Editing Brain–computer interface (section)

====Other research====
In addition to predicting [[kinematic]] and [[kinetic energy|kinetic]] parameters of limb movements, BCIs that predict [[electromyographic]] or electrical activity of the muscles of primates are in process.<ref>{{cite journal | vauthors = Santucci DM, Kralik JD, Lebedev MA, Nicolelis MA | title = Frontal and parietal cortical ensembles predict single-trial muscle activity during reaching movements in primates | journal = The European Journal of Neuroscience | volume = 22 | issue = 6 | pages = 1529–1540 | date = September 2005 | pmid = 16190906 | doi = 10.1111/j.1460-9568.2005.04320.x | s2cid = 31277881 }}</ref> Such BCIs could restore mobility in paralyzed limbs by electrically stimulating muscles.

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, a study reported a BCI that had the potential to help patients with speech impairment caused by neurological disorders. Their BCI used high-density [[electrocorticography]] to tap neural activity from a patient's brain and used [[deep learning]] to synthesize speech.<ref>{{cite journal | vauthors = Anumanchipalli GK, Chartier J, Chang EF | title = Speech synthesis from neural decoding of spoken sentences | journal = Nature | volume = 568 | issue = 7753 | pages = 493–498 | date = April 2019 | pmid = 31019317 | doi = 10.1038/s41586-019-1119-1 | pmc = 9714519 | s2cid = 129946122 | bibcode = 2019Natur.568..493A }}</ref><ref>{{cite journal | vauthors = Pandarinath C, Ali YH | title = Brain implants that let you speak your mind | language = EN | journal = Nature | volume = 568 | issue = 7753 | pages = 466–467 | date = April 2019 | pmid = 31019323 | doi = 10.1038/d41586-019-01181-y | doi-access = free | bibcode = 2019Natur.568..466P }}</ref> In 2021, those researchers reported the potential of a BCI to decode words and sentences in an [[anarthric]] patient who had been unable to speak for over 15 years.<ref name="Neuroprosthesis for Decoding Speech">{{cite journal | vauthors = Moses DA, Metzger SL, Liu JR, Anumanchipalli GK, Makin JG, Sun PF, Chartier J, Dougherty ME, Liu PM, Abrams GM, Tu-Chan A, Ganguly K, Chang EF | display-authors = 6 | title = Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria | journal = The New England Journal of Medicine | volume = 385 | issue = 3 | pages = 217–227 | date = July 2021 | pmid = 34260835 | doi = 10.1056/NEJMoa2027540 | pmc = 8972947 | s2cid = 235907121 }}</ref><ref>Belluck, Pam (14 July 2021). [https://www.nytimes.com/2021/07/14/health/speech-brain-implant-computer.html "Tapping Into the Brain to Help a Paralyzed Man Speak"]. ''The New York Times''.</ref>

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.

Development and implementation of a BCI system is complex and time-consuming. In response to this problem, Gerwin Schalk has been developing [[BCI2000]], a general-purpose system for BCI research, since 2000.<ref>{{cite web|url=https://www.neurotechcenter.org/publications/2010/using-bci2000-bci-research|title=Using BCI2000 in BCI Research|publisher=National Center for Adaptive Neurotechnology|accessdate=5 December 2023}}</ref>

A new 'wireless' approach uses [[light-gated ion channel]]s such as [[channelrhodopsin]] to control the activity of genetically defined subsets of neurons ''[[in vivo]]''. In the context of a simple learning task, illumination of [[transfected]] cells in the [[Somatosensory system|somatosensory cortex]] influenced decision-making in mice.<ref>{{cite journal | vauthors = Huber D, Petreanu L, Ghitani N, Ranade S, Hromádka T, Mainen Z, Svoboda K|author7-link=Karel Svoboda (scientist) | title = Sparse optical microstimulation in barrel cortex drives learned behaviour in freely moving mice | journal = Nature | volume = 451 | issue = 7174 | pages = 61–64 | date = January 2008 | pmid = 18094685 | pmc = 3425380 | doi = 10.1038/nature06445 | bibcode = 2008Natur.451...61H }}</ref>

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 [[Human–computer interaction]] (HCI). In a secondary, implicit control loop, the system adapts to its user, improving its [[usability]].<ref name=":0">{{cite journal |vauthors=Zander TO, Kothe C |date=April 2011 |title=Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general |journal=Journal of Neural Engineering |volume=8 |issue=2 |pages=025005 |bibcode=2011JNEng...8b5005Z |doi=10.1088/1741-2560/8/2/025005 |pmid=21436512 |s2cid=37168897}}</ref>

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>