Editing Brain–computer interface (section)

====Electroencephalography (EEG)-based brain-computer interfaces====

[[File:ElectroEncephalogram.png|thumb|Recordings of brainwaves produced by an [[electroencephalogram]]]]

After Vidal stated the BCI challenge, the initial reports on non-invasive approaches included control of a cursor in 2D using VEP,<ref>Vidal 1977</ref> control of a buzzer using CNV,<ref>Bozinovska et al. 1988, 1990</ref> control of a physical object, a robot, using a brain rhythm (alpha),<ref>Bozinovski et al. 1988</ref> control of a text written on a screen using P300.<ref>Farwell and Donchin, 1988</ref><ref name="Bozinovski1" />

In the early days of BCI research, another substantial barrier to using EEG was that extensive training was required. For example, in experiments beginning in the mid-1990s, Niels Birbaumer at the [[University of Tübingen]] in [[Germany]] trained paralysed people to self-regulate the slow cortical potentials in their EEG to such an extent that these signals could be used as a binary signal to control a computer cursor. (Birbaumer had earlier trained [[Epilepsy|epileptics]] to prevent impending fits by controlling this low voltage wave.) The experiment trained ten patients to move a computer cursor. The process was slow, requiring more than an hour for patients to write 100 characters with the cursor, while training often took months. The slow cortical potential approach has fallen away in favor of approaches that require little or no training, are faster and more accurate, and work for a greater proportion of users.<ref>{{Cite magazine |last=Winters |first=Jeffrey |date=May 2003 |title=Communicating by Brain Waves |url=http://www.psychologytoday.com/articles/200307/communicating-brain-waves |magazine=Psychology Today}}</ref>

Another research parameter is the type of [[Neural oscillation|oscillatory activity]] that is measured. Gert Pfurtscheller founded the BCI Lab 1991 and conducted the first online BCI based on oscillatory features and classifiers. Together with Birbaumer and Jonathan Wolpaw at [[New York State University]] they focused on developing technology that would allow users to choose the brain signals they found easiest to operate a BCI, including ''[[Mu wave|mu]]'' and ''[[Beta wave|beta]]'' rhythms.{{Citation needed|date=May 2024}}

A further parameter is the method of feedback used as shown in studies of [[P300 (Neuroscience)|P300]] signals. Patterns of P300 waves are generated involuntarily ([[Event-related potential|stimulus-feedback]]) when people see something they recognize and may allow BCIs to decode categories of thoughts without training.{{Citation needed|date=May 2024}}

A 2005 study reported EEG emulation of digital control circuits, using a CNV flip-flop.<ref>Adrijan Bozinovski "CNV flip-flop as a brain-computer interface paradigm" In J. Kern, S. Tonkovic, et al. (Eds) Proc 7th Conference of the Croatian Association of Medical Informatics, pp. 149-154, Rijeka, 2005</ref> A 2009 study reported noninvasive EEG control of a robotic arm using a CNV flip-flop.<ref>{{cite conference |last1=Bozinovski |first1=Adrijan |last2=Bozinovska |first2=Liljana |year=2009 |title=Anticipatory brain potentials in a Brain-Robot Interface paradigm |conference=2009 4th International IEEE/EMBS Conference on Neural Engineering |publisher=IEEE |pages=451–454 |doi=10.1109/ner.2009.5109330}}</ref> A 2011 study reported control of two robotic arms solving [[Tower of Hanoi]] task with three disks using a CNV flip-flop.<ref>{{cite journal |last1=Božinovski |first1=Adrijan |last2=Tonković |first2=Stanko |last3=Išgum |first3=Velimir |last4=Božinovska |first4=Liljana |year=2011 |title=Robot Control Using Anticipatory Brain Potentials |journal=Automatika |language=en |volume=52 |issue=1 |pages=20–30 |doi=10.1080/00051144.2011.11828400 |s2cid=33223634 |doi-access=free}}</ref> A 2015 study described EEG-emulation of a [[Schmitt trigger]], flip-flop, [[demultiplexer]], and [[modem]].<ref>{{cite journal |last1=Bozinovski |first1=Stevo |last2=Bozinovski |first2=Adrijan |year=2015 |title=Mental States, EEG Manifestations, and Mentally Emulated Digital Circuits for Brain-Robot Interaction |journal=IEEE Transactions on Autonomous Mental Development |publisher=Institute of Electrical and Electronics Engineers (IEEE) |volume=7 |issue=1 |pages=39–51 |doi=10.1109/tamd.2014.2387271 |issn=1943-0604 |s2cid=21464338 |doi-access=free}}</ref>

Advances by [[Bin He]] and his team at [[University of Minnesota]] suggest the potential of EEG-based brain-computer interfaces to accomplish tasks close to invasive brain-computer interfaces. Using advanced functional neuroimaging including BOLD functional [[MRI]] and [[EEG]] source imaging, They identified the co-variation and co-localization of [[electrophysiological]] and [[hemodynamic]] signals.<ref>{{cite journal |vauthors=Yuan H, Liu T, Szarkowski R, Rios C, Ashe J, He B |date=February 2010 |title=Negative covariation between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: an EEG and fMRI study of motor imagery and movements |journal=NeuroImage |volume=49 |issue=3 |pages=2596–2606 |doi=10.1016/j.neuroimage.2009.10.028 |pmc=2818527 |pmid=19850134}}</ref> Refined by a neuroimaging approach and a training protocol, They fashioned a non-invasive EEG based brain-computer interface to control the flight of a virtual helicopter in 3-dimensional space, based upon motor imagination.<ref>{{cite journal |vauthors=Doud AJ, Lucas JP, Pisansky MT, He B |year=2011 |title=Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain-computer interface |journal=PLOS ONE |volume=6 |issue=10 |pages=e26322 |bibcode=2011PLoSO...626322D |doi=10.1371/journal.pone.0026322 |pmc=3202533 |pmid=22046274 |doi-access=free |veditors=Gribble PL}}</ref> In June 2013 they announced a technique to guide a remote-control helicopter through an obstacle course.<ref>{{cite web |date=5 June 2013 |title=Thought-guided helicopter takes off |url=https://www.bbc.co.uk/news/science-environment-22764978 |access-date=5 June 2013 |work=BBC News}}</ref> They also solved the EEG [[inverse problem]] and then used the resulting virtual EEG for BCI tasks. Well-controlled studies suggested the merits of such a source analysis-based BCI.<ref>{{cite journal |vauthors=Qin L, Ding L, He B |date=September 2004 |title=Motor imagery classification by means of source analysis for brain-computer interface applications |journal=Journal of Neural Engineering |volume=1 |issue=3 |pages=135–141 |bibcode=2004JNEng...1..135Q |doi=10.1088/1741-2560/1/3/002 |pmc=1945182 |pmid=15876632}}</ref>

A 2014 study reported that severely motor-impaired patients could communicate faster and more reliably with non-invasive EEG BCI than with muscle-based communication channels.<ref>{{cite journal |vauthors=Höhne J, Holz E, Staiger-Sälzer P, [[Klaus-Robert Müller|Müller KR]], Kübler A, Tangermann M |date=2014 |title=Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution |journal=PLOS ONE |volume=9 |issue=8 |pages=e104854 |bibcode=2014PLoSO...9j4854H |doi=10.1371/journal.pone.0104854 |pmc=4146550 |pmid=25162231 |doi-access=free}}</ref>

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