Editing Brain–computer interface (section)

==Future directions==
[[File:Brain-computer interface.jpeg|thumb|right|Brain-computer interface]]
A consortium of 12 European partners completed a roadmap to support the European Commission in their funding decisions for the [[Horizon 2020]] framework program. The project was funded by the European Commission. It started in November 2013 and published a roadmap in April 2015.<ref>{{cite web|url=http://bnci-horizon-2020.eu/roadmap|title=Roadmap - BNCI Horizon 2020|website=bnci-horizon-2020.eu|access-date=2019-05-05}}</ref> A 2015 publication describes this project, as well as the Brain-Computer Interface Society.<ref name="bncihorizon2020">{{cite journal|title=BNCI Horizon 2020: towards a roadmap for the BCI community|doi=10.1080/2326263X.2015.1008956 | volume=2 | journal=Brain-Computer Interfaces|pages=1–10|year=2015 | vauthors = Brunner C, Birbaumer N, Blankertz B, Guger C, Kübler A, Mattia D, Millán JD, Miralles F, Nijholt A, Opisso E, Ramsey N | display-authors = 6 |hdl=1874/350349 |s2cid=15822773 |url=http://infoscience.epfl.ch/record/205169 |hdl-access=free }}</ref> It reviewed work within this project that further defined BCIs and applications, explored recent trends, discussed ethical issues, and evaluated directions for new BCIs.

Other recent publications too have explored future BCI directions for new groups of disabled users.<ref name="Wolpaw, J.R 2012"/><ref>{{cite book | vauthors = Allison BZ, Dunne S, Leeb R, Millan J, Nijholt A| date = 2013 | title = Towards Practical Brain-Computer Interfaces: Bridging the Gap from Research to Real-World Applications. | publisher = Springer Verlag | location = Berlin Heidelberg |isbn=978-3-642-29746-5}}</ref>

===Disorders of consciousness (DOC)===
Some people have a [[disorder of consciousness]] (DOC). This state is defined to include people in a coma and those in a [[vegetative state]] (VS) or [[minimally conscious state]] (MCS). BCI research seeks to address DOC. A key initial goal is to identify patients who can perform basic cognitive tasks, which would change their diagnosis, and allow them to make important decisions (such as whether to seek therapy, where to live, and their views on end-of-life decisions regarding them). Patients incorrectly diagnosed may die as a result of end-of-life decisions made by others. The prospect of using BCI to communicate with such patients is a tantalizing prospect.<ref>{{cite book | vauthors = Edlinger G, Allison BZ, Guger C | chapter = How many people could use a BCI system? | pages = 33–66 | veditors = Kansaku K, Cohen L, Birbaumer N |title=Clinical Systems Neuroscience |date=2015 |location=Tokyo  | publisher = pringer Verlag Japan |isbn=978-4-431-55037-2}}</ref><ref>{{cite journal | vauthors = Chatelle C, Chennu S, Noirhomme Q, Cruse D, Owen AM, Laureys S | title = Brain-computer interfacing in disorders of consciousness | journal = Brain Injury | volume = 26 | issue = 12 | pages = 1510–1522 | year = 2012 | pmid = 22759199 | doi = 10.3109/02699052.2012.698362 | s2cid = 6498232 | hdl = 2268/162403 | hdl-access = free }}</ref>

Many such patients cannot use BCIs based on vision. Hence, tools must rely on auditory and/or vibrotactile stimuli. Patients may wear headphones and/or vibrotactile stimulators placed on responsive body parts. Another challenge is that patients may be able to communicate only at unpredictable intervals. Home devices can allow communications when the patient is ready. 

Automated tools can ask questions that patients can easily answer, such as "Is your father named George?" or "Were you born in the USA?" Automated instructions inform patients how to convey yes or no, for example by focusing their attention on stimuli on the right vs. left wrist. This focused attention produces reliable changes in [[electroencephalography|EEG patterns]] that can help determine whether the patient is able to communicate.<ref name="BolyMassimini2012">{{cite journal | vauthors = Boly M, Massimini M, Garrido MI, Gosseries O, Noirhomme Q, Laureys S, Soddu A | title = Brain connectivity in disorders of consciousness | journal = Brain Connectivity | volume = 2 | issue = 1 | pages = 1–10 | year = 2012 | pmid = 22512333 | doi = 10.1089/brain.2011.0049 | hdl-access = free | s2cid = 6447538 | hdl = 2268/131984 }}</ref><ref>{{cite journal | vauthors = Gibson RM, Fernández-Espejo D, Gonzalez-Lara LE, Kwan BY, Lee DH, Owen AM, Cruse D | title = Multiple tasks and neuroimaging modalities increase the likelihood of detecting covert awareness in patients with disorders of consciousness | journal = Frontiers in Human Neuroscience | volume = 8 | pages = 950 | year = 2014 | pmid = 25505400 | pmc = 4244609 | doi = 10.3389/fnhum.2014.00950 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Risetti M, Formisano R, Toppi J, Quitadamo LR, Bianchi L, Astolfi L, Cincotti F, Mattia D | display-authors = 6 | title = On ERPs detection in disorders of consciousness rehabilitation | journal = Frontiers in Human Neuroscience | volume = 7 | pages = 775 | year = 2013 | pmid = 24312041 | pmc = 3834290 | doi = 10.3389/fnhum.2013.00775 | doi-access = free }}</ref>

===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 | pages = 57 | date = April 2020 | pmid = 32334608 | pmc = 7183617 | doi = 10.1186/s12984-020-00686-2 | doi-access = free }}</ref> Several groups have explored systems and methods for motor recovery that include BCIs.<ref>{{cite journal | vauthors = Remsik A, Young B, Vermilyea R, Kiekhoefer L, Abrams J, Evander Elmore S, Schultz P, Nair V, Edwards D, Williams J, Prabhakaran V | display-authors = 6 | title = A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke | journal = Expert Review of Medical Devices | volume = 13 | issue = 5 | pages = 445–454 | date = May 2016 | pmid = 27112213 | pmc = 5131699 | doi = 10.1080/17434440.2016.1174572 }}</ref><ref>{{cite journal | vauthors = Monge-Pereira E, Ibañez-Pereda J, Alguacil-Diego IM, Serrano JI, Spottorno-Rubio MP, Molina-Rueda F | title = Use of Electroencephalography Brain-Computer Interface Systems as a Rehabilitative Approach for Upper Limb Function After a Stroke: A Systematic Review | journal = PM&R | volume = 9 | issue = 9 | pages = 918–932 | date = September 2017 | pmid = 28512066 | doi = 10.1016/j.pmrj.2017.04.016 | s2cid = 20808455 | url = https://discovery.ucl.ac.uk/id/eprint/10042536/ }}</ref><ref>{{Cite book| vauthors = Sabathiel N, Irimia DC, Allison BZ, Guger C, Edlinger G |title=Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience |date=17 July 2016| chapter = Paired Associative Stimulation with Brain-Computer Interfaces: A New Paradigm for Stroke Rehabilitation|series=Lecture Notes in Computer Science|volume=9743|pages=261–272|doi=10.1007/978-3-319-39955-3_25|isbn=978-3-319-39954-6}}</ref><ref>{{cite book | vauthors = Riccio A, Pichiorri F, Schettini F, Toppi J, Risetti M, Formisano R, Molinari M, Astolfi L, Cincotti F, Mattia D | title = Brain-Computer Interfaces: Lab Experiments to Real-World Applications | display-authors = 6 | chapter = Interfacing brain with computer to improve communication and rehabilitation after brain damage | volume = 228 | pages = 357–387 | year = 2016 | pmid = 27590975 | doi = 10.1016/bs.pbr.2016.04.018 | isbn = 978-0-12-804216-8 | series = Progress in Brain Research }}</ref> In this approach, a BCI measures motor activity while the patient imagines or attempts movements as directed by a therapist. The BCI may provide two benefits: (1) if the BCI indicates that a patient is not imagining a movement correctly (non-compliance), then the BCI could inform the patient and therapist; and (2) rewarding feedback such as functional stimulation or the movement of a virtual avatar also depends on the patient's correct movement imagery.

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>

===Functional brain mapping===
In 2014, some 400,000 people underwent [[brain mapping]] during neurosurgery. This procedure is often required for people who do not respond to [[medication]].<ref>{{cite journal | vauthors = Radzik I, Miziak B, Dudka J, Chrościńska-Krawczyk M, Czuczwar SJ | title = Prospects of epileptogenesis prevention | journal = Pharmacological Reports | volume = 67 | issue = 3 | pages = 663–668 | date = June 2015 | pmid = 25933984 | doi = 10.1016/j.pharep.2015.01.016 | s2cid = 31284248 }}</ref> During this procedure, electrodes are placed on the brain to precisely identify the locations of structures and functional areas. Patients may be awake during neurosurgery and asked to perform tasks, such as moving fingers or repeating words. This is necessary so that surgeons can remove the desired tissue while sparing other regions. Removing too much brain tissue can cause permanent damage, while removing too little can mandate additional neurosurgery.{{citation needed|date=December 2023}} 

Researchers explored ways to improve neurosurgical mapping. This work focuses largely on high gamma activity, which is difficult to detect non-invasively. Results improved methods for identifying key functional areas.<ref>{{cite journal | vauthors = Ritaccio A, Brunner P, Gunduz A, Hermes D, Hirsch LJ, Jacobs J, Kamada K, Kastner S, Knight RT, Lesser RP, Miller K, Sejnowski T, Worrell G, Schalk G | display-authors = 6 | title = Proceedings of the Fifth International Workshop on Advances in Electrocorticography | journal = Epilepsy & Behavior | volume = 41 | pages = 183–192 | date = December 2014 | pmid = 25461213 | pmc = 4268064 | doi = 10.1016/j.yebeh.2014.09.015 }}</ref>

===Flexible devices===
[[Flexible electronics]] are [[polymer]]s or other flexible materials (e.g. [[silk]],<ref name="KimSilk">{{cite journal | vauthors = Kim DH, Viventi J, Amsden JJ, Xiao J, Vigeland L, Kim YS, Blanco JA, Panilaitis B, Frechette ES, Contreras D, Kaplan DL, Omenetto FG, Huang Y, Hwang KC, Zakin MR, Litt B, Rogers JA | display-authors = 6 | title = Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics | journal = Nature Materials | volume = 9 | issue = 6 | pages = 511–517 | date = June 2010 | pmid = 20400953 | pmc = 3034223 | doi = 10.1038/nmat2745 | bibcode = 2010NatMa...9..511K }}</ref> [[pentacene]], [[polydimethylsiloxane|PDMS]], [[Parylene]], [[polyimide]]<ref name="Boppart">{{cite journal | vauthors = Boppart SA, Wheeler BC, Wallace CS | title = A flexible perforated microelectrode array for extended neural recordings | journal = IEEE Transactions on Bio-Medical Engineering | volume = 39 | issue = 1 | pages = 37–42 | date = January 1992 | pmid = 1572679 | doi = 10.1109/10.108125 | s2cid = 36593459 }}</ref>) printed with [[circuitry]]; the flexibility allows the electronics to bend. The [[semiconductor device fabrication|fabrication techniques]] used to create these devices resembles those used to create [[integrated circuit]]s and [[microelectromechanical systems]] (MEMS).{{Citation needed|date=December 2019|reason=removing citation to predatory publisher content}} 

Flexible neural interfaces may minimize brain tissue trauma related to mechanical mismatch between electrode and tissue.<ref>{{cite journal | vauthors = Thompson CH, Zoratti MJ, Langhals NB, Purcell EK | title = Regenerative Electrode Interfaces for Neural Prostheses | journal = Tissue Engineering. Part B, Reviews | volume = 22 | issue = 2 | pages = 125–135 | date = April 2016 | pmid = 26421660 | doi = 10.1089/ten.teb.2015.0279 | doi-access = free }}</ref> 

===Neural dust===
{{main|Neural dust}}
[[Neural dust]] is millimeter-sized devices operated as [[Wireless power transfer|wirelessly powered]] nerve sensors that were proposed in a 2011 paper from the [[University of California, Berkeley]] Wireless Research Center.<ref name=Rabaey>{{Cite book| vauthors = Rabaey JM |date=September 2011 |doi=10.1109/essderc.2011.6044240|isbn=978-1-4577-0707-0|chapter=Brain-machine interfaces as the new frontier in extreme miniaturization|title=2011 Proceedings of the European Solid-State Device Research Conference (ESSDERC)|pages=19–24|s2cid=47542923}}</ref><ref>{{Cite journal| vauthors = Warneke B, Last M, Liebowitz B, Pister KS |s2cid=21557|date=January 2001|title=Smart Dust: communicating with a cubic-millimeter computer|journal=Computer|volume=34|issue=1|pages=44–51|doi=10.1109/2.895117|issn=0018-9162}}</ref> In one model, [[local field potential]]s could be distinguished from [[action potential]] "spikes", which would offer greatly diversified data vs conventional techniques.<ref name=Rabaey/>