Brain–computer interface: Difference between revisions

Revision as of 04:18, 7 July 2025

Revision as of 05:53, 12 July 2025

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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 |pagesarticle-number=15 |doi=10.1186/s12984-021-00820-8 |pmc=7825186 |pmid=33485365 |doi-access=free}}</ref>

====Dry active electrode arrays====

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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 |pagesarticle-number=1319 |bibcode=2013NatSR...3.1319P |doi=10.1038/srep01319 |pmc=3584574 |pmid=23448946}}</ref><ref>{{cite news |last=Gorman |first=James |date=28 February 2013 |title=One Rat Thinks, and Another Reacts |url=https://www.nytimes.com/2013/03/01/science/new-research-suggests-two-rat-brains-can-be-linked.html |access-date=28 February 2013 |work=The New York Times}}</ref><ref>{{cite web |last=Sample |first=Ian |date=1 March 2013 |title=Brain-to-brain interface lets rats share information via internet |url=https://www.theguardian.com/science/2013/feb/28/brains-rats-connected-share-information |access-date=2 March 2013 |website=The Guardian}}</ref>

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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==Collaborative BCIs==

The idea of combining/integrating brain signals from multiple individuals was introduced at Humanity+ @Caltech, in December 2010, by Adrian Stoica, who referred to the concept as multi-brain aggregation.<ref>{{Cite web|date=2017-10-05|title=David Pearce – Humanity Plus|url=https://activistjourneys.wordpress.com/david-pearce-humanity-plus/|access-date=2021-12-30|language=en}}</ref><ref>{{Cite web|vauthors=Stoica A|date=2010|title=Speculations on Robots, Cyborgs & Telepresence|website=[[YouTube]]|url=https://www.youtube.com/watch?v=nqByb7VEnZk|url-status=live|archive-url=https://web.archive.org/web/20211228222826/https://www.youtube.com/watch?v=nqByb7VEnZk|archive-date=28 December 2021|access-date=28 December 2021}}</ref><ref>{{Cite web|title=Experts to 'redefine the future' at Humanity+ @ CalTech |website=Kurzweil|url=https://www.kurzweilai.net/experts-to-redefine-the-future-at-humanity-caltech|access-date=2021-12-30|language=en-US}}</ref> A patent was applied for in 2012.<ref>{{Cite patent|number=WO2012100081A2|title=Aggregation of bio-signals from multiple individuals to achieve a collective outcome|gdate=2012-07-26|invent1=Stoica|inventor1-first=Adrian|url=https://patents.google.com/patent/WO2012100081A2/en}}</ref><ref>{{cite journal | vauthors = Wang Y, Jung TP | title = A collaborative brain-computer interface for improving human performance | journal = PLOS ONE | volume = 6 | issue = 5 | pages = e20422 | date = 2011-05-31 | pmid = 21655253 | pmc = 3105048 | doi = 10.1371/journal.pone.0020422 | bibcode = 2011PLoSO...620422W | doi-access = free }}</ref><ref>{{cite journal | vauthors = Eckstein MP, Das K, Pham BT, Peterson MF, Abbey CK, Sy JL, Giesbrecht B | title = Neural decoding of collective wisdom with multi-brain computing | journal = NeuroImage | volume = 59 | issue = 1 | pages = 94–108 | date = January 2012 | pmid = 21782959 | doi = 10.1016/j.neuroimage.2011.07.009 | s2cid = 14930969 }}</ref> Stoica's first paper on the topic appeared in 2012, after the publication of his patent application.<ref>{{Cite book| vauthors = Stoica A |title= 2012 Third International Conference on Emerging Security Technologies |chapter= MultiMind: Multi-Brain Signal Fusion to Exceed the Power of a Single Brain |date= September 2012 |chapter-url= https://ieeexplore.ieee.org/document/6328091 |pages=94–98 |doi=10.1109/EST.2012.47|isbn= 978-0-7695-4791-6 |s2cid= 6783719 }}</ref>

== Ethical considerations==

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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 | pagesarticle-number = 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>