Pain negatively affects the health and welfare of animals. "Pain" is defined by the International Association for the Study of Pain as "an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage."
Only the animal experiencing the pain can know the pain's quality and
intensity, and the degree of suffering. It is harder, if even possible,
for an observer to know whether an emotional experience has occurred,
especially if the sufferer cannot communicate. Therefore, this concept is often excluded in definitions of pain in animals, such as that provided by Zimmerman:
"an aversive sensory experience caused by actual or potential injury
that elicits protective motor and vegetative reactions, results in
learned avoidance and may modify species-specific behaviour, including
social behaviour."
Nonhuman animals cannot report their feelings to language-using humans
in the same manner as human communication, but observation of their
behaviour provides a reasonable indication as to the extent of their
pain. Just as with doctors and medics who sometimes share no common
language with their patients, the indicators of pain can still be
understood.
According to the U.S. National Research Council Committee on
Recognition and Alleviation of Pain in Laboratory Animals, pain is
experienced by many animal species, including mammals and possibly all vertebrates. Overview of anatomy of the nervous system across animal kingdom indicates that, not only vertebrates, but also most invertebrates have the capacity to feel pain.
Pain perception in animals
Although there are numerous definitions of pain, almost all involve two key components. First, nociception is required. This is the ability to detect noxious stimuli which evoke a reflex
response that rapidly moves the entire animal, or the affected part of
its body, away from the source of the stimulus. The concept of
nociception does not imply any adverse, subjective "feeling" – it is a
reflex action. An example in humans would be the rapid withdrawal of a
finger that has touched something hot – the withdrawal occurs before any
sensation of pain is actually experienced.
The second component is the experience of "pain" itself, or suffering
– the internal, emotional interpretation of the nociceptive experience.
Again in humans, this is when the withdrawn finger begins to hurt,
moments after the withdrawal. Pain is therefore a private, emotional
experience. Pain cannot be directly measured in other animals, including
other humans; responses to putatively painful stimuli can be measured,
but not the experience itself. To address this problem when assessing
the capacity of other species to experience pain, argument-by-analogy is
used. This is based on the principle that if an animal responds to a
stimulus in a similar way to ourselves, it is likely to have had an
analogous experience.
The ability to experience pain in an animal, or another human for
that matter, cannot be determined directly but it may be inferred
through analogous physiological and behavioral reactions.
Although many animals share similar mechanisms of pain detection to
those of humans, have similar areas of the brain involved in processing
pain, and show similar pain behaviours, it is notoriously difficult to
assess how animals actually experience pain.
Nociception
Nociceptive
nerves, which preferentially detect (potential) injury-causing stimuli,
have been identified in a variety of animals, including invertebrates.
The medicinal leech, Hirudo medicinalis, and sea slug are classic model systems for studying nociception. Many other vertebrate and invertebrate animals also show nociceptive reflex responses similar to our own.
Reflex response to painful stimuli
Reflex
arc of a dog when its paw is stuck with a pin. The spinal cord responds
to signals from receptors in the paw, producing a reflex withdrawal of
the paw. This localized response does not involve brain processes that
might mediate a consciousness of pain, though these might also occur.
Nociception usually involves the transmission of a signal along nerve fibers
from the site of a noxious stimulus at the periphery to the spinal
cord. Although this signal is also transmitted on to the brain, a reflex response, such as flinching or withdrawal of a limb, is produced by return signals originating in the spinal cord. Thus, both physiological
and behavioral responses to nociception can be detected, and no
reference need be made to a conscious experience of pain. Based on such
criteria, nociception has been observed in all major animal taxa.
Awareness of pain
Nerve
impulses from nociceptors may reach the brain, where information about
the stimulus (e.g. quality, location, and intensity), and effect
(unpleasantness) are registered. Though the brain activity involved has
been studied, the brain processes underlying conscious awareness are not
well known.
Behavioral and physiological indicators
Many
animals also exhibit more complex behavioural and physiological changes
indicative of the ability to experience pain: they eat less food, their
normal behaviour is disrupted, their social behaviour is suppressed,
they may adopt unusual behaviour patterns, they may emit characteristic
distress calls, experience respiratory and cardiovascular changes, as
well as inflammation and release of stress hormones.
Some criteria that may indicate the potential of another species to feel pain include:
The adaptive value
of nociception is obvious; an organism detecting a noxious stimulus
immediately withdraws the limb, appendage or entire body from the
noxious stimulus and thereby avoids further (potential) injury. However,
a characteristic of pain (in mammals at least) is that pain can result
in hyperalgesia (a heightened sensitivity to noxious stimuli) and allodynia
(a heightened sensitivity to non-noxious stimuli). When this heightened
sensitisation occurs, the adaptive value is less clear. First, the pain
arising from the heightened sensitisation can be disproportionate to
the actual tissue damage caused. Second, the heightened sensitisation
may also become chronic, persisting well beyond the tissues healing.
This can mean that rather than the actual tissue damage causing pain, it
is the pain due to the heightened sensitisation that becomes the
concern. This means the sensitisation process is sometimes termed maladaptive.
It is often suggested hyperalgesia and allodynia assist organisms to
protect themselves during healing, but experimental evidence to support
this has been lacking.
In 2014, the adaptive value of sensitisation due to injury was tested using the predatory interactions between longfin inshore squid (Doryteuthis pealeii) and black sea bass (Centropristis striata)
which are natural predators of this squid. If injured squid are
targeted by a bass, they began their defensive behaviours sooner
(indicated by greater alert distances and longer flight initiation
distances) than uninjured squid. If anaesthetic (1% ethanol and MgCl2)
is administered prior to the injury, this prevents the sensitisation
and blocks the behavioural effect. The authors claim this study is the
first experimental evidence to support the argument that nociceptive
sensitisation is actually an adaptive response to injuries.
Argument-by-analogy
To assess the capacity of other species to consciously suffer pain we resort to argument-by-analogy.
That is, if an animal responds to a stimulus the way a human does, it
is likely to have had an analogous experience. If we stick a pin in a
chimpanzee's finger and she rapidly withdraws her hand, we use
argument-by-analogy and infer that like us, she felt pain. It might be
argued that consistency requires us to infer, also, that a cockroach
experiences conscious pain when it writhes after being stuck with a pin.
The usual counter-argument is that although the physiology of
consciousness is not understood, it clearly involves complex brain
processes not present in relatively simple organisms. Other analogies have been pointed out. For example, when given a choice of foods, rats and chickens
with clinical symptoms of pain will consume more of an
analgesic-containing food than animals not in pain. Additionally, the
consumption of the analgesic carprofen in lame chickens was positively correlated to the severity of lameness, and consumption resulted in an improved gait. Such anthropomorphic
arguments face the criticism that physical reactions indicating pain
may be neither the cause nor result of conscious states, and the
approach is subject to criticism of anthropomorphic interpretation. For
example, a single-celled organism such as an amoeba may writhe after
being exposed to noxious stimuli despite the absence of nociception.
The idea that animals might not experience pain or suffering as humans do traces back at least to the 17th-century French philosopher, René Descartes, who argued that animals lack consciousness.Researchers remained unsure into the 1980s as to whether animals
experience pain, and veterinarians trained in the U.S. before 1989 were
simply taught to ignore animal pain. In his interactions with scientists and other veterinarians, Bernard Rollin
was regularly asked to "prove" that animals are conscious, and to
provide "scientifically acceptable" grounds for claiming that they feel
pain. Some authors say that the view that animals feel pain differently is now a minority view.
Academic reviews of the topic are more equivocal, noting that, although
it is likely that some animals have at least simple conscious thoughts
and feelings, some authors continue to question how reliably animal mental states can be determined.
A typical human cutaneous nerve contains 83% C type trauma receptors (the type responsible for transmitting signals described by humans as excruciating pain); the same nerves in humans with congenital insensitivity to pain have only 24-28% C type receptors. The rainbow trout has about 5% C type fibres, while sharks and rays have 0%.
Nevertheless, fish have been shown to have sensory neurons that are
sensitive to damaging stimuli and are physiologically identical to human
nociceptors.
Behavioural and physiological responses to a painful event appear
comparable to those seen in amphibians, birds, and mammals, and
administration of an analgesic drug reduces these responses in fish.
Animal welfare advocates have raised concerns about the possible
suffering of fish caused by angling. Some countries, e.g. Germany, have
banned specific types of fishing, and the British RSPCA now formally prosecutes individuals who are cruel to fish.
Though it has been argued that most invertebrates do not feel pain, there is some evidence that invertebrates, especially the decapod crustaceans (e.g. crabs and lobsters) and cephalopods (e.g. octopuses), exhibit behavioural and physiological reactions indicating they may have the capacity for this experience.
Nociceptors have been found in nematodes, annelids and mollusks. Insects also usually possess nociceptors. In vertebrates, endogenous opioids
are neurochemicals that moderate pain by interacting with opiate
receptors. Opioid peptides and opiate receptors occur naturally in
nematodes, mollusks, insects and crustaceans.
The presence of opioids in crustaceans has been interpreted as an
indication that lobsters may be able to experience pain, although it has
been claimed "at present no certain conclusion can be drawn".
One suggested reason for rejecting a pain experience in
invertebrates is that invertebrate brains are too small. However, brain
size does not necessarily equate to complexity of function. Moreover, weight for body-weight, the cephalopod
brain is in the same size bracket as the vertebrate brain, smaller than
that of birds and mammals, but as big as or bigger than most fish
brains.
Remarkably, as demonstrated by cognitive tests, intelligence of
cephalopods is comparable to that of five-year-old human children.
Since September 2010, all cephalopods being used for scientific
purposes in the EU are protected by EU Directive 2010/63/EU which states
"...there is scientific evidence of their [cephalopods] ability to
experience pain, suffering, distress and lasting harm. In the UK, animal protection legislation
means that cephalopods used for scientific purposes must be killed
humanely, according to prescribed methods (known as "Schedule 1 methods
of euthanasia") known to minimise suffering.
In animal farming
Over 80 billions of land animals are slaughtered for meat every year.
In 2023, it is estimated that 74% of all land livestock are factory farmed. In the United States, 99% of all livestock was estimated in 2017 to be factory farmed. Factory farming, or intensive animal farming, is characterized by densely confined animals and comes with a range of issues, including:
Confinement methods – Many animals, such as egg-laying hens, are
kept in cages with limited space to move. Similarly, pregnant pigs are
often kept in gestation crates, which are so small that the animals cannot turn around.
Aggressiveness – In densely confined environments without
intellectual stimulation, animals tend to become aggressive, sometimes
also engaging in cannibalism.
Mutilations – These procedures are often intended to reduce
aggression in these environments and are typically performed without
anesthetic. Examples include trimming the beaks of chickens, and clipping the teeth and tails of piglets.
Piglets are also frequently castrated to avoid a bad smell that can
sometimes develop in the meat. Routine tail clipping is considered a
traumatic practice for pigs and is banned in Europe, but the ban is
often ignored in practice.
Genetic selection – Farmed animals are typically genetically
selected to increase productivity. For instance, chickens often struggle
to stand due to their unnatural weight, which can also lead to heart
and lung problems.
Diseases – The lack of genetic diversity and the density of animals
in confinement can lead to the spread of diseases, some of which can
also be transmitted to humans.
Artificial insemination – Animals are frequently impregnated through artificial insemination, a process carried out by humans.
Early separations from mothers
Stress
Despite their vast numbers, factory farmed animals are relatively
ignored. Species that appear more different from humans, such as fish or
insects, are often particularly overlooked. One proposed solution to reduce farmed animal suffering is to develop plant-based and cultured alternatives to animal products.
Dolorimetry (dolor:
Latin: pain, grief) is the measurement of the pain response in animals,
including humans. It is practiced occasionally in medicine, as a
diagnostic tool, and is regularly used in research into the basic
science of pain, and in testing the efficacy of analgesics.
The intense sociality of humans and the readiness with which they perceive, and identify
with, manifestations of physical pain in others have made the study of
pain notoriously difficult to quantify. Indeed, many investigators of
animal pain shy away from use of the word "pain" in published research.
They consider the term to be unscientific and grounded in human emotion,
preferring others such as "stress" or "avoidance". As the subjective
experience of animals is very resistant to rational assessment, the
subjective difference between their painless reflex responses to noxious
stimuli (nociception) and pain as humans understand it has been nearly impossible to determine conclusively.
For this reason essentially all scientific research into the
nature of animal pain has depended upon so-called pain proxies. These
include obvious behavioral changes—shying
away, stamping, vocalization, ear cues etc.— as well as subtler
changes, as when injured chickens or rats choose feed that has been
laced with an analgesic over feed that has not. Most prized by
scientists are the quantifiable physiological changes such as elevated heart rate or stress hormoneserum
concentrations. These physiological proxies are valued because their
assessments are carried out by machines and do not rely on humans to
determine the magnitude of the variable under study. This is seldom the
case for behavioral pain proxies, which are most often scored by a
researcher on some numerical scale ranging from "no response" to
"intense response".
Dolormetric methods in animals
Nonhuman animal pain measurement techniques include the paw pressure test, tail flick test, hot plate test and grimace scales.
Grimace scales are used to assess post-operative and disease pain in
mammals. Scales have been developed for ten mammalian species such as
mice, rats, and rabbits. Dale Langford established and published the Mouse Grimace Scale in 2010, with Susana Sotocinal inventing the Rat Grimace Scale a year later in 2011.
Using video stills from recorders, researchers can track changes in an
animal's positioning of ears and whiskers, orbital tightening, and
bulging or flattening of the nose area, and match these images against
the images in the grimace scale.
Laboratory researcher and veterinarians may use the grimace scales to
evaluate when to administer analgesia to an animal or whether severity
of pain warrants a humane endpoint (euthanasia) for the animal in a study.
Animals are kept in laboratories for a wide range of reasons, some of
which may involve pain, suffering or distress, whilst others (e.g. many
of those involved in breeding) will not. The extent to which animal testing causes pain and suffering in laboratory animals is the subject of much debate. Marian Stamp Dawkins
defines "suffering" in laboratory animals as the experience of one of
"a wide range of extremely unpleasant subjective (mental) states." The U.S. National Research Council has published guidelines on the care and use of laboratory animals, as well as a report on recognizing and alleviating pain in vertebrates. The United States Department of Agriculture
defines a "painful procedure" in an animal study as one that would
"reasonably be expected to cause more than slight or momentary pain or
distress in a human being to which that procedure was applied." Some critics argue that, paradoxically, researchers raised in the era of increased awareness of animal welfare may be inclined to deny that animals are in pain simply because they do not want to see themselves as people who inflict it. PETA however argues that there is no doubt about animals in laboratories being inflicted with pain. In the UK, animal research likely to cause "pain, suffering, distress or lasting harm" is regulated by the Animals (Scientific Procedures) Act 1986 and research with the potential to cause pain is regulated by the Animal Welfare Act of 1966 in the US.
In the U.S., researchers are not required to provide laboratory
animals with pain relief if the administration of such drugs would
interfere with their experiment. Laboratory animal veterinarian Larry
Carbone writes, "Without question, present public policy allows humans
to cause laboratory animals unalleviated pain. The AWA, the Guide for the Care and Use of Laboratory Animals,
and current Public Health Service policy all allow for the conduct of
what are often called 'Category E' studies – experiments in which
animals are expected to undergo significant pain or distress that will
be left untreated because treatments for pain would be expected to
interfere with the experiment."
Severity scales
Eleven
countries have national classification systems of pain and suffering
experienced by animals used in research: Australia, Canada, Finland,
Germany, The Republic of Ireland, The Netherlands, New Zealand, Poland,
Sweden, Switzerland, and the UK. The US also has a mandated national
scientific animal-use classification system, but it is markedly
different from other countries in that it reports on whether
pain-relieving drugs were required and/or used.
The first severity scales were implemented in 1986 by Finland and the
UK. The number of severity categories ranges between 3 (Sweden and
Finland) and 9 (Australia). In the UK, research projects are classified
as "mild", "moderate", and "substantial" in terms of the suffering the
researchers conducting the study say they may cause; a fourth category
of "unclassified" means the animal was anesthetized and killed without
recovering consciousness. It should be remembered that in the UK system,
many research projects (e.g. transgenic breeding, feeding distasteful
food) will require a license under the Animals (Scientific Procedures) Act 1986,
but may cause little or no pain or suffering. In December 2001, 39
percent (1,296) of project licenses in force were classified as "mild",
55 percent (1,811) as "moderate", two percent (63) as "substantial", and
4 percent (139) as "unclassified".
In 2009, of the project licenses issued, 35 percent (187) were
classified as "mild", 61 percent (330) as "moderate", 2 percent (13) as
"severe" and 2 percent (11) as unclassified.
In the US, the Guide for the Care and Use of Laboratory Animals
defines the parameters for animal testing regulations. It states, "The
ability to experience and respond to pain is widespread in the animal
kingdom...Pain is a stressor and, if not relieved, can lead to
unacceptable levels of stress and distress in animals." The Guide
states that the ability to recognize the symptoms of pain in different
species is essential for the people caring for and using animals.
Accordingly, all issues of animal pain and distress, and their potential
treatment with analgesia and anesthesia, are required regulatory issues
for animal protocol approval.
A brain–computer interface (BCI), sometimes called a brain–machine interface (BMI), is a direct communication link between the brain's
electrical activity and an external device, most commonly a computer or
robotic limb. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. They are often conceptualized as a human–machine interface that skips the intermediary of moving body parts (e.g. hands or feet). BCI implementations range from non-invasive (EEG, MEG, MRI) and partially invasive (ECoG and endovascular) to invasive (microelectrode array), based on how physically close electrodes are to brain tissue.
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 the Defense Advanced Research Projects Agency (DARPA). Vidal's 1973 paper introduced the expression brain–computer interface into scientific literature.
Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices were implanted in humans in the mid-1990s.
History
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 silver
wires under the scalps of his patients. These were later replaced by
silver foils attached to the patient's head by rubber bandages. Berger
connected these sensors to a Lippmann capillary electrometer, with disappointing results. However, more sophisticated measuring devices, such as the Siemens double-coil recording galvanometer, which displayed voltages as small as 10−4 volt, led to success.
Berger analyzed the interrelation of alternations in his EEG wave diagrams with brain diseases. EEGs permitted completely new possibilities for brain research.
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 Alvin Lucier. The piece makes use of EEG and analog signal processing
hardware (filters, amplifiers, and a mixing board) to stimulate
acoustic percussion instruments. Performing the piece requires producing
alpha waves and thereby "playing" the various instruments via loudspeakers that are placed near or directly on the instruments.
Jacques Vidal coined the term "BCI" and produced the first peer-reviewed publications on this topic. He is widely recognized as the inventor of BCIs. A review pointed out that Vidal's 1973 paper stated the "BCI challenge" 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.
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.
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.
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. 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, Paradromics, Brown, and Synchron.
Neuroprosthetics is an area of neuroscience
concerned with neural prostheses, that is, using artificial devices to
replace the function of impaired nervous systems and brain-related
problems, or of sensory or other organs (bladder, diaphragm, etc.). As
of December 2010, cochlear implants had been implanted as neuroprosthetic devices in some 736,900 people worldwide. Other neuroprosthetic devices aim to restore vision, including retinal implants. The first neuroprosthetic device, however, was the pacemaker.
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 cognitive function. Both use similar experimental methods and surgical techniques.
Several laboratories have managed to read signals from monkey and rat cerebral cortices to operate BCIs to produce movement. Monkeys have moved computer cursors
and commanded robotic arms to perform simple tasks simply by thinking
about the task and seeing the results, without motor output. In May 2008 photographs that showed a monkey at the University of Pittsburgh Medical Center operating a robotic arm by thinking were published in multiple studies. Sheep have also been used to evaluate BCI technology including Synchron's Stentrode.
In 2020, Elon Musk's Neuralink was successfully implanted in a pig. In 2021, Musk announced that the company had successfully enabled a monkey to play video games using Neuralink's device.
Early work
Monkey operating a robotic arm with brain–computer interfacing (Schwartz lab, University of Pittsburgh)
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.
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.
Algorithms to reconstruct movements from motor cortexneurons, which control movement, date back to the 1970s. In the 1980s, Georgopoulos at Johns Hopkins University found a mathematical relationship between the electrical responses of single motor cortex neurons in rhesus macaque monkeys
and the direction in which they moved their arms. He also found that
dispersed groups of neurons, in different areas of the monkey's brains,
collectively controlled motor commands. He was able to record the
firings of neurons in only one area at a time, due to equipment
limitations.
Several groups have been able to capture complex brain motor cortex signals by recording from neural ensembles (groups of neurons) and using these to control external devices.
Research
Kennedy and Yang Dan
Phillip Kennedy (Neural Signals founder (1987) and colleagues built the
first intracortical brain–computer interface by implanting
neurotrophic-cone electrodes into monkeys.
Yang Dan and colleagues' recordings of cat vision using a BCI implanted in the lateral geniculate nucleus (top row: original image; bottom row: recording)
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.
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 owl monkeys
and used the devices to reproduce monkey movements in robotic arms.
Monkeys' advanced reaching and grasping abilities and hand manipulation
skills, made them good test subjects.
By 2000, the group succeeded in building a BCI that reproduced owl monkey movements while the monkey operated a joystick or reached for food. The BCI operated in real time and could remotely control a separate robot. But the monkeys received no feedback (open-loop BCI).
Diagram of the BCI developed by Miguel Nicolelis and colleagues for use on rhesus monkeys
Later experiments on rhesus monkeys included feedback
and reproduced monkey reaching and grasping movements in a robot arm.
Their deeply cleft and furrowed brains made them better models for human
neurophysiology
than owl monkeys. The monkeys were trained to reach and grasp objects
on a computer screen by manipulating a joystick while corresponding
movements by a robot arm were hidden. The monkeys were later shown the robot and learned to control it by
viewing its movements. The BCI used velocity predictions to control
reaching movements and simultaneously predicted gripping force.
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.
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. The group created a BCI for three-dimensional tracking in virtual reality and reproduced BCI control in a robotic arm.
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.
In addition to predicting kinematic and kinetic parameters of limb movements, BCIs that predict electromyographic or electrical activity of the muscles of primates are in process. Such BCIs could restore mobility in paralyzed limbs by electrically stimulating muscles.
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
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.
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 electrocorticography to tap neural activity from a patient's brain and used deep learning to synthesize speech. In 2021, those researchers reported the potential of a BCI to decode words and sentences in an anarthric patient who had been unable to speak for over 15 years.
The biggest impediment to BCI technology is the lack of a sensor
modality that provides safe, accurate and robust access to brain
signals. The use of a better sensor expands the range of communication
functions that can be provided using a BCI.
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.
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.
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 (HCI). In a secondary, implicit control loop, the system adapts to its user, improving its usability.
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.
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 potentially rejecting the
implanted electrodes.
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.
In vision science, direct brain implants have been used to treat non-congenital (acquired) blindness. One of the first scientists to produce a working brain interface to restore sight was private researcher William Dobelle.
Dobelle's first prototype was implanted into "Jerry", a man blinded in
adulthood, in 1978. A single-array BCI containing 68 electrodes was
implanted onto Jerry's visual cortex and succeeded in producing phosphenes,
the sensation of seeing light. The system included cameras mounted on
glasses to send signals to the implant. Initially, the implant allowed
Jerry to see shades of grey in a limited field of vision at a low
frame-rate. This also required him to be hooked up to a mainframe computer,
but shrinking electronics and faster computers made his artificial eye
more portable and now enable him to perform simple tasks unassisted.
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. Dobelle died in 2004 before his processes and developments were documented, leaving no one to continue his work.
Subsequently, Naumann and the other patients in the program began
having problems with their vision, and eventually lost their "sight"
again.
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 'locked-in syndrome' after a brain-stem stroke
in 1997. Ray's implant was installed in 1998 and he lived long enough
to start working with the implant, eventually learning to control a
computer cursor; he died in 2002 of a brain aneurysm.
TetraplegicMatt Nagle became the first person to control an artificial hand using a BCI in 2005 as part of the first nine-month human trial of Cyberkinetics's BrainGate chip-implant. Implanted in Nagle's right precentral gyrus
(area of the motor cortex for arm movement), the 96-electrode implant
allowed Nagle to control a robotic arm by thinking about moving his hand
as well as a computer cursor, lights and TV. One year later, Jonathan Wolpaw received the Altran Foundation for Innovation
prize for developing a Brain Computer Interface with electrodes located
on the surface of the skull, instead of directly in the brain.
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.
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 Hidden Markov models and recurrent neural networks.
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.
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.
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.
Technical challenges
There exist a number of technical challenges to recording brain activity with invasive BCIs. Advances in CMOS
technology are pushing and enabling integrated, invasive BCI designs
with smaller size, lower power requirements, and higher signal
acquisition capabilities. Invasive BCIs involve electrodes that penetrate brain tissue in an attempt to record action potential
signals (also known as spikes) from individual, or small groups of,
neurons near the electrode. The interface between a recording electrode
and the electrolytic solution surrounding neurons has been modelled
using the Hodgkin-Huxley model.
Electronic limitations to invasive BCIs have been an active area of research in recent decades. While intracellular recordings
of neurons reveal action potential voltages on the scale of hundreds of
millivolts, chronic invasive BCIs rely on recording extracellular
voltages which typically are three orders of magnitude smaller, existing
at hundreds of microvolts.
Further adding to the challenge of detecting signals on the scale of
microvolts is the fact that the electrode-tissue interface has a high capacitance
at small voltages. Due to the nature of these small signals, for BCI
systems that incorporate functionality onto an integrated circuit, each
electrode requires its own amplifier and ADC, which convert analog extracellular voltages into digital signals.
Because a typical neuron action potential lasts for one millisecond,
BCIs measuring spikes must have sampling rates ranging from 300 Hz to
5 kHz. Yet another concern is that invasive BCIs must be low-power, so
as to dissipate less heat to surrounding tissue; at the most basic level
more power is traditionally needed to optimize signal-to-noise ratio. Optimal battery design is an active area of research in BCIs.
Illustration
of invasive and partially invasive BCIs: electrocorticography (ECoG),
endovascular, and intracortical microelectrode.
Challenges existing in the area of material science
are central to the design of invasive BCIs. Variations in signal
quality over time have been commonly observed with implantable
microelectrodes.
Optimal material and mechanical characteristics for long term signal
stability in invasive BCIs has been an active area of research. It has been proposed that the formation of glial scarring,
secondary to damage at the electrode-tissue interface, is likely
responsible for electrode failure and reduced recording performance. Research has suggested that blood-brain barrier
leakage, either at the time of insertion or over time, may be
responsible for the inflammatory and glial reaction to chronic
microelectrodes implanted in the brain. As a result, flexible and tissue-like designs have been researched and developed to minimize foreign-body reaction by means of matching the Young's modulus of the electrode closer to that of brain tissue.
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.
Endovascular
A
systematic review published in 2020 detailed multiple clinical and
non-clinical studies investigating the feasibility of endovascular BCIs.
In 2010, researchers affiliated with University of Melbourne
began developing a BCI that could be inserted via the vascular system.
Australian neurologist Thomas Oxley conceived the idea for this BCI, called Stentrode, earning funding from DARPA. Preclinical studies evaluated the technology in sheep.
Stentrode is a monolithic 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.
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.
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 clotting and venous thrombosis exist.
Human trials with Stentrode were underway as of 2021. 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,
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.
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. 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. This research indicates that control is rapid, requires minimal training, balancing signal fidelity and level of invasiveness.
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.
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.
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.
Edward Chang and Joseph Makin from 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. 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.
After Vidal stated the BCI challenge, the initial reports on
non-invasive approaches included control of a cursor in 2D using VEP, control of a buzzer using CNV, control of a physical object, a robot, using a brain rhythm (alpha), control of a text written on a screen using P300.
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 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.
Another research parameter is the type of 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 and beta rhythms.
A further parameter is the method of feedback used as shown in studies of P300 signals. Patterns of P300 waves are generated involuntarily (stimulus-feedback) when people see something they recognize and may allow BCIs to decode categories of thoughts without training.
A 2005 study reported EEG emulation of digital control circuits, using a CNV flip-flop. A 2009 study reported noninvasive EEG control of a robotic arm using a CNV flip-flop. A 2011 study reported control of two robotic arms solving Tower of Hanoi task with three disks using a CNV flip-flop. A 2015 study described EEG-emulation of a Schmitt trigger, flip-flop, demultiplexer, and modem.
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.
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. In June 2013 they announced a technique to guide a remote-control helicopter through an obstacle course. 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.
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.
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.
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.
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.
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. 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.
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.
SSVEP mobile EEG BCIs
In 2009, the NCTU Brain-Computer-Interface-headband was announced. Those researchers also engineered silicon-based microelectro-mechanical system (MEMS) dry electrodes designed for application to non-hairy body sites. These electrodes were secured to the headband's DAQ board with snap-on electrode holders. The signal processing module measured 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.
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 module, a communication module, and a Bluetooth phone. The electrodes were placed to pick up steady state visual evoked potentials (SSVEPs). SSVEPs are electrical responses to flickering visual stimuli with repetition rates over 6 Hz that are best found in the parietal and occipital scalp regions of the visual cortex. It was reported that all study participants were able to initiate the phone call with minimal practice in natural environments.
The scientists reported that a single channel fast Fourier transform (FFT) and multiple channel system canonical correlation analysis (CCA) algorithm can support mobile BCIs. The CCA algorithm has been applied in experiments investigating BCIs with claimed high accuracy and speed. Cellular BCI technology can reportedly be translated for other applications, such as picking up sensorimotor mu/beta rhythms to function as a motor-imagery based BCI.
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.
One of the difficulties with EEG readings is susceptibility to motion artifacts.
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,
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.
The researchers stated that CCA data with and without ICA processing
were similar. They concluded that CCA demonstrated robustness to motion
artifacts. 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. 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 study, a person with paraplegia operated a BCI-robotic gait orthosis to regain basic ambulation. In 2009 independent researcher Alex Blainey used the Emotiv EPOC to control a 5 axis robot arm. He made several demonstrations of mind controlled wheelchairs and home automation.
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.
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 resolution of 10x10 pixels.
A 2011 study reported second-by-second reconstruction of videos watched by the study's subjects, from fMRI data.
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.
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.
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 dataset 2 for motor imagery was the Filter Bank Common Spatial Pattern, developed by Ang et al. from A*STAR, Singapore.
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 resistance (GSR), and heart rate variability (HRV) can do so. Many biofeedback systems treat disorders such as 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 Hz, alpha:8–12 Hz, SMR: 12–15 Hz, beta: 15–18 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.
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.
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 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.
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.
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.
Pupil-size oscillation
A 2016 article described a new non-EEG-based HCI that required no visual fixation, or ability to move the eyes. The interface is based on covert 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.
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.
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.
Other researchers achieved brain-to-brain communication between
participants 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.
The world's first neurochip, developed by Caltech researchers Jerome Pine and Michael Maher
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.
Development of the first neurochip was claimed by a Caltech team led by Jerome Pine and Michael Maher in 1997. 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.
In 2004 Thomas DeMarse at the University of Florida used a culture of 25,000 neurons taken from a rat's brain to fly a F-22 fighter jet aircraft simulator. After collection, the cortical neurons were cultured in a petri dish
and reconnected themselves to form a living neural network. The cells
were arranged over a grid of 60 electrodes and used to control the pitch and yaw
functions of the simulator. The study's focus was on understanding how
the human brain performs and learns computational tasks at a cellular
level.
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. A patent was applied for in 2012. Stoica's first paper on the topic appeared in 2012, after the publication of his patent application.
Ethical considerations
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.
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. Other concerns involve the use of BCIs in advanced interrogation techniques, unauthorized access ("brain hacking"),
social stratification through selective enhancement, privacy issues
related to mind-reading, tracking and "tagging" systems, and the
potential for mind, movement, and emotion control.
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." Haselager and colleagues highlighted the importance of managing expectations and value.
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.
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.
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.
In 2008, Final Fantasy developer Square Enix announced that it was partnering with NeuroSky to create Judecca, a game.
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.
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.
In November 2011, Time magazine selected "necomimi" produced by Neurowear as one of the year's best inventions.
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.
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. Basic diagnostic software is available for Android devices, as well as a text entry app for Unity.
In 2020, NextMind released a dev kit including an EEG headset with dry electrodes at $399. The device can run various visual-BCI demonstration applications or developers can create their own. It was later acquired by Snap Inc. in 2022.
In 2023, PiEEG released a shield that allows converting a
single-board computer Raspberry Pi to a brain-computer interface for
$350.
Future directions
Brain-computer interface
A consortium of 12 European partners completed a roadmap to support the European Commission in their funding decisions for the Horizon 2020
framework program. The project was funded by the European Commission.
It started in November 2013 and published a roadmap in April 2015. A 2015 publication describes this project, as well as the Brain-Computer Interface Society.
It reviewed work within this project that further defined BCIs and
applications, explored recent trends, discussed ethical issues, and
evaluated directions for new BCIs.
Other recent publications too have explored future BCI directions for new groups of disabled users.
Disorders of consciousness (DOC)
Some people have a disorder of consciousness (DOC). This state is defined to include people in a coma and those in a vegetative state (VS) or minimally conscious state
(MCS). BCI research seeks to address DOC. A key initial goal is to
identify patients who can perform basic cognitive tasks, which would
change their diagnosis, and allow them to make important decisions (such
as whether to seek therapy, where to live, and their views on
end-of-life decisions regarding them). Patients incorrectly diagnosed
may die as a result of end-of-life decisions made by others. The
prospect of using BCI to communicate with such patients is a tantalizing
prospect.
Many such patients cannot use BCIs based on vision. Hence, tools
must rely on auditory and/or vibrotactile stimuli. Patients may wear
headphones and/or vibrotactile stimulators placed on responsive body
parts. Another challenge is that patients may be able to communicate
only at unpredictable intervals. Home devices can allow communications
when the patient is ready.
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 convey 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.
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. Several groups have explored systems and methods for motor recovery that include BCIs.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.
Imaging studies combined with EEG-based BCI systems hold promise for
investigating neuroplasticity during motor recovery post-stroke.
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. 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.
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.
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.
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.
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