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111-405: The brain–computer interface toy, released was Uncle Milton Industries ' Star Wars Science line in 2009, comes with a headset that claims to sense the brain's electric fields (similar to an EEG ) and relays the signals to a tube that uses a fan to blow a ball into the air. The harder the user concentrates, the harder the fan blows, and the higher the ball is suspended. The voice of Yoda instructs

222-414: A brain–computer interface . Oscillatory activity is observed throughout the central nervous system at all levels of organization. Three different levels have been widely recognized: the micro-scale (activity of a single neuron), the meso-scale (activity of a local group of neurons) and the macro-scale (activity of different brain regions). Neurons generate action potentials resulting from changes in

333-460: 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 (hands...), although they also raise

444-403: A neural ensemble , also referred to as local synchronization. In addition to local synchronization, oscillatory activity of distant neural structures (single neurons or neural ensembles) can synchronize. Neural oscillations and synchronization have been linked to many cognitive functions such as information transfer, perception, motor control and memory. The opposite of neuron synchronization

555-559: 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 . Other laboratories that have developed BCIs and algorithms that decode neuron signals include John Donoghue at the Carney Institute for Brain Science at Brown University , Andrew Schwartz at

666-672: A bimodal distribution, i.e. a high- and low-amplitude mode, and hence shows that resting-state activity does not just reflect a noise process. In case of fMRI, spontaneous fluctuations in the blood-oxygen-level dependent (BOLD) signal reveal correlation patterns that are linked to resting state networks, such as the default network . The temporal evolution of resting state networks is correlated with fluctuations of oscillatory EEG activity in different frequency bands. Ongoing brain activity may also have an important role in perception, as it may interact with activity related to incoming stimuli. Indeed, EEG studies suggest that visual perception

777-506: A biological neuron is a mathematical description of the properties of nerve cells, or neurons, that is designed to accurately describe and predict its biological processes. One of the most successful neuron models is the Hodgkin–Huxley model, for which Hodgkin and Huxley won the 1963 Nobel Prize in physiology or medicine. The model is based on data from the squid giant axon and consists of nonlinear differential equations that approximate

888-1165: A broad spectral content similar to pink noise , but also reveal oscillatory activity in specific frequency bands. The first discovered and best-known frequency band is alpha activity (8–12 Hz ) that can be detected from the occipital lobe during relaxed wakefulness and which increases when the eyes are closed. Other frequency bands are: delta (1–4 Hz), theta (4–8 Hz), beta (13–30 Hz), low gamma (30–70 Hz), and high gamma (70–150 Hz) frequency bands. Faster rhythms such as gamma activity have been linked to cognitive processing. Indeed, EEG signals change dramatically during sleep. In fact, different sleep stages are commonly characterized by their spectral content. Consequently, neural oscillations have been linked to cognitive states, such as awareness and consciousness . Although neural oscillations in human brain activity are mostly investigated using EEG recordings, they are also observed using more invasive recording techniques such as single-unit recordings . Neurons can generate rhythmic patterns of action potentials or spikes. Some types of neurons have

999-516: 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

1110-429: A common frequency, they will generate oscillations in the mean field. (See also figure at top of page.) Neural ensembles can generate oscillatory activity endogenously through local interactions between excitatory and inhibitory neurons. In particular, inhibitory interneurons play an important role in producing neural ensemble synchrony by generating a narrow window for effective excitation and rhythmically modulating

1221-504: A different frequency than the firing frequency of individual neurons. A well-known example of macroscopic neural oscillations is alpha activity . Neural oscillations in humans were observed by researchers as early as 1924 (by Hans Berger ). More than 50 years later, intrinsic oscillatory behavior was encountered in vertebrate neurons, but its functional role is still not fully understood. The possible roles of neural oscillations include feature binding , information transfer mechanisms and

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1332-451: A dynamical pattern at the global scale. The Kuramoto model is widely used to study oscillatory brain activity, and several extensions have been proposed that increase its neurobiological plausibility, for instance by incorporating topological properties of local cortical connectivity. In particular, it describes how the activity of a group of interacting neurons can become synchronized and generate large-scale oscillations. Simulations using

1443-566: A first, second, and third-place winner, who receive awards of $ 3,000, $ 2,000, and $ 1,000, respectively. 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. Invasive BCI research has targeted repairing damaged sight and providing new functionality for people with paralysis. Invasive BCIs are implanted directly into

1554-445: A functional role of neural oscillations; a unified interpretation, however, is still lacking. Richard Caton discovered electrical activity in the cerebral hemispheres of rabbits and monkeys and presented his findings in 1875. Adolf Beck published in 1890 his observations of spontaneous electrical activity of the brain of rabbits and dogs that included rhythmic oscillations altered by light, detected with electrodes directly placed on

1665-632: A grant from the National Science Foundation , followed by a contract from the Defence 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,

1776-414: A group of interacting neurons that form a network, called a central pattern generator . Central pattern generators are neuronal circuits that—when activated—can produce rhythmic motor patterns in the absence of sensory or descending inputs that carry specific timing information. Examples are walking , breathing , and swimming , Most evidence for central pattern generators comes from lower animals, such as

1887-419: 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 . Tetraplegic Matt Nagle became

1998-487: 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

2109-706: A more physiologically realistic setting, oscillatory activity is generally studied using computer simulations of a computational model . The functions of neural oscillations are wide-ranging and vary for different types of oscillatory activity. Examples are the generation of rhythmic activity such as a heartbeat and the neural binding of sensory features in perception, such as the shape and color of an object. Neural oscillations also play an important role in many neurological disorders , such as excessive synchronization during seizure activity in epilepsy , or tremor in patients with Parkinson's disease . Oscillatory activity can also be used to control external devices such as

2220-513: A much slower time scale. That is, the concentration levels of certain neurotransmitters are known to regulate the amount of oscillatory activity. For instance, GABA concentration has been shown to be positively correlated with frequency of oscillations in induced stimuli. A number of nuclei in the brainstem have diffuse projections throughout the brain influencing concentration levels of neurotransmitters such as norepinephrine , acetylcholine and serotonin . These neurotransmitter systems affect

2331-425: A neural code representing multiple items in a temporal frame Neural synchronization can be modulated by task constraints, such as attention , and is thought to play a role in feature binding , neuronal communication , and motor coordination . Neuronal oscillations became a hot topic in neuroscience in the 1990s when the studies of the visual system of the brain by Gray, Singer and others appeared to support

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2442-407: 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

2553-567: A population of physically interconnected neurons or a group of disparate neurons whose inputs or signalling targets define a recognizable circuit. These models aim to describe how the dynamics of neural circuitry arise from interactions between individual neurons. Local interactions between neurons can result in the synchronization of spiking activity and form the basis of oscillatory activity. In particular, models of interacting pyramidal cells and inhibitory interneurons have been shown to generate brain rhythms such as gamma activity . Similarly, it

2664-526: 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

2775-505: 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. Andersen's group used recordings of premovement activity from the posterior parietal cortex , including signals created when experimental animals anticipated receiving a reward. In addition to predicting kinematic and kinetic parameters of limb movements, BCIs that predict electromyographic or electrical activity of

2886-403: 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

2997-471: 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

3108-495: A slow wave and a fast wave. There are many kinds, generally written as A-B coupling, meaning the A of a slow wave is coupled with the B of a fast wave. For example, phase–amplitude coupling is where the phase of a slow wave is coupled with the amplitude of a fast wave. The theta-gamma code is a coupling between theta wave and gamma wave in the hippocampal network. During a theta wave, 4 to 8 non-overlapping neuron ensembles are activated in sequence. This has been hypothesized to form

3219-399: A system affect each other after significant time delays. Limit-cycle oscillations can be complex but there are powerful mathematical tools for analyzing them; the mathematics of delayed-feedback oscillations is primitive in comparison. Linear oscillators and limit-cycle oscillators qualitatively differ in terms of how they respond to fluctuations in input. In a linear oscillator, the frequency

3330-481: Is amplitude change in oscillatory activity. For instance, gamma activity often increases during increased mental activity such as during object representation. Because induced responses may have different phases across measurements and therefore would cancel out during averaging, they can only be obtained using time-frequency analysis . Induced activity generally reflects the activity of numerous neurons: amplitude changes in oscillatory activity are thought to arise from

3441-496: Is an ongoing debate. It has recently been proposed that even if phases are not aligned across trials, induced activity may still cause event-related potentials because ongoing brain oscillations may not be symmetric and thus amplitude modulations may result in a baseline shift that does not average out. This model implies that slow event-related responses, such as asymmetric alpha activity, could result from asymmetric brain oscillation amplitude modulations, such as an asymmetry of

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3552-449: Is best seen in local field potentials which reflect the synchronous activity of local groups of neurons, but has also been shown in EEG and MEG recordings providing increasing evidence for a close relation between synchronous oscillatory activity and a variety of cognitive functions such as perceptual grouping and attentional top-down control. Cells in the sinoatrial node , located in

3663-433: Is considered to play a crucial role during brain development, such as in network formation and synaptogenesis. Spontaneous activity may be informative regarding the current mental state of the person (e.g. wakefulness, alertness) and is often used in sleep research. Certain types of oscillatory activity, such as alpha waves , are part of spontaneous activity. Statistical analysis of power fluctuations of alpha activity reveals

3774-423: Is dependent on both the phase and amplitude of cortical oscillations. For instance, the amplitude and phase of alpha activity at the moment of visual stimulation predicts whether a weak stimulus will be perceived by the subject. In response to input, a neuron or neuronal ensemble may change the frequency at which it oscillates, thus changing the rate at which it spikes. Often, a neuron's firing rate depends on

3885-508: Is inversely related to the delay time. An example of such a feedback loop is the connections between the thalamus and cortex – the thalamocortical radiations . This thalamocortical network is able to generate oscillatory activity known as recurrent thalamo-cortical resonance . The thalamocortical network plays an important role in the generation of alpha activity . In a whole-brain network model with realistic anatomical connectivity and propagation delays between brain areas, oscillations in

3996-513: Is more or less constant but the amplitude can vary greatly. In a limit-cycle oscillator, the amplitude tends to be more or less constant but the frequency can vary greatly. A heartbeat is an example of a limit-cycle oscillation in that the frequency of beats varies widely, while each individual beat continues to pump about the same amount of blood. Computational models adopt a variety of abstractions in order to describe complex oscillatory dynamics observed in brain activity. Many models are used in

4107-894: Is neural isolation, which is when electrical activity of neurons is not temporally synchronized. This is when the likelihood of the neuron to reach its threshold potential for the signal to propagate to the next neuron decreases. This phenomenon is typically observed as the spectral intensity decreases from the summation of these neurons firing, which can be utilized to differentiate cognitive function or neural isolation. However, new non-linear methods have been used that couple temporal and spectral entropic relationships simultaneously to characterize how neurons are isolated, (the signal's inability to propagate to adjacent neurons), an indicator of impairment (e.g., hypoxia). Neural oscillations have been most widely studied in neural activity generated by large groups of neurons. Large-scale activity can be measured by techniques such as EEG. In general, EEG signals have

4218-436: Is one of the most abstract and fundamental models used to investigate neural oscillations and synchronization. It captures the activity of a local system (e.g., a single neuron or neural ensemble) by its circular phase alone and hence ignores the amplitude of oscillations (amplitude is constant). Interactions amongst these oscillators are introduced by a simple algebraic form (such as a sine function) and collectively generate

4329-464: Is referred to as phase resetting. In addition, external activity may not interact with ongoing activity at all, resulting in an additive response. Spontaneous activity is brain activity in the absence of an explicit task, such as sensory input or motor output, and hence also referred to as resting-state activity. It is opposed to induced activity, i.e. brain activity that is induced by sensory stimuli or motor responses. The term ongoing brain activity

4440-445: Is used in electroencephalography and magnetoencephalography for those signal components that are not associated with the processing of a stimulus or the occurrence of specific other events, such as moving a body part, i.e. events that do not form evoked potentials / evoked fields , or induced activity. Spontaneous activity is usually considered to be noise if one is interested in stimulus processing; however, spontaneous activity

4551-405: Is very common in single neurons where spike timing is adjusted to neuronal input (a neuron may spike at a fixed delay in response to periodic input, which is referred to as phase locking ) and may also occur in neuronal ensembles when the phases of their neurons are adjusted simultaneously. Phase resetting is fundamental for the synchronization of different neurons or different brain regions because

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4662-749: 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

4773-546: The FitzHugh–Nagumo model and the Hindmarsh–Rose model , or highly idealized neuron models such as the leaky integrate-and-fire neuron, originally developed by Lapique in 1907. Such models only capture salient membrane dynamics such as spiking or bursting at the cost of biophysical detail, but are more computationally efficient, enabling simulations of larger biological neural networks . A neural network model describes

4884-531: The University of Pittsburgh , and Richard Andersen at Caltech . These researchers produced working BCIs using recorded signals from far fewer neurons than Nicolelis (15–30 neurons versus 50–200 neurons). 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

4995-540: The Wilson-Cowan model . If a group of neurons engages in synchronized oscillatory activity, the neural ensemble can be mathematically represented as a single oscillator. Different neural ensembles are coupled through long-range connections and form a network of weakly coupled oscillators at the next spatial scale. Weakly coupled oscillators can generate a range of dynamics including oscillatory activity. Long-range connections between different brain structures, such as

5106-455: The beta frequency range emerge from the partial synchronisation of subsets of brain areas oscillating in the gamma-band (generated at the mesoscopic level). Scientists have identified some intrinsic neuronal properties that play an important role in generating membrane potential oscillations. In particular, voltage-gated ion channels are critical in the generation of action potentials. The dynamics of these ion channels have been captured in

5217-457: The generation of rhythmic motor output . Over the last decades more insight has been gained, especially with advances in brain imaging . A major area of research in neuroscience involves determining how oscillations are generated and what their roles are. Oscillatory activity in the brain is widely observed at different levels of organization and is thought to play a key role in processing neural information. Numerous experimental studies support

5328-401: 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

5439-476: The heart rate . In the absence of extrinsic neural and hormonal control, cells in the SA node will rhythmically discharge. The sinoatrial node is richly innervated by the autonomic nervous system , which up or down regulates the spontaneous firing frequency of the pacemaker cells. Synchronized firing of neurons also forms the basis of periodic motor commands for rhythmic movements. These rhythmic outputs are produced by

5550-400: The neural binding hypothesis. According to this idea, synchronous oscillations in neuronal ensembles bind neurons representing different features of an object. For example, when a person looks at a tree, visual cortex neurons representing the tree trunk and those representing the branches of the same tree would oscillate in synchrony to form a single representation of the tree. This phenomenon

5661-493: 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

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5772-438: The right atrium of the heart, spontaneously depolarize approximately 100 times per minute. Although all of the heart's cells have the ability to generate action potentials that trigger cardiac contraction, the sinoatrial node normally initiates it, simply because it generates impulses slightly faster than the other areas. Hence, these cells generate the normal sinus rhythm and are called pacemaker cells as they directly control

5883-584: The thalamus and the cortex (see thalamocortical oscillation ), involve time-delays due to the finite conduction velocity of axons. Because most connections are reciprocal, they form feed-back loops that support oscillatory activity. Oscillations recorded from multiple cortical areas can become synchronized to form large-scale brain networks , whose dynamics and functional connectivity can be studied by means of spectral analysis and Granger causality measures. Coherent activity of large-scale brain activity may form dynamic links between brain areas required for

5994-481: 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 cortex neurons , 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

6105-521: The Kuramoto model with realistic long-range cortical connectivity and time-delayed interactions reveal the emergence of slow patterned fluctuations that reproduce resting-state BOLD functional maps, which can be measured using fMRI . Both single neurons and groups of neurons can generate oscillatory activity spontaneously. In addition, they may show oscillatory responses to perceptual input or motor output. Some types of neurons will fire rhythmically in

6216-401: The absence of any synaptic input. Likewise, brain-wide activity reveals oscillatory activity while subjects do not engage in any activity, so-called resting-state activity . These ongoing rhythms can change in different ways in response to perceptual input or motor output. Oscillatory activity may respond by increases or decreases in frequency and amplitude or show a temporary interruption, which

6327-445: 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

6438-566: 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

6549-621: The central idea is to take the density of neurons to the continuum limit , resulting in spatially continuous neural networks . Instead of modelling individual neurons, this approach approximates a group of neurons by its average properties and interactions. It is based on the mean field approach , an area of statistical physics that deals with large-scale systems. Models based on these principles have been used to provide mathematical descriptions of neural oscillations and EEG rhythms. They have for instance been used to investigate visual hallucinations. The Kuramoto model of coupled phase oscillators

6660-487: The company had successfully enabled a monkey to play video games using Neuralink's device. 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

6771-405: The context of a simple learning task, illumination of transfected cells in the somatosensory cortex influenced decision-making in mice. 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

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6882-454: The critical threshold and therefore do not result in an action potential. They can result from postsynaptic potentials from synchronous inputs or from intrinsic properties of neurons. Neuronal spiking can be classified by its activity pattern. The excitability of neurons can be subdivided in Class I and II. Class I neurons can generate action potentials with arbitrarily low frequency depending on

6993-563: 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). Later experiments on rhesus monkeys included feedback and reproduced monkey reaching and grasping movements in

7104-532: 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. Phillip Kennedy (Neural Signals founder (1987) and colleagues built

7215-464: 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

7326-516: The electric membrane potential. Neurons can generate multiple action potentials in sequence forming so-called spike trains. These spike trains are the basis for neural coding and information transfer in the brain. Spike trains can form all kinds of patterns, such as rhythmic spiking and bursting , and often display oscillatory activity. Oscillatory activity in single neurons can also be observed in sub-threshold fluctuations in membrane potential. These rhythmic changes in membrane potential do not reach

7437-451: The electrical characteristics of a neuron, including the generation and propagation of action potentials . The model is so successful at describing these characteristics that variations of its "conductance-based" formulation continue to be utilized in neuron models over a half a century later. The Hodgkin–Huxley model is too complicated to understand using classical mathematical techniques, so researchers often turn to simplifications such as

7548-472: The electrodes are connected directly to each other instead of being worn by the player, the game will proceed to play itself and pass all of the training exercises without any user input. This Star Wars -related article is a stub . You can help Misplaced Pages by expanding it . This toy -related article is a stub . You can help Misplaced Pages by expanding it . Brain%E2%80%93computer interface A brain–computer interface ( BCI ), sometimes called

7659-414: The field, each defined at a different level of abstraction and trying to model different aspects of neural systems. They range from models of the short-term behaviour of individual neurons, through models of how the dynamics of neural circuitry arise from interactions between individual neurons, to models of how behaviour can arise from abstract neural modules that represent complete subsystems. A model of

7770-402: The firing rate of excitatory neurons. Neural oscillation can also arise from interactions between different brain areas coupled through the structural connectome . Time delays play an important role here. Because all brain areas are bidirectionally coupled, these connections between brain areas form feedback loops. Positive feedback loops tend to cause oscillatory activity where frequency

7881-523: The first neuroprosthetic devices were implanted in humans in the mid-1990s. Studies in human-computer interaction via the application of machine learning to statistical temporal features extracted from the frontal lobe ( EEG brainwave ) data has achieved success in classifying mental states (relaxed, neutral, concentrating), mental emotional states (negative, neutral, positive), and thalamocortical dysrhythmia . The history of brain-computer interfaces (BCIs) starts with Hans Berger 's discovery of

7992-457: The first intracortical brain–computer interface by implanting neurotrophic-cone electrodes into monkeys. 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

8103-485: 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

8214-432: 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

8325-399: The frequency of large-scale oscillations does not need to match the firing pattern of individual neurons. Isolated cortical neurons fire regularly under certain conditions, but in the intact brain, cortical cells are bombarded by highly fluctuating synaptic inputs and typically fire seemingly at random. However, if the probability of a large group of neurons firing is rhythmically modulated at

8436-485: 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

8547-400: 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

8658-422: The input strength, whereas Class II neurons generate action potentials in a certain frequency band, which is relatively insensitive to changes in input strength. Class II neurons are also more prone to display sub-threshold oscillations in membrane potential. A group of neurons can also generate oscillatory activity. Through synaptic interactions, the firing patterns of different neurons may become synchronized and

8769-433: The integration of distributed information. Microglia  – the major immune cells of the brain – have been shown to play an important role in shaping network connectivity, and thus, influencing neuronal network oscillations both ex vivo and in vivo . In addition to fast direct synaptic interactions between neurons forming a network, oscillatory activity is regulated by neuromodulators on

8880-440: The intracellular currents that propagate forward and backward down the dendrites. Under this assumption, asymmetries in the dendritic current would cause asymmetries in oscillatory activity measured by EEG and MEG, since dendritic currents in pyramidal cells are generally thought to generate EEG and MEG signals that can be measured at the scalp. Cross-frequency coupling (CFC) describes the coupling (statistical correlation) between

8991-410: The level of neural ensembles , synchronized activity of large numbers of neurons can give rise to macroscopic oscillations, which can be observed in an electroencephalogram . Oscillatory activity in groups of neurons generally arises from feedback connections between the neurons that result in the synchronization of their firing patterns. The interaction between neurons can give rise to oscillations at

9102-674: The motion of a pendulum , and vibrations of every sort. They generally arise when a physical system is perturbed by a small degree from a minimum-energy state , and are well understood mathematically. Noise-driven harmonic oscillators realistically simulate alpha rhythm in the waking EEG as well as slow waves and spindles in the sleep EEG. Successful EEG analysis algorithms were based on such models. Several other EEG components are better described by limit-cycle or delayed-feedback oscillations. Limit-cycle oscillations arise from physical systems that show large deviations from equilibrium , whereas delayed-feedback oscillations arise when components of

9213-399: The motor cortex of tetraplegia patients. 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

9324-618: The motor cortex, utilizing Hidden Markov models and recurrent neural networks . Neural oscillation Neural oscillations , or brainwaves , are rhythmic or repetitive patterns of neural activity in the central nervous system . Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials , which then produce oscillatory activation of post-synaptic neurons. At

9435-431: 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

9546-435: 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

9657-524: 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. 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

9768-538: 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 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

9879-400: The periphery. These sensory BCI devices enable real-time, behaviorally-relevant decisions based upon closed-loop neural stimulation. 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

9990-519: The physiological state, e.g., wakefulness or arousal , and have a pronounced effect on amplitude of different brain waves, such as alpha activity. Oscillations can often be described and analyzed using mathematics. Mathematicians have identified several dynamical mechanisms that generate rhythmicity. Among the most important are harmonic (linear) oscillators, limit cycle oscillators, and delayed- feedback oscillators. Harmonic oscillations appear very frequently in nature—examples are sound waves,

10101-402: The possibility of erasing the distinction between brain and machine . 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

10212-563: The potential to serve as pacemakers for synchronous network oscillations, and bursts of spikes may underlie or enhance neuronal resonance. Many of these neurons can be considered intrinsic oscillators, namely, neurons that generate their oscillations intrinsically, as their oscillation frequencies can be modified by local applications of glutamate in-vivo. Apart from intrinsic properties of neurons, biological neural network properties are also an important source of oscillatory activity. Neurons communicate with one another via synapses and affect

10323-429: The presentation of a stimulus. As a consequence, those signal components that are the same in each single measurement are conserved and all others, i.e. ongoing or spontaneous activity, are averaged out. That is, event-related potentials only reflect oscillations in brain activity that are phase -locked to the stimulus or event. Evoked activity is often considered to be independent from ongoing brain activity, although this

10434-502: 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. A new 'wireless' approach uses light-gated ion channels such as channelrhodopsin to control the activity of genetically defined subsets of neurons in vivo . In

10545-534: The rhythmic changes in electric potential caused by their action potentials may accumulate ( constructive interference ). That is, synchronized firing patterns result in synchronized input into other cortical areas, which gives rise to large-amplitude oscillations of the local field potential . These large-scale oscillations can also be measured outside the scalp using electroencephalography (EEG) and magnetoencephalography (MEG). The electric potentials generated by single neurons are far too small to be picked up outside

10656-420: 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

10767-486: The scalp, and EEG or MEG activity always reflects the summation of the synchronous activity of thousands or millions of neurons that have similar spatial orientation. Neurons in a neural ensemble rarely all fire at exactly the same moment, i.e. fully synchronized. Instead, the probability of firing is rhythmically modulated such that neurons are more likely to fire at the same time, which gives rise to oscillations in their mean activity. (See figure at top of page.) As such,

10878-669: The strength of neural oscillations in recorded data. Neural oscillations are commonly studied within a mathematical framework and belong to the field of "neurodynamics", an area of research in the cognitive sciences that places a strong focus on the dynamic character of neural activity in describing brain function. It considers the brain a dynamical system and uses differential equations to describe how neural activity evolves over time. In particular, it aims to relate dynamic patterns of brain activity to cognitive functions such as perception and memory. In very abstract form, neural oscillations can be analyzed analytically . When studied in

10989-747: The summed activity it receives. Frequency changes are also commonly observed in central pattern generators and directly relate to the speed of motor activities , such as step frequency in walking. However, changes in relative oscillation frequency between different brain areas is not so common because the frequency of oscillatory activity is often related to the time delays between brain areas. Next to evoked activity, neural activity related to stimulus processing may result in induced activity. Induced activity refers to modulation in ongoing brain activity induced by processing of stimuli or movement preparation. Hence, they reflect an indirect response in contrast to evoked responses. A well-studied type of induced activity

11100-690: The surface of the brain. Before Hans Berger , Vladimir Vladimirovich Pravdich-Neminsky published the first animal EEG and the evoked potential of a dog. Neural oscillations are observed throughout the central nervous system at all levels, and include spike trains , local field potentials and large-scale oscillations which can be measured by electroencephalography (EEG). In general, oscillations can be characterized by their frequency , amplitude and phase . These signal properties can be extracted from neural recordings using time-frequency analysis . In large-scale oscillations, amplitude changes are considered to result from changes in synchronization within

11211-429: The synchronization of neural activity, for instance by synchronization of spike timing or membrane potential fluctuations of individual neurons. Increases in oscillatory activity are therefore often referred to as event-related synchronization, while decreases are referred to as event-related desynchronization. Phase resetting occurs when input to a neuron or neuronal ensemble resets the phase of ongoing oscillations. It

11322-411: 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

11433-522: The tendency to fire at particular frequencies, either as resonators or as intrinsic oscillators . Bursting is another form of rhythmic spiking. Spiking patterns are considered fundamental for information coding in the brain. Oscillatory activity can also be observed in the form of subthreshold membrane potential oscillations (i.e. in the absence of action potentials). If numerous neurons spike in synchrony , they can give rise to oscillations in local field potentials . Quantitative models can estimate

11544-468: 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"

11655-613: The timing of spike trains in the post-synaptic neurons. Depending on the properties of the connection, such as the coupling strength, time delay and whether coupling is excitatory or inhibitory , the spike trains of the interacting neurons may become synchronized . Neurons are locally connected, forming small clusters that are called neural ensembles . Certain network structures promote oscillatory activity at specific frequencies. For example, neuronal activity generated by two populations of interconnected inhibitory and excitatory cells can show spontaneous oscillations that are described by

11766-479: The timing of spikes can become phase locked to the activity of other neurons. Phase resetting also permits the study of evoked activity, a term used in electroencephalography and magnetoencephalography for responses in brain activity that are directly related to stimulus -related activity. Evoked potentials and event-related potentials are obtained from an electroencephalogram by stimulus-locked averaging, i.e. averaging different trials at fixed latencies around

11877-425: 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,

11988-649: The user on developing their skills. In a 2010 episode of the College Humor series Bleep Bloop , the hosts Jeff Rubin and Pat Cassels tested out the toy, even having a co-worker, Brian Murphy, play Brain Age , a video game advertised as making you use your brain more, while he had the Force Trainer headset on. One user of the toy argues that the brainwave effect of the Force Trainer II is fake; if

12099-440: The various instruments via loudspeakers that are placed near or directly on the instruments. 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

12210-905: The well-established Hodgkin–Huxley model that describes how action potentials are initiated and propagated by means of a set of differential equations. Using bifurcation analysis , different oscillatory varieties of these neuronal models can be determined, allowing for the classification of types of neuronal responses. The oscillatory dynamics of neuronal spiking as identified in the Hodgkin–Huxley model closely agree with empirical findings. In addition to periodic spiking, subthreshold membrane potential oscillations , i.e. resonance behavior that does not result in action potentials, may also contribute to oscillatory activity by facilitating synchronous activity of neighboring neurons. Like pacemaker neurons in central pattern generators , subtypes of cortical cells fire bursts of spikes (brief clusters of spikes) rhythmically at preferred frequencies. Bursting neurons have

12321-408: Was shown that simulations of neural networks with a phenomenological model for neuronal response failures can predict spontaneous broadband neural oscillations. Neural field models are another important tool in studying neural oscillations and are a mathematical framework describing evolution of variables such as mean firing rate in space and time. In modeling the activity of large numbers of neurons,

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