Editing Brain–computer interface (section)

===Partially invasive BCIs===
Partially invasive BCI devices are implanted inside the skull but rest outside the brain rather than within the grey matter. They produce higher resolution signals than non-invasive BCIs where the bone tissue of the cranium deflects and deforms signals and have a lower risk of forming scar-tissue in the brain than fully invasive BCIs. Preclinical demonstration of intracortical BCIs from the stroke perilesional cortex has been conducted.<ref name="robust neuroprosthetic">{{cite journal | vauthors = Gulati T, Won SJ, Ramanathan DS, Wong CC, Bodepudi A, Swanson RA, Ganguly K | title = Robust neuroprosthetic control from the stroke perilesional cortex | journal = The Journal of Neuroscience | volume = 35 | issue = 22 | pages = 8653–8661 | date = June 2015 | pmid = 26041930 | pmc = 6605327 | doi = 10.1523/JNEUROSCI.5007-14.2015 }}</ref>

====Endovascular====
A systematic review published in 2020 detailed multiple clinical and non-clinical studies investigating the feasibility of endovascular BCIs.<ref>{{cite journal | vauthors = Soldozy S, Young S, Kumar JS, Capek S, Felbaum DR, Jean WC, Park MS, Syed HR | display-authors = 6 | title = A systematic review of endovascular stent-electrode arrays, a minimally invasive approach to brain-machine interfaces | language = en-US | journal = Neurosurgical Focus | volume = 49 | issue = 1 | pages = E3 | date = July 2020 | pmid = 32610291 | doi = 10.3171/2020.4.FOCUS20186 | s2cid = 220308983 | doi-access = free }}</ref>

In 2010, researchers affiliated with University of Melbourne began developing a BCI that could be inserted via the vascular system. Australian neurologist [[Thomas Oxley (Mount Sinai Hospital)|Thomas Oxley]] conceived the idea for this BCI, called Stentrode, earning funding from [[DARPA]]. Preclinical studies evaluated the technology in sheep.<ref name=":7" />

[[Stentrode]] is a monolithic [[Stent-electrode recording array|stent electrode array]] designed to be delivered via an intravenous catheter under image-guidance to the [[superior sagittal sinus]], in the region which lies adjacent to the [[motor cortex]].<ref name=":4">{{cite book | vauthors = Opie N | title = Brain-Computer Interface Research| chapter = The StentrodeTM Neural Interface System|date=2021 |pages=127–132| veditors = Guger C, Allison BZ, Tangermann M |series=SpringerBriefs in Electrical and Computer Engineering|place=Cham|publisher=Springer International Publishing |doi=10.1007/978-3-030-60460-8_13 |isbn = 978-3-030-60460-8 | s2cid = 234102889}}</ref> This proximity enables Stentrode to measure neural activity. The procedure is most similar to how venous sinus stents are placed for the treatment of [[idiopathic intracranial hypertension]].<ref>{{cite journal | vauthors = Teleb MS, Cziep ME, Lazzaro MA, Gheith A, Asif K, Remler B, Zaidat OO | title = Idiopathic Intracranial Hypertension. A Systematic Analysis of Transverse Sinus Stenting | journal = Interventional Neurology | volume = 2 | issue = 3 | pages = 132–143 | date = May 2014 | pmid = 24999351 | pmc = 4080637 | doi = 10.1159/000357503 }}</ref> Stentrode communicates neural activity to a battery-less telemetry unit implanted in the chest, which communicates wirelessly with an external telemetry unit capable of power and data transfer. While an endovascular BCI benefits from avoiding a [[craniotomy]] for insertion, risks such as [[Thrombus|clotting]] and [[venous thrombosis]] exist.<!--In pre-clinical animal studies implanted with Stentrode, twenty animals showed no evidence of thrombus formation after 190 days, possibly due to endothelial incorporation of the Stentrode into the vessel wall.<ref name=":4" />-->

Human trials with Stentrode were underway as of 2021.<ref name=":4" /> In November 2020, two participants with [[amyotrophic lateral sclerosis]] were able to wirelessly control an operating system to text, email, shop, and bank using direct thought using Stentrode,<ref>{{cite web | vauthors = Bryson S |title=Stentrode Device Allows Computer Control by ALS Patients with Partial Upper Limb Paralysis |url=https://alsnewstoday.com/news-posts/2020/11/05/stentrode-device-allows-computer-control-by-als-patients-with-partial-upper-limb-paralysis |website=ALS News Today|date=5 November 2020 }}</ref> marking the first time a brain-computer interface was implanted via the patient's blood vessels, eliminating the need for brain surgery. In January 2023, researchers reported no serious adverse events during the first year for all four patients, who could use it to operate computers.<ref>{{cite news |last1=Lanese |first1=Nicoletta |title=New 'thought-controlled' device reads brain activity through the jugular |url=https://www.livescience.com/brain-computer-interface-through-vein-safety |access-date=16 February 2023 |work=livescience.com |date=12 January 2023 |language=en |archive-date=16 February 2023 |archive-url=https://web.archive.org/web/20230216220922/https://www.livescience.com/brain-computer-interface-through-vein-safety |url-status=live }}</ref><ref>{{cite journal |last1=Mitchell |first1=Peter |last2=Lee |first2=Sarah C. M. |last3=Yoo |first3=Peter E. |last4=Morokoff |first4=Andrew |last5=Sharma |first5=Rahul P. |last6=Williams |first6=Daryl L. |last7=MacIsaac |first7=Christopher |last8=Howard |first8=Mark E. |last9=Irving |first9=Lou |last10=Vrljic |first10=Ivan |last11=Williams |first11=Cameron |last12=Bush |first12=Steven |last13=Balabanski |first13=Anna H. |last14=Drummond |first14=Katharine J. |last15=Desmond |first15=Patricia |last16=Weber |first16=Douglas |last17=Denison |first17=Timothy |last18=Mathers |first18=Susan |last19=O'Brien |first19=Terence J. |last20=Mocco |first20=J. |last21=Grayden |first21=David B. |last22=Liebeskind |first22=David S. |last23=Opie |first23=Nicholas L. |last24=Oxley |first24=Thomas J. |last25=Campbell |first25=Bruce C. V. |title=Assessment of Safety of a Fully Implanted Endovascular Brain-Computer Interface for Severe Paralysis in 4 Patients: The Stentrode With Thought-Controlled Digital Switch (SWITCH) Study |journal=JAMA Neurology |date=9 January 2023 |volume=80 |issue=3 |pages=270–278 |doi=10.1001/jamaneurol.2022.4847 |pmid=36622685 |pmc=9857731 |s2cid=255545643 |issn=2168-6149}}</ref>

==== Electrocorticography ====
[[Electrocorticography]] (ECoG) measures brain electrical activity from beneath the skull in a way similar to non-invasive electroencephalography, using electrodes embedded in a thin plastic pad placed above the cortex, beneath the [[dura mater]].<ref>{{cite book | last1=Serruya | first1=Mijail | last2=Donoghue | first2=John | chapter = Chapter III: Design Principles of a Neuromotor Prosthetic Device | title = Neuroprosthetics: Theory and Practice | veditors = Horch KW, Dhillon GS | publisher = Imperial College Press | year=2004 |pages=1158–1196 | doi=10.1142/9789812561763_0040 | archive-url=https://web.archive.org/web/20050404155139/http://donoghue.neuro.brown.edu/pubs/2003-SerruyaDonoghue-Chap3-preprint.pdf | archive-date=4 April 2005 |chapter-url=http://donoghue.neuro.brown.edu/pubs/2003-SerruyaDonoghue-Chap3-preprint.pdf}}</ref> ECoG technologies were first trialled in humans in 2004 by Eric Leuthardt and Daniel Moran from [[Washington University in St. Louis]]. In a later trial, the researchers enabled a teenage boy to play [[Space Invaders]].<ref>{{cite web | url = http://news-info.wustl.edu/news/page/normal/7800.html | title = Teenager moves video icons just by imagination | work = Press release | publisher = Washington University in St Louis | date = 9 October 2006 }}</ref> This research indicates that control is rapid, requires minimal training, balancing signal fidelity and level of invasiveness.{{refn|group=note|These electrodes had not been implanted in the patient with the intention of developing a BCI. The patient had had severe [[epilepsy]] and the electrodes were temporarily implanted to help his physicians localize seizure foci; the BCI researchers simply took advantage of this.<ref>{{cite journal | vauthors = Schalk G, Miller KJ, Anderson NR, Wilson JA, Smyth MD, Ojemann JG, Moran DW, Wolpaw JR, Leuthardt EC | display-authors = 6 | title = Two-dimensional movement control using electrocorticographic signals in humans | journal = Journal of Neural Engineering | volume = 5 | issue = 1 | pages = 75–84 | date = March 2008 | pmid = 18310813 | pmc = 2744037 | doi = 10.1088/1741-2560/5/1/008 | bibcode = 2008JNEng...5...75S }}</ref>}}

Signals can be either subdural or epidural, but are not taken from within the brain [[parenchyma]]. Patients are required to have invasive monitoring for localization and resection of an epileptogenic focus.{{Citation needed|date=May 2024}}

ECoG offers higher spatial resolution, better signal-to-noise ratio, wider frequency range, and less training requirements than scalp-recorded EEG, and at the same time has lower technical difficulty, lower clinical risk, and may have superior long-term stability than intracortical single-neuron recording.<ref>{{cite journal | vauthors = Nicolas-Alonso LF, Gomez-Gil J | title = Brain computer interfaces, a review | journal = Sensors | volume = 12 | issue = 2 | pages = 1211–1279 | date = 2012-01-31 | pmid = 22438708 | pmc = 3304110 | doi = 10.3390/s120201211 | bibcode = 2012Senso..12.1211N | doi-access = free }}</ref> This feature profile and evidence of the high level of control with minimal training requirements shows potential for real world application for people with motor disabilities.<ref name=Mondeofuse>{{cite news | vauthors = Yanagisawa T |title=Electrocorticographic Control of Prosthetic Arm in Paralyzed Patients |doi=10.1002/ana.22613 |quote= ECoG- Based BCI has advantage in signal and durability that are absolutely necessary for clinical application|work=[[American Neurological Association]] |year= 2011 |volume=71 |issue=3 |pages=353–361 }}</ref><ref name=TelepathicCommVowel>{{cite news | vauthors = Pei X |title=Decoding Vowels and Consonants in Spoken and Imagined Words Using Electrocorticographic Signals in Humans |pmid=21750369 |quote= Justin Williams, a biomedical engineer at the university, has already transformed the ECoG implant into a micro device that can be installed with a minimum of fuss. It has been tested in animals for a long period of time – the micro ECoG stays in place and doesn't seem to negatively affect the immune system.|work=J Neural Eng 046028th ser. 8.4 |year=2011 }}</ref> 

[[Edward Chang (neurosurgeon)|Edward Chang]] and Joseph Makin from [[UCSF Medical Center|UCSF]] reported that ECoG signals could be used to decode speech from epilepsy patients implanted with high-density ECoG arrays over the peri-Sylvian cortices.<ref>{{cite book | vauthors = Makin JG, Moses DA, Chang EF | title = Brain-Computer Interface Research | veditors = Guger C, Allison BZ, Gunduz A | chapter = Speech Decoding as Machine Translation|date=2021 |pages=23–33 |series=SpringerBriefs in Electrical and Computer Engineering|place=Cham|publisher=Springer International Publishing |language=en |doi=10.1007/978-3-030-79287-9_3 |isbn=978-3-030-79287-9 | s2cid = 239756345 }}</ref><ref>{{cite journal | vauthors = Makin JG, Moses DA, Chang EF | title = Machine translation of cortical activity to text with an encoder-decoder framework | journal = Nature Neuroscience | volume = 23 | issue = 4 | pages = 575–582 | date = April 2020 | pmid = 32231340 | doi = 10.1038/s41593-020-0608-8 | pmc = 10560395 | s2cid = 214704481 }}</ref> They reported word error rates of 3% (a marked improvement from prior efforts) utilizing an encoder-decoder [[neural network]], which translated ECoG data into one of fifty sentences composed of 250 unique words.{{cn|date=April 2025}}

====Functional near-infrared spectroscopy====
In 2014, a BCI using [[functional near-infrared spectroscopy]] for "locked-in" patients with [[amyotrophic lateral sclerosis]] (ALS) was able to restore basic ability to communicate.<ref>{{cite journal | vauthors = Gallegos-Ayala G, Furdea A, Takano K, Ruf CA, Flor H, Birbaumer N | title = Brain communication in a completely locked-in patient using bedside near-infrared spectroscopy | journal = Neurology | volume = 82 | issue = 21 | pages = 1930–1932 | date = May 2014 | pmid = 24789862 | pmc = 4049706 | doi = 10.1212/WNL.0000000000000449 }}</ref>

====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>

====Dry active electrode arrays====
In the early 1990s Babak Taheri, at [[University of California, Davis]] demonstrated the first single and multichannel dry active electrode arrays.<ref>{{cite journal |vauthors=Taheri BA, Knight RT, Smith RL |date=May 1994 |title=A dry electrode for EEG recording |url=https://zenodo.org/record/1253862 |journal=Electroencephalography and Clinical Neurophysiology |volume=90 |issue=5 |pages=376–383 |doi=10.1016/0013-4694(94)90053-1 |pmid=7514984}}</ref> The arrayed electrode was demonstrated to perform well compared to [[silver]]/[[silver chloride]] electrodes. The device consisted of four sensor sites with integrated electronics to reduce noise by [[impedance matching]]. The advantages of such electrodes are:

* no electrolyte used,
* no skin preparation,
* significantly reduced sensor size,
* compatibility with EEG monitoring systems.

The active electrode array is an integrated system containing an array of capacitive sensors with local integrated circuitry packaged with batteries to power the circuitry. This level of integration was required to achieve the result.

The electrode was tested on a test bench and on human subjects in four modalities, namely:

* spontaneous EEG,
* sensory event-related potentials,
* brain stem potentials,
* cognitive event-related potentials.

Performance compared favorably with that of standard wet electrodes in terms of skin preparation, no gel requirements (dry), and higher signal-to-noise ratio.<ref>{{cite thesis |bibcode=1994PhDT........82A |title=Active Micromachined Scalp Electrode Array for Eeg Signal Recording |vauthors=Alizadeh-Taheri B |degree=PHD Thesis |year=1994 |page=82}}</ref>

In 1999 Hunter Peckham and others at [[Case Western Reserve University]] used a 64-electrode EEG skullcap to return limited hand movements to a [[quadriplegic]]. As he concentrated on simple but opposite concepts like up and down. A basic pattern was identified in his beta-rhythm EEG output and used to control a switch: Above average activity was interpreted as on, below average off. The signals were also used to drive nerve controllers embedded in his hands, restoring some movement.<ref>{{Cite magazine |last=Hockenberry |first=John |date=August 2001 |title=The Next Brainiacs |url=https://www.wired.com/wired/archive/9.08/assist_pr.html |magazine=Wired |volume=9 |issue=8}}</ref>

==== SSVEP mobile EEG BCIs ====
In 2009, the NCTU Brain-Computer-Interface-headband was announced. Those researchers also engineered silicon-based [[Microelectromechanical systems|microelectro-mechanical system]] (MEMS) [[Electroencephalography#Dry EEG electrodes|dry electrodes]] designed for application to non-hairy body sites. These electrodes were secured to the headband's [[Data acquisition|DAQ]] board with snap-on electrode holders. The signal processing module measured [[Alpha wave|alpha]] activity and transferred it over [[Bluetooth]] to a phone that assessed the patients' alertness and cognitive capacity. When the subject became drowsy, the phone sent arousing feedback to the operator to rouse them.<ref>{{Citation| vauthors = Lin CT, Ko LW, Chang CJ, Wang YT, Chung CH, Yang FS, Duann JR, Jung TP, Chiou JC | display-authors = 6 |title=Wearable and Wireless Brain-Computer Interface and Its Applications |date=2009 |work=Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience| series = Lecture Notes in Computer Science | volume = 5638 |pages=741–748|publisher=Springer Berlin Heidelberg|doi=10.1007/978-3-642-02812-0_84|isbn=978-3-642-02811-3|s2cid=14515754}}</ref>

In 2011, researchers reported a cellular based BCI that could cause a phone to ring. The wearable system was composed of a four channel bio-signal acquisition/amplification [[Modular design|module]], a communication module, and a Bluetooth phone. The electrodes were placed to pick up steady state visual evoked potentials ([[Steady state visually evoked potential|SSVEPs]]).<ref name=":1">{{cite journal | vauthors = Wang YT, Wang Y, Jung TP | title = A cell-phone-based brain-computer interface for communication in daily life | journal = Journal of Neural Engineering | volume = 8 | issue = 2 | pages = 025018 | date = April 2011 | pmid = 21436517 | doi = 10.1088/1741-2560/8/2/025018 | s2cid = 10943518 | bibcode = 2011JNEng...8b5018W }}</ref> SSVEPs are electrical responses to flickering visual stimuli with repetition rates over 6&nbsp;Hz<ref name=":1" /> that are best found in the parietal and occipital scalp regions of the visual cortex.<ref>{{cite journal | vauthors = Guger C, Allison BZ, Großwindhager B, Prückl R, Hintermüller C, Kapeller C, Bruckner M, Krausz G, Edlinger G | display-authors = 6 | title = How Many People Could Use an SSVEP BCI? | journal = Frontiers in Neuroscience | volume = 6 | pages = 169 | date = 2012 | pmid = 23181009 | pmc = 3500831 | doi = 10.3389/fnins.2012.00169 | doi-access = free }}</ref><ref name=":2">{{cite book | vauthors = Lin YP, Wang Y, Jung TP | title = 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) | chapter = A mobile SSVEP-based brain-computer interface for freely moving humans: The robustness of canonical correlation analysis to motion artifacts | volume = 2013 | pages = 1350–1353 | year = 2013 | pmid = 24109946 | doi = 10.1109/EMBC.2013.6609759 | isbn = 978-1-4577-0216-7 | s2cid = 23136360 }}</ref><ref>{{cite journal | vauthors = Rashid M, Sulaiman N, Abdul Majeed AP, Musa RM, Ab Nasir AF, Bari BS, Khatun S | title = Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review | journal = Frontiers in Neurorobotics | volume = 14 | pages = 25 | date = 2020 | pmid = 32581758 | pmc = 7283463 | doi = 10.3389/fnbot.2020.00025 | doi-access = free }}</ref> It was reported that all study participants were able to initiate the phone call with minimal practice in natural environments.<ref>{{cite patent | country = US | number = 20130127708 | gdate = 23 May 2013 }}</ref>

The scientists reported that a single channel [[fast Fourier transform]] (FFT) and multiple channel system [[canonical correlation analysis]] ([[Canonical correlation|CCA]]) algorithm can support mobile BCIs.<ref name=":1" /><ref name=":3">{{cite book | vauthors = Wang YT, Wang Y, Cheng CK, Jung TP | title = 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) | chapter = Developing stimulus presentation on mobile devices for a truly portable SSVEP-based BCI | volume = 2013 | pages = 5271–5274 | year = 2013 | pmid = 24110925 | doi = 10.1109/EMBC.2013.6610738 | isbn = 978-1-4577-0216-7 | s2cid = 14324159 }}</ref> The CCA algorithm has been applied in experiments investigating BCIs with claimed high accuracy and speed.<ref>{{cite journal | vauthors = Bin G, Gao X, Yan Z, Hong B, Gao S | title = An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method | journal = Journal of Neural Engineering | volume = 6 | issue = 4 | pages = 046002 | date = August 2009 | pmid = 19494422 | doi = 10.1088/1741-2560/6/4/046002 | bibcode = 2009JNEng...6d6002B | s2cid = 32640699 }}</ref> Cellular BCI technology can reportedly be translated for other applications, such as picking up sensorimotor [[Mu wave|mu]]/[[Beta wave|beta]] rhythms to function as a motor-imagery based BCI.<ref name=":1" />

In 2013, comparative tests performed on [[Android (operating system)|Android]] cell phone, tablet, and computer based BCIs, analyzed the power [[Spectral density|spectrum density]] of resultant EEG SSVEPs. The stated goals of this study were to "increase the practicability, portability, and ubiquity of an SSVEP-based BCI, for daily use". It was reported that the stimulation frequency on all mediums was accurate, although the phone's signal was not stable. The amplitudes of the SSVEPs for the laptop and tablet were reported to be larger than those of the cell phone. These two qualitative characterizations were suggested as indicators of the feasibility of using a mobile stimulus BCI.<ref name=":3" />

One of the difficulties with EEG readings is susceptibility to motion artifacts.<ref>{{cite journal | vauthors = Symeonidou ER, Nordin AD, Hairston WD, Ferris DP | title = Effects of Cable Sway, Electrode Surface Area, and Electrode Mass on Electroencephalography Signal Quality during Motion | journal = Sensors | volume = 18 | issue = 4 | pages = 1073 | date = April 2018 | pmid = 29614020 | pmc = 5948545 | doi = 10.3390/s18041073 | doi-access = free | bibcode = 2018Senso..18.1073S }}</ref> In most research projects, the participants were asked to sit still in a laboratory setting, reducing head and eye movements as much as possible. However, since these initiatives were intended to create a mobile device for daily use,<ref name=":3" /> the technology had to be tested in motion. In 2013, researchers tested mobile EEG-based BCI technology, measuring SSVEPs from participants as they walked on a treadmill. Reported results were that as speed increased, SSVEP detectability using CCA decreased. [[Independent component analysis]] (ICA) had been shown to be efficient in separating EEG signals from noise.<ref>{{cite journal | vauthors = Wang Y, Wang R, Gao X, Hong B, Gao S | title = A practical VEP-based brain-computer interface | journal = IEEE Transactions on Neural Systems and Rehabilitation Engineering | volume = 14 | issue = 2 | pages = 234–239 | date = June 2006 | pmid = 16792302 | doi = 10.1109/TNSRE.2006.875576 }}</ref> The researchers stated that CCA data with and without ICA processing were similar. They concluded that CCA demonstrated robustness to motion artifacts.<ref name=":2" /> EEG-based BCI applications offer low spatial resolution. Possible solutions include: EEG source connectivity based on [[graph theory]], EEG pattern recognition based on Topomap and EEG-[[fMRI]] fusion.

====Prosthesis and environment control====
Non-invasive BCIs have been applied to prosthetic upper and lower extremity devices in people with paralysis. For example, Gert Pfurtscheller of [[Graz University of Technology]] and colleagues demonstrated a BCI-controlled [[functional electrical stimulation]] system to restore upper extremity movements in a person with tetraplegia due to [[spinal cord injury]].<ref>{{cite journal | vauthors = Pfurtscheller G, Müller GR, Pfurtscheller J, Gerner HJ, Rupp R | title = 'Thought'--control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia | journal = Neuroscience Letters | volume = 351 | issue = 1 | pages = 33–36 | date = November 2003 | pmid = 14550907 | doi = 10.1016/S0304-3940(03)00947-9 | s2cid = 38568963 }}</ref> Between 2012 and 2013, researchers at [[University of California, Irvine]] demonstrated for the first time that BCI technology can restore brain-controlled walking after [[spinal cord injury]]. In their [[Spinal cord injury research|study]], a person with [[paraplegia]] operated a BCI-robotic gait [[orthosis]] to regain basic ambulation.<ref name="DoWang2013">{{cite journal | vauthors = Do AH, Wang PT, King CE, Chun SN, Nenadic Z | title = Brain-computer interface controlled robotic gait orthosis | journal = Journal of Neuroengineering and Rehabilitation | volume = 10 | issue = 1 | pages = 111 | date = December 2013 | pmid = 24321081 | pmc = 3907014 | doi = 10.1186/1743-0003-10-111 | doi-access = free }}</ref><ref>[https://www.youtube.com/watch?v=HXNCwonhjG8 Subject with Paraplegia Operates BCI-controlled RoGO (4x)] at YouTube.com</ref> In 2009 independent researcher Alex Blainey used the [[Emotiv]] EPOC to control a 5 axis robot arm.<ref>[https://www.youtube.com/watch?v=4Cq35VbRpTY Alex Blainey controls a cheap consumer robot arm using the EPOC headset via a serial relay port] at YouTube.com</ref> He made several demonstrations of mind controlled wheelchairs and [[home automation]].

==== Magnetoencephalography and fMRI ====
{{Main|Magnetoencephalography|Functional magnetic resonance imaging}}

[[File:Visual stimulus reconstruction using fMRI.png|thumb|ATR Labs' reconstruction of human vision using [[functional magnetic resonance imaging|fMRI]] (top row: original image; bottom row: reconstruction from mean of combined readings)]][[Magnetoencephalography]] (MEG) and [[functional magnetic resonance imaging]] (fMRI) have both been used as non-invasive BCIs.<ref>Ranganatha Sitaram, Andrea Caria, Ralf Veit, Tilman Gaber, Giuseppina Rota, Andrea Kuebler and Niels Birbaumer(2007) "[https://archive.today/20120731202844/http://mts.hindawi.com/utils/GetFile.aspx?msid=25487&vnum=2&ftype=manuscript FMRI Brain–Computer Interface: A Tool for Neuroscientific Research and Treatment]"</ref> In a widely reported experiment, fMRI allowed two users to play [[Pong]] in real-time by altering their [[haemodynamic response]] or brain blood flow through [[biofeedback]].<ref>{{cite journal|doi=10.1038/news040823-18|title=Mental ping-pong could aid paraplegics|journal=News@nature|date=27 August 2004 | last = Peplow |first=Mark }}</ref>

fMRI measurements of haemodynamic responses in real time have also been used to control robot arms with a seven-second delay between thought and movement.<ref>{{cite web | url = http://techon.nikkeibp.co.jp/english/NEWS_EN/20060525/117493/ | title = To operate robot only with brain, ATR and Honda develop BMI base technology | work = Tech-on | date = 26 May 2006 | access-date = 22 September 2006 | archive-date = 23 June 2017 | archive-url = https://web.archive.org/web/20170623060519/http://techon.nikkeibp.co.jp/english/NEWS_EN/20060525/117493/ | url-status = dead }}</ref>

In 2008 research developed in the Advanced Telecommunications Research (ATR) [[Computational Neuroscience]] Laboratories in [[Kyoto]], Japan, allowed researchers to reconstruct images from brain signals at a [[Display resolution|resolution]] of 10x10 [[pixels]].<ref>{{cite journal | vauthors = Miyawaki Y, Uchida H, Yamashita O, Sato MA, Morito Y, Tanabe HC, Sadato N, Kamitani Y | display-authors = 6 | title = Visual image reconstruction from human brain activity using a combination of multiscale local image decoders | journal = Neuron | volume = 60 | issue = 5 | pages = 915–929 | date = December 2008 | pmid = 19081384 | doi = 10.1016/j.neuron.2008.11.004 | s2cid = 17327816 | doi-access = free }}</ref>

A 2011 study reported second-by-second reconstruction of videos watched by the study's subjects, from fMRI data.<ref>{{cite journal |vauthors=Nishimoto S, Vu AT, Naselaris T, Benjamini Y, Yu B, Gallant JL |date=October 2011 |title=Reconstructing visual experiences from brain activity evoked by natural movies |journal=Current Biology |volume=21 |issue=19 |pages=1641–1646 |doi=10.1016/j.cub.2011.08.031 |pmc=3326357 |pmid=21945275}}</ref> This was achieved by creating a statistical model relating videos to brain activity. This model was then used to look up 100 one-second video segments, in a database of 18 million seconds of random [[YouTube]] videos, matching visual patterns to brain activity recorded when subjects watched a video. These 100 one-second video extracts were then combined into a mash-up image that resembled the video.<ref>{{cite magazine | url = http://blogs.scientificamerican.com/observations/2011/09/22/breakthrough-could-enable-others-to-watch-your-dreams-and-memories-video/ | title= Breakthrough Could Enable Others to Watch Your Dreams and Memories | last = Yam |first=Philip | date = 22 September 2011 | magazine = Scientific American | access-date = 25 September 2011}}</ref><ref>{{cite web | url = https://sites.google.com/site/gallantlabucb/publications/nishimoto-et-al-2011 | title = Reconstructing visual experiences from brain activity evoked by natural movies (Project page) | publisher = The Gallant Lab at [[UC Berkeley]] | access-date = 25 September 2011 |url-status=dead |archiveurl=https://web.archive.org/web/20110925024037/https://sites.google.com/site/gallantlabucb/publications/nishimoto-et-al-2011 |archivedate=2011-09-25}}</ref><ref>{{cite web | url= http://newscenter.berkeley.edu/2011/09/22/brain-movies/| title= Scientists use brain imaging to reveal the movies in our mind |last=Anwar |first=Yasmin | date= 22 September 2011 | publisher = [[UC Berkeley]] News Center| access-date = 25 September 2011}}</ref>

====BCI control strategies in neurogaming====

=====Motor imagery=====

[[Motor imagery]] involves imagining the movement of body parts, activating the [[sensorimotor cortex]], which modulates sensorimotor oscillations in the EEG. This can be detected by the BCI and used to infer user intent. Motor imagery typically requires training to acquire acceptable control. Training sessions typically consume hours over several days. Regardless of the duration of the training session, users are unable to master the control scheme. This results in very slow pace of the gameplay.<ref name="ieeexplore.ieee.org">{{cite journal| vauthors = Marshall D, Coyle D, Wilson S, Callaghan M |title=Games, Gameplay, and BCI: The State of the Art|journal=IEEE Transactions on Computational Intelligence and AI in Games|volume=5|issue=2|page = 83|doi=10.1109/TCIAIG.2013.2263555 |year=2013|s2cid=206636315}}</ref> Machine learning methods were used to compute a subject-specific model for detecting motor imagery performance. The top performing algorithm from BCI Competition IV in 2022<ref>{{cite web|url=http://www.bbci.de/competition/iv/|title=Goals of the organizers|publisher=BBC|access-date=19 December 2022}}</ref> dataset 2 for motor imagery was the Filter Bank Common Spatial Pattern, developed by Ang et al. from [[A*STAR]], [[Singapore]].<ref>{{cite journal | vauthors = Ang KK, Chin ZY, Wang C, Guan C, Zhang H | title = Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b | journal = Frontiers in Neuroscience | volume = 6 | page = 39 | date = 1 January 2012 | pmid = 22479236 | pmc = 3314883 | doi = 10.3389/fnins.2012.00039 | doi-access = free }}</ref>

=====Bio/neurofeedback for passive BCI designs=====
Biofeedback can be used to monitor a subject's mental relaxation. In some cases, biofeedback does not match EEG, while parameters such as [[electromyography]] (EMG), [[galvanic skin response|galvanic skin resistance]] (GSR), and [[heart rate variability]] (HRV) can do so. Many biofeedback systems treat disorders such as [[Attention deficit hyperactivity disorder|attention deficit hyperactivity disorder (ADHD)]], sleep problems in children, teeth grinding, and chronic pain. EEG biofeedback systems typically monitor four brainwave bands (theta: 4–7&nbsp;Hz, alpha:8–12&nbsp;Hz, SMR: 12–15&nbsp;Hz, beta: 15–18&nbsp;Hz) and challenge the subject to control them. Passive BCI uses BCI to enrich human–machine interaction with information on the user's mental state, for example, simulations that detect when users intend to push brakes during emergency vehicle braking.<ref name=":0" /> Game developers using passive BCIs understand that through repetition of game levels the user's cognitive state adapts. During the first play of a given level, the player reacts differently than during subsequent plays: for example, the user is less surprised by an event that they expect.<ref name="ieeexplore.ieee.org"/>

=====Visual evoked potential (VEP)=====

A VEP is an electrical potential recorded after a subject is presented with a visual stimuli. The types of VEPs include SSVEPs and P300 potential.

[[Steady state visually evoked potential|Steady-state visually evoked potential]]s (SSVEPs) use potentials generated by exciting the [[retina]], using visual stimuli modulated at certain frequencies. SSVEP stimuli are often formed from alternating checkerboard patterns and at times use flashing images. The frequency of the phase reversal of the stimulus used can be distinguished by EEG; this makes detection of SSVEP stimuli relatively easy. SSVEP is used within many BCI systems. This is due to several factors. The signal elicited is measurable in as large a population as the transient VEP and blink movement. Electrocardiographic artefacts do not affect the frequencies monitored. The SSVEP signal is robust; the topographic organization of the primary visual cortex is such that a broader area obtains afferents from the visual field's central or fovial region. SSVEP comes with problems. As SSVEPs use flashing stimuli to infer user intent, the user must gaze at one of the flashing or iterating symbols in order to interact with the system. It is, therefore, likely that the symbols become irritating and uncomfortable during longer play sessions.

Another type of VEP is the [[P300 (neuroscience)|P300 potential]]. This potential is a positive peak in the EEG that occurs roughly 300 ms after the appearance of a target stimulus (a stimulus for which the user is waiting or seeking) or [[Oddball paradigm|oddball stimuli]]. P300 amplitude decreases as the target stimuli and the ignored stimuli grow more similar. P300 is thought to be related to a higher level attention process or an orienting response. Using P300 requires fewer training sessions. The first application to use it was the P300 matrix. Within this system, a subject chooses a letter from a 6 by 6 grid of letters and numbers. The rows and columns of the grid flashed sequentially and every time the selected "choice letter" was illuminated the user's P300 was (potentially) elicited. However, the communication process, at approximately 17 characters per minute, was slow. P300 offers a discrete selection rather than continuous control. The advantage of P300 within games is that the player does not have to learn how to use a new control system, requiring only short training instances to learn gameplay mechanics and the basic BCI paradigm.<ref name="ieeexplore.ieee.org"/>

==== Non-brain-based human–computer interface (physiological computing) ====
Human-computer interaction can exploit other recording modalities, such as [[electrooculography]] and eye-tracking. These modalities do not record brain activity and therefore do not qualify as BCIs.<ref>{{Cite journal |last=Fairclough |first=Stephen H. |date=January 2009 |title=Fundamentals of physiological computing |url=https://academic.oup.com/iwc/article-lookup/doi/10.1016/j.intcom.2008.10.011 |journal=Interacting with Computers |language=en |volume=21 |issue=1–2 |pages=133–145 |doi=10.1016/j.intcom.2008.10.011|s2cid=16314534 }}</ref>

=====Electrooculography (EOG)=====
In 1989, a study reported control of a mobile robot by eye movement using electrooculography signals. A mobile robot was driven to a goal point using five EOG commands, interpreted as forward, backward, left, right, and stop.<ref>{{cite book |title=Advances in Robot Design and Intelligent Control |vauthors=Bozinovski S |year=2017 |isbn=978-3-319-49057-1 |series=Advances in Intelligent Systems and Computing |volume=540 |pages=449–462 |chapter=Signal Processing Robotics Using Signals Generated by a Human Head: From Pioneering Works to EEG-Based Emulation of Digital Circuits |doi=10.1007/978-3-319-49058-8_49}}</ref>

=====Pupil-size oscillation=====
A 2016 article described a new non-EEG-based HCI that required no [[visual fixation]], or ability to move the eyes.<ref>{{cite journal |vauthors=Mathôt S, Melmi JB, van der Linden L, Van der Stigchel S |year=2016 |title=The Mind-Writing Pupil: A Human-Computer Interface Based on Decoding of Covert Attention through Pupillometry |journal=PLOS ONE |volume=11 |issue=2 |pages=e0148805 |bibcode=2016PLoSO..1148805M |doi=10.1371/journal.pone.0148805 |pmc=4743834 |pmid=26848745 |doi-access=free}}</ref> The interface is based on covert [[interest (emotion)|interest]]; directing attention to a chosen letter on a virtual keyboard, without the need to look directly at the letter. Each letter has its own (background) circle which micro-oscillates in brightness differently from the others. Letter selection is based on best fit between unintentional pupil-size oscillation and the background circle's brightness oscillation pattern. Accuracy is additionally improved by the user's mental rehearsal of the words 'bright' and 'dark' in synchrony with the brightness transitions of the letter's circle.