Editing Brain–computer interface (section)

===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>