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A brain–computer interface (BCI), sometimes called a brain–machine interface (BMI), is a direct communication link between the

Research on BCIs began in the 1970s by Jacques Vidal at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA.^[3][4] Vidal's 1973 paper introduced the expression brain–computer interface into scientific literature.

Due to the

Studies in

The history of brain-computer interfaces (BCIs) starts with Hans Berger's discovery of the brain's electrical activity and the development of electroencephalography (EEG). In 1924 Berger was the first to record human brain activity utilizing EEG. Berger was able to identify oscillatory activity, such as the alpha wave (8–13 Hz), by analyzing EEG traces.

Berger's first recording device was rudimentary. He inserted

Berger analyzed the interrelation of alternations in his EEG wave diagrams with

Although the term had not yet been coined, one of the earliest examples of a working brain-machine interface was the piece Music for Solo Performer (1965) by American composer

Vidal coined the term "BCI" and produced the first peer-reviewed publications on this topic.[3]^[4] He is widely recognized as the inventor of BCIs.^[10][11][12] A review pointed out that Vidal's 1973 paper stated the "BCI challenge"^[13] of controlling external objects using EEG signals, and especially use of Contingent Negative Variation (CNV) potential as a challenge for BCI control. Vidal's 1977 experiment was the first application of BCI after his 1973 BCI challenge. It was a noninvasive EEG (actually Visual Evoked Potentials (VEP)) control of a cursor-like graphical object on a computer screen. The demonstration was movement in a maze.^[14]

1988 was the first demonstration of noninvasive EEG control of a physical object, a robot. The experiment demonstrated EEG control of multiple start-stop-restart cycles of movement, along an arbitrary trajectory defined by a line drawn on a floor. The line-following behavior was the default robot behavior, utilizing autonomous intelligence and an autonomous energy source.^{[15][16][17][18]}

In 1990, a report was given on a closed loop, bidirectional, adaptive BCI controlling a computer buzzer by an anticipatory brain potential, the Contingent Negative Variation (CNV) potential.^[19][20] The experiment described how an expectation state of the brain, manifested by CNV, used a feedback loop to control the S2 buzzer in the S1-S2-CNV paradigm. The resulting cognitive wave representing the expectation learning in the brain was termed Electroexpectogram (EXG). The CNV brain potential was part of Vidal's 1973 challenge.

Studies in the 2010s suggested neural stimulation's potential to restore functional connectivity and associated behaviors through modulation of molecular mechanisms.^[21][22] This opened the door for the concept that BCI technologies may be able to restore function.

Beginning in 2013, DARPA funded BCI technology through the BRAIN initiative, which supported work out of teams including University of Pittsburgh Medical Center,^[23] Paradromics,^[24] Brown,^[25] and Synchron.^[26]

Neuroprosthetics

Neuroprosthetics is an area of

The terms are sometimes used interchangeably. Neuroprosthetics and BCIs seek to achieve the same aims, such as restoring sight, hearing, movement, ability to communicate, and even

Several laboratories have managed to read signals from monkey and rat

In 2020, Elon Musk's Neuralink was successfully implanted in a pig.^[30] In 2021, Musk announced that the company had successfully enabled a monkey to play video games using Neuralink's device.^[31]

Early work

In 1969 operant conditioning studies by Fetz et.al. at the Regional Primate Research Center and Department of Physiology and Biophysics, University of Washington School of Medicine showed that monkeys could learn to control the deflection of a biofeedback arm with neural activity.^[32] Similar work in the 1970s established that monkeys could learn to control the firing rates of individual and multiple neurons in the primary motor cortex if they were rewarded accordingly.^[33]

electrodes into monkeys.^{[citation needed]}

In 1999, Yang Dan et.al. at University of California, Berkeley decoded neuronal firings to reproduce images from cats. The team used an array of electrodes embedded in the thalamus (which integrates the brain's sensory input). Researchers targeted 177 brain cells in the thalamus lateral geniculate nucleus area, which decodes signals from the retina. Neuron firings were recorded from watching eight short movies. Using mathematical filters, the researchers decoded the signals to reconstruct recognizable scenes and moving objects.^[35]

Duke University professor Miguel Nicolelis advocates using multiple electrodes spread over a greater area of the brain to obtain neuronal signals.

After initial studies in rats during the 1990s, Nicolelis and colleagues developed BCIs that decoded brain activity in

By 2000, the group succeeded in building a BCI that reproduced owl monkey movements while the monkey operated a joystick or reached for food.^[36] The BCI operated in real time and could remotely control a separate robot. But the monkeys received no feedback (open-loop BCI).

Later experiments on

In 2011 O'Doherty and colleagues showed a BCI with sensory feedback with rhesus monkeys. The monkey controlled the position of an avatar arm while receiving sensory feedback through direct intracortical stimulation (ICMS) in the arm representation area of the sensory cortex.^[39]

Donoghue, Schwartz, and Andersen

Other laboratories that have developed BCIs and algorithms that decode neuron signals include

The Carney Institute reported training rhesus monkeys to use a BCI to track visual targets on a computer screen (closed-loop BCI) with or without a joystick.^[40] The group created a BCI for three-dimensional tracking in virtual reality and reproduced BCI control in a robotic arm.^[41] The same group demonstrated that a monkey could feed itself pieces of fruit and marshmallows using a robotic arm controlled by the animal's brain signals.^[42][43][44]

Andersen's group used recordings of premovement activity from the posterior parietal cortex, including signals created when experimental animals anticipated receiving a reward.^[45]

Other research

In addition to predicting

Nicolelis and colleagues demonstrated that large neural ensembles can predict arm position. This work allowed BCIs to read arm movement intentions and translate them into actuator movements. Carmena and colleagues[37] programmed a BCI that allowed a monkey to control reaching and grasping movements by a robotic arm. Lebedev and colleagues argued that brain networks reorganize to create a new representation of the robotic appendage in addition to the representation of the animal's own limbs.^[38]

In 2019, a study reported a BCI that had the potential to help patients with speech impairment caused by neurological disorders. Their BCI used high-density

Development and implementation of a BCI system is complex and time-consuming. In response to this problem, Gerwin Schalk has been developing BCI2000, a general-purpose system for BCI research, since 2000.^[51]

A new 'wireless' approach uses

BCIs led to a deeper understanding of neural networks and the central nervous system. Research has reported that despite neuroscientists' inclination to believe that neurons have the most effect when working together, single neurons can be conditioned through the use of BCIs to fire in a pattern that allows primates to control motor outputs. BCIs led to development of the single neuron insufficiency principle that states that even with a well-tuned firing rate, single neurons can only carry limited information and therefore the highest level of accuracy is achieved by recording ensemble firings. Other principles discovered with BCIs include the neuronal multitasking principle, the neuronal mass principle, the neural degeneracy principle, and the plasticity principle.^[53]

BCIs are proposed to be applied by users without disabilities. Passive BCIs allow for assessing and interpreting changes in the user state during Human-Computer Interaction (

BCI systems can potentially be used to encode signals from the periphery. These sensory BCI devices enable real-time, behaviorally-relevant decisions based upon closed-loop neural stimulation.[55]

The BCI Award

The BCI Research Award is awarded annually in recognition of innovative research. Each year, a renowned research laboratory is asked to judge projects. The jury consists of BCI experts recruited by that laboratory. The jury selects twelve nominees, then chooses a first, second, and third-place winner, who receive awards of $3,000, $2,000, and $1,000, respectively.

Human research

Invasive BCIs

Invasive BCI requires surgery to implant electrodes under the scalp for accessing brain signals. The main advantage is to increase accuracy. Downsides include side effects from the surgery, including scar tissue that can obstruct brain signals or the body may not accept the implanted electrodes.^[56]

Vision

Invasive BCI research has targeted repairing damaged sight and providing new functionality for people with paralysis. Invasive BCIs are implanted directly into the grey matter of the brain during neurosurgery. Because they lie in the grey matter, invasive devices produce the highest quality signals of BCI devices but are prone to scar-tissue build-up, causing the signal to weaken, or disappear, as the body reacts to the foreign object.^[57]

In

In 2002, Jens Naumann, also blinded in adulthood, became the first in a series of 16 paying patients to receive Dobelle's second generation implant, one of the earliest commercial uses of BCIs. The second generation device used a more sophisticated implant enabling better mapping of phosphenes into coherent vision. Phosphenes are spread out across the visual field in what researchers call "the starry-night effect". Immediately after his implant, Jens was able to use his imperfectly restored vision to drive an automobile slowly around the parking area of the research institute.^[59] Dobelle died in 2004 before his processes and developments were documented, leaving no one to continue his work.^[60] Subsequently, Naumann and the other patients in the program began having problems with their vision, and eventually lost their "sight" again.^[61][62]

Movement

BCIs focusing on motor neuroprosthetics aim to restore movement in individuals with paralysis or provide devices to assist them, such as interfaces with computers or robot arms.

Kennedy and Bakay were first to install a human brain implant that produced signals of high enough quality to simulate movement. Their patient, Johnny Ray (1944–2002), developed '

Research teams led by the BrainGate group and another at University of Pittsburgh Medical Center, both in collaborations with the United States Department of Veterans Affairs (VA), demonstrated control of prosthetic limbs with many degrees of freedom using direct connections to arrays of neurons in the motor cortex of tetraplegia patients.^[66][67]

Communication

In May 2021, a Stanford University team reported a successful proof-of-concept test that enabled a quadraplegic participant to produce English sentences at about 86 characters per minute and 18 words per minute. The participant imagined moving his hand to write letters, and the system performed handwriting recognition on electrical signals detected in the motor cortex, utilizing

A 2021 study reported that a paralyzed patient was able to communicate 15 words per minute using a brain implant that analyzed vocal tract motor neurons.[70]^[49]

In a review article, authors wondered whether human information transfer rates can surpass that of language with BCIs. Language research has reported that information transfer rates are relatively constant across many languages. This may reflect the brain's information processing limit. Alternatively, this limit may be intrinsic to language itself, as a modality for information transfer.^[71]

In 2023 two studies used BCIs with recurrent neural network to decode speech at a record rate of 62 words per minute and 78 words per minute.^[72][73][74]

Technical challenges

There exist a number of technical challenges to recording brain activity with invasive BCIs. Advances in

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.[90]

Endovascular

A systematic review published in 2020 detailed multiple clinical and non-clinical studies investigating the feasibility of endovascular BCIs.^[91]

In 2010, researchers affiliated with University of Melbourne began developing a BCI that could be inserted via the vascular system. Australian neurologist

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.^[97] 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.^[98] This research indicates that control is rapid, requires minimal training, balancing signal fidelity and level of invasiveness.^{[note 1]}

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]}

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.^[100] 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.^[101][102]

After Vidal stated the BCI challenge, the initial reports on non-invasive approaches included control of a cursor in 2D using VEP,[107] control of a buzzer using CNV,^[108] control of a physical object, a robot, using a brain rhythm (alpha),^[109] control of a text written on a screen using P300.^[110][111]

In the early days of BCI research, another substantial barrier to using EEG was that extensive training ws 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 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.^[112]

Another research parameter is the type of

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.[121]

A 2019 study reported that the application of evolutionary algorithms could improve EEG mental state classification with a non-invasive Muse device, enabling classification of data acquired by a consumer-grade sensing device.^[122]

In a 2021 systematic review of

In the early 1990s Babak Taheri, at University of California, Davis demonstrated the first single and multichannel dry active electrode arrays.^[125] 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.^[126]

In 1999 Hunter Peckham and others at

In 2011, researchers reported a cellular based BCI that could cause phone to ring. The wearable system was composed of a four channel bio-signal acquisition/amplification module, a communication module, and a Bluetooth phone. The electrodes were placed to pick up steady state visual evoked potentials (SSVEPs).^[129] SSVEPs are electrical responses to flickering visual stimuli with repetition rates over 6 Hz^[129] that are best found in the parietal and occipital scalp regions of the visual cortex.^{[130][131][132]} It was reported that all study participants were able to initiate the phone call with minimal practice in natural environments.^[133]

The scientists reported that a single channel

In 2013, comparative tests performed on Android cell phone, tablet, and computer based BCIs, analyzed the power 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.^[134]

One of the difficulties with EEG readings is susceptibility to motion artifacts.

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

Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) have both been used as non-invasive BCIs.^[142] 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.^[143]

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.^[144]

In 2008 research developed in the Advanced Telecommunications Research (ATR)

A 2011 study reported second-by-second reconstruction of videos watched by the study's subjects, from fMRI data.[146] 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.^{[147][148][149]}

BCI control strategies in neurogaming

Motor imagery

Steady-state visually evoked potentials (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 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 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.^[150]

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.^[153]

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.^[154]

Pupil-size oscillation

A 2016 article described a new non-EEG-based HCI that required no

In the 1960s a researcher after training used EEG to create Morse code using alpha waves.^[156] 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 the first-ever direct brain-to-brain interface.^{[157][158][159]}

In 2002 Kevin Warwick had an array of 100 electrodes fired into his nervous system in order to link his nervous system to the Internet. Warwick carried out a series of experiments. Electrodes were implanted into his wife's nervous system, allowing them to conduct the first direct electronic communication experiment between the nervous systems of two humans.^{[161][162][163][164]}

Other researchers achieved brain-to-brain communication between at a distance using non-invasive technology attached to the participants' scalps. The words were encoded in binary streams by the cognitive motor input of the person sending the information. Pseudo-random bits of the information carried encoded words "hola" ("hi" in Spanish) and "ciao" ("goodbye" in Italian) and were transmitted mind-to-mind.^[165]

Cell-culture BCIs

Researchers have built devices to interface with neural cells and entire neural networks in vitro. Experiments on cultured neural tissue focused on building problem-solving networks, constructing basic computers and manipulating robotic devices. Research into techniques for stimulating and recording individual neurons grown on semiconductor chips is neuroelectronics or neurochips.^[166]

Development of the first neurochip was claimed by a Caltech team led by Jerome Pine and Michael Maher in 1997.^[167] The Caltech chip had room for 16 neurons.

In 2003 a team led by Theodore Berger, at the University of Southern California, worked on a neurochip designed to function as an artificial or prosthetic hippocampus. The neurochip was designed for rat brains. The hippocampus was chosen because it is thought to be the most structured and most studied part of the brain. Its function is to encode experiences for storage as long-term memories elsewhere in the brain.^[168]

In 2004 Thomas DeMarse at the

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.[170]^[171][172] A patent was applied for in 2012.^{[173][174][175]} Stoica's first paper on the topic appeared in 2012, after the publication of his patent application.^[176]

Ethical considerations

BCIs present significant ethical questions, including concerns about privacy, autonomy, consent, and the consequences of merging human cognition with external devices. Exploring these ethical considerations highlights the complex interplay between advancing technology and preserving fundamental human rights and values. The concerns can be broadly categorized into user-centric issues and legal and social issues.

Concerns center on the safety and long-term effects on users. These include obtaining informed consent from individuals with communication difficulties, the impact on patients' and families' quality of life, health-related side effects, misuse of therapeutic applications, safety risks, and the non-reversible nature of some BCI-induced changes. Additionally, questions arise about access to maintenance, repair, and spare parts, particularly in the event of a company's bankruptcy^[177]

The legal and social aspects of BCIs complicate mainstream adoption. Concerns include issues of accountability and responsibility, such as claims that BCI influence overrides free will and control over actions, inaccurate translation of cognitive intentions, personality changes resulting from deep-brain stimulation, and the blurring of the line between human and machine.^[178] Other concerns involve the use of BCIs in advanced interrogation techniques, unauthorized access ("brain hacking"),^[179] social stratification through selective enhancement, privacy issues related to mind-reading, tracking and "tagging" systems, and the potential for mind, movement, and emotion control.^[180] Researchers have also theorized that BCIs could exacerbate existing social inequalities.

In their current form, most BCIs are more akin to corrective therapies that engage few of such ethical issues. Bioethics is well-equipped to address the challenges posed by BCI technologies, with Clausen suggesting in 2009 that "BCIs pose ethical challenges, but these are conceptually similar to those that bioethicists have addressed for other realms of therapy."^[181] Haselager and colleagues highlighted the importance of managing expectations and value.^[182] Standard protocols can ensure ethically sound informed-consent procedures for locked-in patients.

The evolution of BCIs mirrors that of pharmaceutical science, which began as a means to address impairments and now enhances focus and reduces the need for sleep. As BCIs progress from therapies to enhancements, the BCI community is working to create consensus on ethical guidelines for research, development, and dissemination.^[183][184] Ensuring equitable access to BCIs will be crucial in preventing generational inequalities that could hinder the right to human flourishing.

Low-cost systems

Various companies are developing inexpensive BCIs for research and entertainment. Toys such as the NeuroSky and Mattel MindFlex have seen some commercial success.

• In 2006, Sony patented a neural interface system allowing radio waves to affect signals in the neural cortex.^[185]
• In 2007, NeuroSky released the first affordable consumer based EEG along with the game NeuroBoy. It was the first large scale EEG device to use dry sensor technology.^[186]
• In 2008,
  OCZ Technology developed a device for use in video games relying primarily on electromyography.^[187]
• In 2008, Final Fantasy developer Square Enix announced that it was partnering with NeuroSky to create Judecca, a game.^[188][189]
• In 2009, Mattel partnered with NeuroSky to release Mindflex, a game that used an EEG to steer a ball through an obstacle course. It was by far the best selling consumer based EEG at the time.^[188][190]
• In 2009,
  Uncle Milton Industries partnered with NeuroSky to release the Star Wars Force Trainer, a game designed to create the illusion of possessing the Force.^[188][191]
• In 2009, Emotiv released the EPOC, a 14 channel EEG device that can read 4 mental states, 13 conscious states, facial expressions, and head movements. The EPOC was the first commercial BCI to use dry sensor technology, which can be dampened with a saline solution for a better connection.^[192]
• In November 2011, Time magazine selected "necomimi" produced by Neurowear as one of the year's best inventions.^[193]
• In February 2014, They Shall Walk (a nonprofit organization fixed on constructing exoskeletons, dubbed LIFESUITs, for paraplegics and quadriplegics) began a partnership with James W. Shakarji on the development of a wireless BCI.^[194]
• In 2016, a group of hobbyists developed an open-source BCI board that sends neural signals to the audio jack of a smartphone, dropping the cost of entry-level BCI to £20.^[195] Basic diagnostic software is available for Android devices, as well as a text entry app for Unity.^[196]
• In 2020, NextMind released a dev kit including an EEG headset with dry electrodes at $399.^[197][198] The device can run various visual-BCI demonstration applications or developers can create their own. It was later acquired by Snap Inc. in 2022.^[199]

Future directions

A consortium of 12 European partners completed a roadmap to support the European Commission in their funding decisions for the

Other recent publications too have explored future BCI directions for new groups of disabled users.[10]^[202]

Disorders of consciousness (DOC)

Some people have a

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 coney yes or no, for example by focusing their attention on stimuli on the right vs. left wrist. This focused attention produces reliable changes in EEG patterns that can help determine whether the patient is able to communicate.^{[205][206][207]}

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.^{[208][209][210][211]} Several groups have explored systems and methods for motor recovery that include BCIs.^{[212][213][214][215]} 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.^{[216][217][218]} Imaging studies combined with EEG-based BCI systems hold promise for investigating neuroplasticity during motor recovery post-stroke.^[218] 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.^[219] 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.^{[220][221][222]}

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.^[223] 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]}

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.^[224]

Flexible devices

Flexible neural interfaces may minimize brain tissue trauma related to mechanical mismatch between electrode and tissue.[227]

Neural dust

Neural dust is millimeter-sized devices operated as wirelessly powered nerve sensors that were proposed in a 2011 paper from the University of California, Berkeley Wireless Research Center.^[228][229] In one model, local field potentials could be distinguished from action potential "spikes", which would offer greatly diversified data vs conventional techniques.^[228]

See also

Notes

References

Further reading

• Brouse, Andrew. "A Young Person's Guide to Brainwave Music: Forty years of audio from the human EEG". eContact! 14.2 – Biotechnological Performance Practice / Pratiques de performance biotechnologique (July 2012). Montréal: CEC.
• Gupta, Cota Navin and Ramaswamy Palanappian. "Using High-Frequency Electroencephalogram in Visual and Auditory-Based Brain-Computer Interface Designs". eContact! 14.2 – Biotechnological Performance Practice / Pratiques de performance biotechnologique (July 2012). Montréal: CEC.
• Ouzounian, Gascia. "The Biomuse Trio in Conversation: An Interview with R. Benjamin Knapp and Eric Lyon". eContact! 14.2 – Biotechnological Performance Practice / Pratiques de performance biotechnologique (July 2012). Montréal: CEC.
• 20 Years of Brain-Machine Interface Research. Nicolelis Lab Series. Vol. 1. 2019. p. 452.
• 20 Years of Brain-Machine Interface Research. Nicolelis Lab Series. Vol. 2. 2019. p. 436.

External links

Wikimedia Commons has media related to Brain-computer interfaces.

Scholia has a topic profile for SalFromOuterSpace/sandbox.

• Wandelt, Sarah K.; Bjånes, David A.; Pejsa, Kelsie; Lee, Brian; Liu, Charles; Andersen, Richard A. (2024-05-13). "Representation of internal speech by single neurons in human supramarginal gyrus". Nature Human Behaviour: 1–14.
  ISSN 2397-3374
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• The Unlock Project
• CIA - Wireless BCI