Editing Brain–computer interface (section)

===Invasive BCIs===
Invasive BCI requires surgery to implant electrodes under the scalp for accessing brain signals. The main advantage is to increase accuracy. Downsides include side effects from the surgery, including scar tissue that can obstruct brain signals, or the body potentially rejecting the implanted electrodes.<ref>{{cite journal |vauthors=Abdulkader SN, Atia A, Mostafa MS |date=July 2015 |title=Brain computer interfacing: Applications and challenges |journal=Egyptian Informatics Journal |volume=16 |issue=2 |pages=213–230 |doi=10.1016/j.eij.2015.06.002 |issn=1110-8665 |doi-access=free}}</ref>

==== Vision ====
Invasive BCI research has targeted repairing damaged sight and providing new functionality for people with paralysis. Invasive BCIs are implanted directly into the [[grey matter]] of the brain during neurosurgery. Because they lie in the grey matter, invasive devices produce the highest quality signals of BCI devices but are prone to [[scar|scar-tissue]] build-up, causing the signal to weaken, or disappear, as the body reacts to the foreign object.<ref>{{cite journal | vauthors = Polikov VS, Tresco PA, Reichert WM | title = Response of brain tissue to chronically implanted neural electrodes | journal = Journal of Neuroscience Methods | volume = 148 | issue = 1 | pages = 1–18 | date = October 2005 | pmid = 16198003 | doi = 10.1016/j.jneumeth.2005.08.015 | s2cid = 11248506 }}</ref>

In [[vision science]], direct [[brain implant]]s have been used to treat non-[[congenital]] (acquired) blindness. One of the first scientists to produce a working brain interface to restore sight was private researcher [[William Dobelle]]. Dobelle's first prototype was implanted into "Jerry", a man blinded in adulthood, in 1978. A single-array BCI containing 68 electrodes was implanted onto Jerry's [[visual cortex]] and succeeded in producing [[phosphenes]], the sensation of seeing light. The system included cameras mounted on glasses to send signals to the implant. Initially, the implant allowed Jerry to see shades of grey in a limited field of vision at a low frame-rate. This also required him to be hooked up to a [[mainframe computer]], but shrinking electronics and faster computers made his artificial eye more portable and now enable him to perform simple tasks unassisted.<ref>[https://www.wired.com/wired/archive/10.09/vision.html "Vision quest"]. ''[[Wired (magazine)|Wired]]''. (September 2002).</ref>

In 2002, Jens Naumann, also blinded in adulthood, became the first in a series of 16 paying patients to receive Dobelle's second generation implant, one of the earliest commercial uses of BCIs. The second generation device used a more sophisticated implant enabling better mapping of phosphenes into coherent vision. Phosphenes are spread out across the visual field in what researchers call "the starry-night effect". Immediately after his implant, Jens was able to use his imperfectly restored vision to [[driving|drive]] an automobile slowly around the parking area of the research institute.<ref>{{Cite news| vauthors = Kotler S |title=Vision Quest|language=en-US|magazine=Wired|url=https://www.wired.com/2002/09/vision/|access-date=2021-11-10|issn=1059-1028}}</ref> Dobelle died in 2004 before his processes and developments were documented, leaving no one to continue his work.<ref>{{cite web |date=1 November 2004 |title=Dr. William Dobelle, Artificial Vision Pioneer, Dies at 62 |url=https://www.nytimes.com/2004/11/01/obituaries/01dobelle.html |work=The New York Times |vauthors=Tuller D}}</ref> Subsequently, Naumann and the other patients in the program began having problems with their vision, and eventually lost their "sight" again.<ref name="Naumann,_2012">{{cite book | vauthors = Naumann J |title=Search for Paradise: A Patient's Account of the Artificial Vision Experiment. |date=2012 |publisher=Xlibris |isbn=978-1-4797-0920-5}}</ref><ref>{{cite web|author=nurun.com |url=http://www.thewhig.com/2012/11/28/mans-high-tech-paradise-lost |title=Mr. Jen Naumann's high-tech paradise lost |work=thewhig |publisher=Thewhig.com |date= 28 November 2012|access-date=19 December 2016}}</ref>

====Movement====
BCIs focusing on motor neuroprosthetics aim to restore movement in individuals with paralysis or provide devices to assist them, such as interfaces with computers or robot arms.

Kennedy and Bakay were first to install a human brain implant that produced signals of high enough quality to simulate movement. Their patient, Johnny Ray (1944–2002), developed '[[locked-in syndrome]]' after a brain-stem [[stroke]] in 1997. Ray's implant was installed in 1998 and he lived long enough to start working with the implant, eventually learning to control a computer cursor; he died in 2002 of a [[brain aneurysm]].<ref>{{cite journal | vauthors = Kennedy PR, Bakay RA | title = Restoration of neural output from a paralyzed patient by a direct brain connection | journal = NeuroReport | volume = 9 | issue = 8 | pages = 1707–1711 | date = June 1998 | pmid = 9665587 | doi = 10.1097/00001756-199806010-00007 | s2cid = 5681602 }}</ref>

[[Tetraplegic]] [[Matt Nagle]] became the first person to control an artificial hand using a BCI in 2005 as part of the first nine-month human trial of [[Cyberkinetics]]'s [[BrainGate]] chip-implant. Implanted in Nagle's right [[precentral gyrus]] (area of the motor cortex for arm movement), the 96-electrode implant allowed Nagle to control a robotic arm by thinking about moving his hand as well as a computer cursor, lights and TV.<ref>{{cite journal | vauthors = Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP | display-authors = 6 | title = Neuronal ensemble control of prosthetic devices by a human with tetraplegia | journal = Nature | volume = 442 | issue = 7099 | pages = 164–171 | date = July 2006 | pmid = 16838014 | doi = 10.1038/nature04970 | s2cid = 4347367 | bibcode = 2006Natur.442..164H | others = Gerhard M. Friehs, Jon A. Mukand, Maryam Saleh, Abraham H. Caplan, Almut Branner, David Chen, Richard D. Penn and John P. Donoghue }}</ref> One year later, Jonathan Wolpaw received the [[Altran Foundation for Innovation]] prize for developing a Brain Computer Interface with electrodes located on the surface of the skull, instead of directly in the brain.<ref>{{cite web|author=Martins Iduwe|url=https://www.academia.edu/32267156|title=Brain Computer Interface|publisher=Academia.edu|accessdate=5 December 2023}}</ref>

Research teams led by the BrainGate group and another at [[University of Pittsburgh Medical Center]], both in collaborations with the [[United States Department of Veterans Affairs]] (VA), demonstrated control of prosthetic limbs with many degrees of freedom using direct connections to arrays of neurons in the motor cortex of tetraplegia patients.<ref>{{cite journal | vauthors = Hochberg LR, Bacher D, Jarosiewicz B, Masse NY, Simeral JD, Vogel J, Haddadin S, Liu J, Cash SS, van der Smagt P, Donoghue JP | display-authors = 6 | title = Reach and grasp by people with tetraplegia using a neurally controlled robotic arm | journal = Nature | volume = 485 | issue = 7398 | pages = 372–375 | date = May 2012 | pmid = 22596161 | pmc = 3640850 | doi = 10.1038/nature11076 | bibcode = 2012Natur.485..372H }}</ref><ref>{{cite journal | vauthors = Collinger JL, Wodlinger B, Downey JE, Wang W, Tyler-Kabara EC, Weber DJ, McMorland AJ, Velliste M, Boninger ML, Schwartz AB | display-authors = 6 | title = High-performance neuroprosthetic control by an individual with tetraplegia | journal = Lancet | volume = 381 | issue = 9866 | pages = 557–564 | date = February 2013 | pmid = 23253623 | pmc = 3641862 | doi = 10.1016/S0140-6736(12)61816-9 }}</ref>

====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>

A 2021 study reported that a paralyzed patient was able to communicate 15 words per minute using a brain implant that analyzed vocal tract motor neurons.<ref>{{cite web | vauthors = Hamilton J | date = 14 July 2021 | url = https://www.npr.org/sections/health-shots/2021/07/14/1016028911/experimental-brain-implant-lets-man-with-paralysis-turn-his-thoughts-into-words | title = Experimental Brain Implant Lets Man With Paralysis Turn His Thoughts Into Words | work =  All Things Considered | publisher = NPR }}</ref><ref name="Neuroprosthesis for Decoding Speech"/>

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 }}</ref>

==== Technical challenges ====
There exist a number of technical challenges to recording brain activity with invasive BCIs. Advances in [[CMOS]] technology are pushing and enabling integrated, invasive BCI designs with smaller size, lower power requirements, and higher signal acquisition capabilities.<ref>{{Cite journal| vauthors = Zhang M, Tang Z, Liu X, Van der Spiegel J |date= April 2020 |title=Electronic neural interfaces |journal=Nature Electronics |language=en |volume=3 |issue=4 |pages=191–200 |doi=10.1038/s41928-020-0390-3 |s2cid= 216508360 |issn=2520-1131 }}</ref> Invasive BCIs involve electrodes that penetrate brain tissue in an attempt to record [[action potential]] signals (also known as spikes) from individual, or small groups of, neurons near the electrode. The interface between a recording electrode and the electrolytic solution surrounding neurons has been modelled using the [[Hodgkin-Huxley model]].<ref>{{cite journal | vauthors = Hodgkin AL, Huxley AF | title = A quantitative description of membrane current and its application to conduction and excitation in nerve | journal = The Journal of Physiology | volume = 117 | issue = 4 | pages = 500–544 | date = August 1952 | pmid = 12991237 | pmc = 1392413 | doi = 10.1113/jphysiol.1952.sp004764 }}</ref><ref name="Revealing neuronal function through">{{cite journal | vauthors = Obien ME, Deligkaris K, Bullmann T, Bakkum DJ, Frey U | title = Revealing neuronal function through microelectrode array recordings | journal = Frontiers in Neuroscience | volume = 8 | pages = 423 | date = 2015 | pmid = 25610364 | doi = 10.3389/fnins.2014.00423 | pmc = 4285113 | doi-access = free }}</ref>

Electronic limitations to invasive BCIs have been an active area of research in recent decades. While [[Patch clamp|intracellular recordings]] of neurons reveal action potential voltages on the scale of hundreds of millivolts, chronic invasive BCIs rely on recording extracellular voltages which typically are three orders of magnitude smaller, existing at hundreds of microvolts.<ref name=":8">{{Cite journal| vauthors = Harrison RR |date= July 2008 |title=The Design of Integrated Circuits to Observe Brain Activity |journal=Proceedings of the IEEE|volume=96|issue=7|pages=1203–1216|doi=10.1109/JPROC.2008.922581|s2cid= 7020369 |issn=1558-2256}}</ref> Further adding to the challenge of detecting signals on the scale of microvolts is the fact that the electrode-tissue interface has a high [[capacitance]] at small voltages. Due to the nature of these small signals, for BCI systems that incorporate functionality onto an integrated circuit, each electrode requires its own [[amplifier]] and [[Analog-to-digital converter|ADC]], which convert analog extracellular voltages into digital signals.<ref name=":8" /> Because a typical neuron action potential lasts for one millisecond, BCIs measuring spikes must have sampling rates ranging from 300&nbsp;Hz to 5&nbsp;kHz. Yet another concern is that invasive BCIs must be low-power, so as to dissipate less heat to surrounding tissue; at the most basic level more power is traditionally needed to optimize [[signal-to-noise ratio]].<ref name="Revealing neuronal function through"/> Optimal battery design is an active area of research in BCIs.<ref>{{Cite book| vauthors = Haci D, Liu Y, Ghoreishizadeh SS, Constandinou TG |title= 2020 IEEE 11th Latin American Symposium on Circuits & Systems (LASCAS) |chapter= Key Considerations for Power Management in Active Implantable Medical Devices |date= February 2020 |pages=1–4|doi=10.1109/LASCAS45839.2020.9069004|isbn= 978-1-7281-3427-7 |s2cid= 215817530 |chapter-url= https://discovery.ucl.ac.uk/id/eprint/10090175/ }}</ref>[[File:Invasive and partially invasive BCIs.png|thumb|Illustration of invasive and partially invasive BCIs: electrocorticography (ECoG), endovascular, and intracortical microelectrode.|248x248px]]Challenges existing in the area of [[material science]] are central to the design of invasive BCIs. Variations in signal quality over time have been commonly observed with implantable microelectrodes.<ref>{{cite journal | vauthors = Downey JE, Schwed N, Chase SM, Schwartz AB, Collinger JL | title = Intracortical recording stability in human brain-computer interface users | journal = Journal of Neural Engineering | volume = 15 | issue = 4 | pages = 046016 | date = August 2018 | pmid = 29553484 | doi = 10.1088/1741-2552/aab7a0 | bibcode = 2018JNEng..15d6016D | s2cid = 3961913 }}</ref> Optimal material and mechanical characteristics for long term signal stability in invasive BCIs has been an active area of research.<ref>{{cite journal | vauthors = Szostak KM, Grand L, Constandinou TG | title = Neural Interfaces for Intracortical Recording: Requirements, Fabrication Methods, and Characteristics | journal = Frontiers in Neuroscience | volume = 11 | pages = 665 | date = 2017 | pmid = 29270103 | doi = 10.3389/fnins.2017.00665 | pmc = 5725438 | doi-access = free }}</ref> It has been proposed that the formation of [[glial scar]]ring, secondary to damage at the electrode-tissue interface, is likely responsible for electrode failure and reduced recording performance.<ref name=":10" /> Research has suggested that [[blood-brain barrier]] leakage, either at the time of insertion or over time, may be responsible for the inflammatory and glial reaction to chronic microelectrodes implanted in the brain.<ref name=":10">{{cite journal | vauthors = Saxena T, Karumbaiah L, Gaupp EA, Patkar R, Patil K, Betancur M, Stanley GB, Bellamkonda RV | display-authors = 6 | title = The impact of chronic blood-brain barrier breach on intracortical electrode function | journal = Biomaterials | volume = 34 | issue = 20 | pages = 4703–4713 | date = July 2013 | pmid = 23562053 | doi = 10.1016/j.biomaterials.2013.03.007 }}</ref><ref>{{cite journal | vauthors = Nolta NF, Christensen MB, Crane PD, Skousen JL, Tresco PA | title = BBB leakage, astrogliosis, and tissue loss correlate with silicon microelectrode array recording performance | journal = Biomaterials | volume = 53 | pages = 753–762 | date = 2015-06-01 | pmid = 25890770 | doi = 10.1016/j.biomaterials.2015.02.081 }}</ref> As a result, flexible<ref>{{cite journal | vauthors = Robinson JT, Pohlmeyer E, Gather MC, Kemere C, Kitching JE, Malliaras GG, Marblestone A, Shepard KL, Stieglitz T, Xie C | display-authors = 6 | title = Developing Next-generation Brain Sensing Technologies - A Review | journal = IEEE Sensors Journal | volume = 19 | issue = 22 | pages = 10163–10175 | date = November 2019 | pmid = 32116472 | doi = 10.1109/JSEN.2019.2931159 | pmc = 7047830 }}</ref><ref>{{cite journal | vauthors = Luan L, Wei X, Zhao Z, Siegel JJ, Potnis O, Tuppen CA, Lin S, Kazmi S, Fowler RA, Holloway S, Dunn AK, Chitwood RA, Xie C | display-authors = 6 | title = Ultraflexible nanoelectronic probes form reliable, glial scar-free neural integration | journal = Science Advances | volume = 3 | issue = 2 | pages = e1601966 | date = February 2017 | pmid = 28246640 | pmc = 5310823 | doi = 10.1126/sciadv.1601966 | bibcode = 2017SciA....3E1966L }}</ref><ref>{{cite journal | vauthors = Frank JA, Antonini MJ, Anikeeva P | title = Next-generation interfaces for studying neural function | journal = Nature Biotechnology | volume = 37 | issue = 9 | pages = 1013–1023 | date = September 2019 | pmid = 31406326 | pmc = 7243676 | doi = 10.1038/s41587-019-0198-8 }}</ref> and tissue-like designs<ref name=":9">{{cite journal | vauthors = Hong G, Viveros RD, Zwang TJ, Yang X, Lieber CM | title = Tissue-like Neural Probes for Understanding and Modulating the Brain | journal = Biochemistry | volume = 57 | issue = 27 | pages = 3995–4004 | date = July 2018 | pmid = 29529359 | pmc = 6039269 | doi = 10.1021/acs.biochem.8b00122 }}</ref><ref>{{cite journal | vauthors = Viveros RD, Zhou T, Hong G, Fu TM, Lin HG, Lieber CM | title = Advanced One- and Two-Dimensional Mesh Designs for Injectable Electronics | journal = Nano Letters | volume = 19 | issue = 6 | pages = 4180–4187 | date = June 2019 | pmid = 31075202 | pmc = 6565464 | doi = 10.1021/acs.nanolett.9b01727 | bibcode = 2019NanoL..19.4180V }}</ref> have been researched and developed to minimize [[foreign-body reaction]] by means of matching the [[Young's modulus]] of the electrode closer to that of brain tissue.<ref name=":9" />