neural interface">{{cite news|url=http://www.wikipatents.com/US-Patent-6729337/method-and-system-for-generating-sensory-data-onto-the-human-neural |title=Sony patent neural interface |url-status=dead |archive-url=https://web.archive.org/web/20120407071853/http://www.wikipatents.com/US-Patent-6729337/method-and-system-for-generating-sensory-data-onto-the-human-neural |archive-date=7 April 2012 |df=dmy }}</ref>
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===Motor recovery===
People may lose some of their ability to move due to many causes, such as stroke or injury. Research in recent years has demonstrated the utility of EEG-based BCI systems in aiding motor recovery and neurorehabilitation in patients who have had a stroke.<ref>{{cite journal | vauthors = Silvoni S, Ramos-Murguialday A, Cavinato M, Volpato C, Cisotto G, Turolla A, Piccione F, Birbaumer N | display-authors = 6 | title = Brain-computer interface in stroke: a review of progress | journal = Clinical EEG and Neuroscience | volume = 42 | issue = 4 | pages = 245–252 | date = October 2011 | pmid = 22208122 | doi = 10.1177/155005941104200410 | s2cid = 37902399 }}</ref><ref>{{cite journal | vauthors = Leamy DJ, Kocijan J, Domijan K, Duffin J, Roche RA, Commins S, Collins R, Ward TE | display-authors = 6 | title = An exploration of EEG features during recovery following stroke - implications for BCI-mediated neurorehabilitation therapy | journal = Journal of Neuroengineering and Rehabilitation | volume = 11 | pages = 9 | date = January 2014 | pmid = 24468185 | pmc = 3996183 | doi = 10.1186/1743-0003-11-9 | first8 = Tomas E | doi-access = free }}</ref><ref>{{cite book | vauthors = Tung SW, Guan C, Ang KK, Phua KS, Wang C, Zhao L, Teo WP, Chew E | title = 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) | chapter = Motor imagery BCI for upper limb stroke rehabilitation: An evaluation of the EEG recordings using coherence analysis | display-authors = 6 | volume = 2013 | pages = 261–264 | date = July 2013 | pmid = 24109674 | doi = 10.1109/EMBC.2013.6609487 | isbn = 978-1-4577-0216-7 | s2cid = 5071115 }}</ref><ref>{{cite journal | vauthors = Bai Z, Fong KN, Zhang JJ, Chan J, Ting KH | title = Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis | journal = Journal of Neuroengineering and Rehabilitation | volume = 17 | issue = 1 |
So far, BCIs for motor recovery have relied on the EEG to measure the patient's motor imagery. However, studies have also used fMRI to study different changes in the brain as persons undergo BCI-based stroke rehab training.<ref>{{cite journal | vauthors = Várkuti B, Guan C, Pan Y, Phua KS, Ang KK, Kuah CW, Chua K, Ang BT, Birbaumer N, Sitaram R | display-authors = 6 | title = Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke | journal = Neurorehabilitation and Neural Repair | volume = 27 | issue = 1 | pages = 53–62 | date = January 2013 | pmid = 22645108 | doi = 10.1177/1545968312445910 | s2cid = 7120989 }}</ref><ref>{{cite journal | vauthors = Young BM, Nigogosyan Z, Remsik A, Walton LM, Song J, Nair VA, Grogan SW, Tyler ME, Edwards DF, Caldera K, Sattin JA, Williams JC, Prabhakaran V | display-authors = 6 | title = Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device | journal = Frontiers in Neuroengineering | volume = 7 | pages = 25 | date = 2014 | pmid = 25071547 | pmc = 4086321 | doi = 10.3389/fneng.2014.00025 | doi-access = free }}</ref><ref name=":6">{{cite journal | vauthors = Yuan K, Chen C, Wang X, Chu WC, Tong RK | title = BCI Training Effects on Chronic Stroke Correlate with Functional Reorganization in Motor-Related Regions: A Concurrent EEG and fMRI Study | journal = Brain Sciences | volume = 11 | issue = 1 | pages = 56 | date = January 2021 | pmid = 33418846 | doi = 10.3390/brainsci11010056 | pmc = 7824842 | doi-access = free }}</ref> Imaging studies combined with EEG-based BCI systems hold promise for investigating neuroplasticity during motor recovery post-stroke.<ref name=":6" /> Future systems might include the fMRI and other measures for real-time control, such as functional near-infrared, probably in tandem with EEGs. Non-invasive brain stimulation has also been explored in combination with BCIs for motor recovery.<ref>{{cite journal | vauthors = Mrachacz-Kersting N, Voigt M, Stevenson AJ, Aliakbaryhosseinabadi S, Jiang N, Dremstrup K, Farina D | title = The effect of type of afferent feedback timed with motor imagery on the induction of cortical plasticity | journal = Brain Research | volume = 1674 | pages = 91–100 | date = November 2017 | pmid = 28859916 | doi = 10.1016/j.brainres.2017.08.025 | hdl-access = free | s2cid = 5866337 | hdl = 10012/12325 }}</ref> In 2016, scientists out of the [[University of Melbourne]] published preclinical proof-of-concept data related to a potential brain-computer interface technology platform being developed for patients with paralysis to facilitate control of external devices such as robotic limbs, computers and exoskeletons by translating brain activity.<ref>{{cite web | vauthors = Opie N |title=Research Overview |url=https://medicine.unimelb.edu.au/research-groups/medicine-and-radiology-research/royal-melbourne-hospital/the-vascular-bionics-laboratory |website=University of Melbourne Medicine |date=2 April 2019 |publisher=University of Melbourne |access-date=5 December 2019}}</ref><ref>{{cite journal | vauthors = Oxley TJ, Opie NL, John SE, Rind GS, Ronayne SM, Wheeler TL, Judy JW, McDonald AJ, Dornom A, Lovell TJ, Steward C, Garrett DJ, Moffat BA, Lui EH, Yassi N, Campbell BC, Wong YT, Fox KE, Nurse ES, Bennett IE, Bauquier SH, Liyanage KA, van der Nagel NR, Perucca P, Ahnood A, Gill KP, Yan B, Churilov L, French CR, Desmond PM, Horne MK, Kiers L, Prawer S, Davis SM, Burkitt AN, Mitchell PJ, Grayden DB, May CN, O'Brien TJ | display-authors = 6 | title = Minimally invasive endovascular stent-electrode array for high-fidelity, chronic recordings of cortical neural activity | journal = Nature Biotechnology | volume = 34 | issue = 3 | pages = 320–327 | date = March 2016 | pmid = 26854476 | doi = 10.1038/nbt.3428 | s2cid = 205282364 }}</ref><ref>{{cite web |title=Synchron begins trialling Stentrode neural interface technology |date=22 September 2019 |url=https://www.medicaldevice-network.com/news/synchron-stentrode-study/ |publisher=Verdict Medical Devices |access-date=5 December 2019}}</ref>
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* [[Wetware computer]] (Uses similar technology for IO)
* [[Whole brain emulation]]
* [[Wirehead (science fiction)]]{{div col end}}
== Notes ==
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{{Footer Neuropsychology}}
{{emerging technologies|topics=yes|neuro=yes|infocom=yes}}
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[[Category:Brain–computer interface| ]]
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[[Category:Neural engineering|*]]
[[Category:User interface techniques]]
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