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.A brain-computer interface ( BCI), sometimes called a neural-control interface ( NCI), mind-machine interface ( MMI), direct neural interface ( DNI), or brain-machine interface ( BMI), is a direct communication pathway between an enhanced or wired and an external device. BCI differs from in that it allows for bidirectional information flow. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions.Research on BCIs began in the 1970s at the (UCLA) under a grant from the, followed by a contract from. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature.Due to the 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 devices implanted in humans appeared in the mid-1990s. Contents.History The history of brain–computer interfaces (BCIs) starts with 's discovery of the electrical activity of the human brain and the development of (EEG).
In 1924 Berger was the first to record human brain activity by means of EEG. Berger was able to identify, such as Berger's wave or the (8–13 Hz), by analyzing EEG traces.Berger's first recording device was very rudimentary. He inserted 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, with disappointing results.
However, more sophisticated measuring devices, such as the double-coil recording, which displayed electric voltages as small as one ten thousandth of a volt, led to success.Berger analyzed the interrelation of alternations in his EEG wave diagrams with. EEGs permitted completely new possibilities for the research of human brain activities.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 the American composer.
The piece makes use of EEG and analog signal processing hardware (filters, amplifiers, and a mixing board) to stimulate acoustic percussion instruments. To perform the piece one must produce and thereby 'play' the various percussion instruments via loudspeakers which are placed near or directly on the instruments themselves.Professor Jacques Vidal coined the term 'BCI' and produced the first peer-reviewed publications on this topic. Vidal is widely recognized as the inventor of BCIs in the BCI community, as reflected in numerous peer-reviewed articles reviewing and discussing the field (e.g., ). His 1973 paper stated the 'BCI challenge': Control of external objects using EEG signals. Especially he pointed out to potential as a challenge for BCI control. The 1977 experiment Vidal described 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.After his early contributions, Vidal was not active in BCI research, nor BCI events such as conferences, for many years. In 2011, however, he gave a lecture in, supported by the Future BNCI project, presenting the first BCI, which earned a standing ovation. Vidal was joined by his wife, Laryce Vidal, who previously worked with him at UCLA on his first BCI project.In 1988, a report was given on noninvasive EEG control of a physical object, a robot. The experiment described was EEG control of multiple start-stop-restart of the robot 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 autonomous source of energy. This 1988 report written by Stevo Bozinovski, Mihail Sestakov, and Liljana Bozinovska was the first one about a robot control using EEG.In 1990, a report was given on a bidirectional adaptive BCI controlling 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, controls in a feedback loop the S2 buzzer in the S1-S2-CNV paradigm. The obtained cognitive wave representing the expectation learning in the brain is named Electroexpectogram (EXG). The CNV brain potential was part of the BCI challenge presented by Vidal in his 1973 paper.BCIs versus neuroprosthetics. Main article:Neuroprosthetics is an area of concerned with neural prostheses, that is, using artificial devices to replace the function of impaired nervous systems and brain-related problems, or of sensory organs. As of December 2010, had been implanted as neuroprosthetic device in approximately 220,000 people worldwide. There are also several neuroprosthetic devices that aim to restore vision, including.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. Both use similar experimental methods and surgical techniques.Animal BCI research Several laboratories have managed to record signals from monkey and rat to operate BCIs to produce movement. Monkeys have navigated on screen and commanded robotic arms to perform simple tasks simply by thinking about the task and seeing the visual feedback, but without any motor output. In May 2008 photographs that showed a monkey at the operating a robotic arm by thinking were published in a number of well-known science journals and magazines. Early work.
Monkey operating a robotic arm with brain–computer interfacing (Schwartz lab, University of Pittsburgh)In 1969 the studies of Fetz and colleagues,at the Regional Primate Research Center and Department of Physiology and Biophysics, in, showed for the first time that monkeys could learn to control the deflection of a meter arm with neural activity. Similar work in the 1970s established that monkeys could quickly learn to voluntarily control the firing rates of individual and multiple neurons in the primary if they were rewarded for generating appropriate patterns of neural activity.Studies that developed to reconstruct movements from, which control movement, date back to the 1970s. In the 1980s, Apostolos Georgopoulos at found a mathematical relationship between the electrical responses of single motor cortex neurons in and the direction in which they moved their arms (based on a function). He also found that dispersed groups of neurons, in different areas of the monkey's brains, collectively controlled motor commands, but was able to record the firings of neurons in only one area at a time, because of the technical limitations imposed by his equipment.There has been rapid development in BCIs since the mid-1990s. Several groups have been able to capture complex brain motor cortex signals by recording from (groups of neurons) and using these to control external devices.Prominent research successes Kennedy and Yang Dan Phillip Kennedy (who later founded Neural Signals in 1987) and colleagues built the first intracortical brain–computer interface by implanting neurotrophic-cone into monkeys. Yang Dan and colleagues' recordings of cat vision using a BCI implanted in the (top row: original image; bottom row: recording)In 1999, researchers led by Yang Dan at the decoded neuronal firings to reproduce images seen by cats.
The team used an array of electrodes embedded in the (which integrates all of the brain's sensory input) of sharp-eyed cats. Researchers targeted 177 brain cells in the thalamus area, which decodes signals from the. The cats were shown eight short movies, and their neuron firings were recorded. Using mathematical filters, the researchers decoded the signals to generate movies of what the cats saw and were able to reconstruct recognizable scenes and moving objects. Similar results in humans have since been achieved by researchers in Japan.Nicolelis , a professor at, in, has been a prominent proponent of using multiple electrodes spread over a greater area of the brain to obtain neuronal signals to drive a BCI.After conducting initial studies in rats during the 1990s, Nicolelis and his colleagues developed BCIs that decoded brain activity in and used the devices to reproduce monkey movements in robotic arms. Monkeys have advanced reaching and grasping abilities and good hand manipulation skills, making them ideal test subjects for this kind of work.By 2000, the group succeeded in building a BCI that reproduced owl monkey movements while the monkey operated a or reached for food.
The BCI operated in real time and could also control a separate robot remotely over. But the monkeys could not see the arm moving and did not receive any feedback, a so-called BCI. Jens Naumann, a man with acquired blindness, being interviewed about his vision BCI on CBS'sInvasive BCI research has targeted repairing damaged sight and providing new functionality for people with paralysis.
Invasive BCIs are implanted directly into the 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 build-up, causing the signal to become weaker, or even non-existent, as the body reacts to a foreign object in the brain.In, direct have been used to treat non- (acquired) blindness. One of the first scientists to produce a working brain interface to restore sight was private researcher.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 and succeeded in producing, 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, but shrinking electronics and faster computers made his artificial eye more portable and now enable him to perform simple tasks unassisted. Dummy unit illustrating the design of a interfaceIn 2002, Jens Naumann, also blinded in adulthood, became the first in a series of 16 paying patients to receive Dobelle's second generation implant, marking 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 an automobile slowly around the parking area of the research institute. Unfortunately, Dobelle died in 2004 before his processes and developments were documented.
Subsequently, when Mr. Naumann and the other patients in the program began having problems with their vision, there was no relief and they eventually lost their 'sight' again. Naumann wrote about his experience with Dobelle's work in Search for Paradise: A Patient's Account of the Artificial Vision Experiment and has returned to his farm in Southeast Ontario, Canada, to resume his normal activities.
Movement BCIs focusing on motor neuroprosthetics aim to either restore movement in individuals with paralysis or provide devices to assist them, such as interfaces with computers or robot arms.Researchers at in, led by Philip Kennedy and Roy Bakay, were first to install a brain implant in a human that produced signals of high enough quality to simulate movement. Their patient, Johnny Ray (1944–2002), suffered from ‘’ after suffering a brain-stem 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.became the first person to control an artificial hand using a BCI in 2005 as part of the first nine-month human trial of ’s chip-implant. Implanted in Nagle's right (area of the motor cortex for arm movement), the 96-electrode BrainGate implant allowed Nagle to control a robotic arm by thinking about moving his hand as well as a computer cursor, lights and TV.
ATR Labs' reconstruction of human vision using (top row: original image; bottom row: reconstruction from mean of combined readings)(MEG) and (fMRI) have both been used successfully as non-invasive BCIs. In a widely reported experiment, fMRI allowed two users being scanned to play in real-time by altering their or brain blood flow through techniques.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) Laboratories in, Japan, allowed the scientists to reconstruct images directly from the brain and display them on a computer in black and white at a of 10x10. The article announcing these achievements was the of the journal of 10 December 2008.In 2011 researchers from published a study reporting second-by-second reconstruction of videos watched by the study's subjects, from fMRI data. This was achieved by creating a statistical model relating visual patterns in videos shown to the subjects, to the brain activity caused by watching the videos.
This model was then used to look up the 100 one-second video segments, in a database of 18 million seconds of random videos, whose visual patterns most closely matched the brain activity recorded when subjects watched a new video. These 100 one-second video extracts were then combined into a mashed-up image that resembled the video being watched. BCI control strategies in neurogaming Motor imagery involves the imagination of the movement of various body parts resulting in activation, which modulates sensorimotor oscillations in the EEG. This can be detected by the BCI to infer a user's intent.
Motor imagery typically requires a number of sessions of training before acceptable control of the BCI is acquired. These training sessions may take a number of hours over several days before users can consistently employ the technique with acceptable levels of precision. 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. Advance machine learning methods were recently developed to compute a subject-specific model for detecting the performance of motor imagery. The top performing algorithm from BCI Competition IV dataset 2 for motor imagery is the Filter Bank Common Spatial Pattern, developed by Ang et al. Bio/neurofeedback for passive BCI designs Biofeedback is used to monitor a subject's mental relaxation.
In some cases, biofeedback does not monitor electroencephalography (EEG), but instead bodily parameters such as (EMG), (GSR), and (HRV). Many biofeedback systems are used to treat certain disorders such as attention deficit hyperactivity disorder (ADHD), sleep problems in children, teeth grinding, and chronic pain. EEG biofeedback systems typically monitor four different 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 involves using BCI to enrich human–machine interaction with implicit information on the actual user's state, for example, simulations to detect when users intend to push brakes during an emergency car stopping procedure. Game developers using passive BCIs need to acknowledge that through repetition of game levels the user's cognitive state will change or adapt.
Within the first playof a level, the user will react to things differently from during the second play: for example, the user will be less surprised at an event in the game if he/she is expecting it. Visual evoked potential (VEP) A VEP is an electrical potential recorded after a subject is presented with a type of visual stimuli. There are several types of VEPs.(SSVEPs) use potentials generated by exciting the, using visual stimuli modulated at certain frequencies. SSVEP's stimuli are often formed from alternating checkerboard patterns and at times simply use flashing images. The frequency of the phase reversal of the stimulus used can be clearly distinguished in the spectrum of an EEG; this makes detection of SSVEP stimuli relatively easy. SSVEP has proved to be successful 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 and electrocardiographic artefacts do not affect the frequencies monitored.
In addition, the SSVEP signal is exceptionally robust; the topographic organization of the primary visual cortex is such that a broader area obtains afferents from the central or fovial region of the visual field. SSVEP does have several problems however. As SSVEPs use flashing stimuli to infer a user's 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 could become irritating and uncomfortable to use during longer play sessions, which can often last more than an hour which may not be an ideal gameplay.Another type of VEP used with applications is the. The P300 event-related potential is a positive peak in the EEG that occurs at roughly 300 ms after the appearance of a target stimulus (a stimulus for which the user is waiting or seeking).
The P300 amplitude decreases as the target stimuli and the ignored stimuli grow more similar.The P300 is thought to be related to a higher level attention process or an orienting response using P300 as a control scheme has the advantage of the participant only having to attend limited training sessions. The first application to use the P300 model was the P300 matrix. Within this system, a subject would choose a letter from a grid of 6 by 6 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 quite slow.
The P300 is a BCI that offers a discrete selection rather than a continuous control mechanism. The advantage of P300 use within games is that the player does not have to teach himself/herself how to use a completely new control system and so only has to undertake short training instances, to learn the gameplay mechanics and basic use of the BCI paradigm. Synthetic telepathy/silent communication. See also: andIn 2010 the DARPA's budget for the fiscal year included $4 million to start up a program called Silent Talk. The goal was to 'allow user-to-user communication on the battlefield without the use of vocalized speech through analysis of neural signals'. Main article:Researchers have built devices to interface with neural cells and entire neural networks in cultures outside animals. As well as furthering research on animal implantable devices, experiments on cultured neural tissue have focused on building problem-solving networks, constructing basic computers and manipulating robotic devices.
Research into techniques for stimulating and recording from individual neurons grown on semiconductor chips is sometimes referred to as neuroelectronics. The world's first, developed by researchers Jerome Pine and Michael MaherDevelopment of the first working 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, started work on a neurochip designed to function as an artificial or prosthetic.
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The neurochip was designed to function in rat brains and was intended as a prototype for the eventual development of higher-brain prosthesis. The hippocampus was chosen because it is thought to be the most ordered and structured part of the brain and is the most studied area. Its function is to encode experiences for storage as long-term memories elsewhere in the brain.In 2004 Thomas DeMarse at the used a culture of 25,000 neurons taken from a rat's brain to fly a fighter jet.
After collection, the cortical neurons were cultured in a and rapidly began to reconnect themselves to form a living neural network. The cells were arranged over a grid of 60 electrodes and used to control the and functions of the simulator. The study's focus was on understanding how the human brain performs and learns computational tasks at a cellular level.Ethical considerations.
This section includes a, but its sources remain unclear because it has insufficient. Please help to this section by more precise citations. ( June 2019) Sources: User-centric issues. long-term effects to the user remain largely unknown. obtaining informed consent from people who have difficulty communicating,. the consequences of BCI technology for the quality of life of patients and their families,. health-related side-effects (e.g.
Neurofeedback of sensorimotor rhythm training is reported to affect sleep quality),. therapeutic applications and their potential misuseLegal and social. Issues of accountability and responsibility: claims that the influence of BCIs overrides free will and control over sensory-motor actions, claims that cognitive intention was inaccurately translated due to a BCI malfunction. Personality changes involved caused by deep-brain stimulation. blurring of the division between human and machine, inability to distinguish between human vs. Machine-controlled actions.
use of the technology in advanced interrogation techniques by governmental authorities,. selective enhancement and social stratification,. questions of research ethics that arise when progressing from animal experimentation to application in human subjects,.
and privacy,.In their current form, most BCIs are far removed from the ethical issues considered above. They are actually similar to corrective therapies in function. Clausen stated in 2009 that “BCIs pose ethical challenges, but these are conceptually similar to those that bioethicists have addressed for other realms of therapy”. Moreover, he suggests that bioethics is well-prepared to deal with the issues that arise with BCI technologies.
Haselager and colleagues pointed out that expectations of BCI efficacy and value play a great role in ethical analysis and the way BCI scientists should approach media. Furthermore, standard protocols can be implemented to ensure ethically sound informed-consent procedures with locked-in patients.The case of BCIs today has parallels in medicine, as will its evolution. Much as pharmaceutical science began as a balance for impairments and is now used to increase focus and reduce need for sleep, BCIs will likely transform gradually from therapies to enhancements.
Efforts are made inside the BCI community to create consensus on ethical guidelines for BCI research, development and dissemination. Low-cost BCI-based interfaces. Main article:Recently a number of companies have scaled back medical grade EEG technology (and in one case, NeuroSky, rebuilt the technology from the ground up ) to create inexpensive BCIs. This technology has been built into toys and gaming devices; some of these toys have been extremely commercially successful like the NeuroSky and Mattel MindFlex. In 2006 patented a neural interface system allowing radio waves to affect signals in the neural cortex. In 2007 released the first affordable consumer based EEG along with the game NeuroBoy. This was also the first large scale EEG device to use dry sensor technology.
In 2008 developed a device for use in video games relying primarily on. In 2008 the developer announced that it was partnering with NeuroSky to create a game, Judecca. In 2009 partnered with NeuroSky to release the, a game that used an EEG to steer a ball through an obstacle course. By far the best selling consumer based EEG to date.
In 2009 partnered with NeuroSky to release the, a game designed to create the illusion of possessing. In 2009 released the EPOC, a 14 channel EEG device that can read 4 mental states, 13 conscious states, facial expressions, and head movements. The EPOC is the first commercial BCI to use dry sensor technology, which can be dampened with a saline solution for a better connection. In November 2011 selected 'necomimi' produced by as one of the best inventions of the year. The company announced that it expected to launch a consumer version of the garment, consisting of cat-like ears controlled by a brain-wave reader produced by, in spring 2012.
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 devices, as well as a text entry app for.Future directions. Brain-computer interfaceA consortium consisting of 12 European partners has completed a roadmap to support the European Commission in their funding decisions for the new framework program.
The project, which was funded by the European Commission, started in November 2013 and published a roadmap in April 2015. A 2015 publication led by Dr. Clemens Brunner describes some of the analyses and achievements of this project, as well as the emerging Brain-Computer Interface Society. For example, this article reviewed work within this project that further defined BCIs and applications, explored recent trends, discussed ethical issues, and evaluated different directions for new BCIs. As the article notes, their new roadmap generally extends and supports the recommendations from the Future BNCI project managed by Dr. Brendan Allison, which conveys substantial enthusiasm for emerging BCI directions.Other recent publications too have explored future BCI directions for new groups of disabled users (e.g., ). Some prominent examples are summarized below.Disorders of consciousness (DOC) Some persons have a (DOC).
This state is defined to include persons with coma, as well as persons in a vegetative state (VS) or minimally conscious state (MCS). New BCI research seeks to help persons with DOC in different ways.
A key initial goal is to identify patients who are able to perform basic cognitive tasks, which would of course lead to a change in their diagnosis. That is, some persons who are diagnosed with DOC may in fact be able to process information and make important life decisions (such as whether to seek therapy, where to live, and their views on end-of-life decisions regarding them). Some persons who are diagnosed with DOC die as a result of end-of-life decisions, which may be made by family members who sincerely feel this is in the patient's best interests.
Given the new prospect of allowing these patients to provide their views on this decision, there would seem to be a strong ethical pressure to develop this research direction to guarantee that DOC patients are given an opportunity to decide whether they want to live.These and other articles describe new challenges and solutions to use BCI technology to help persons with DOC. One major challenge is that these patients cannot use BCIs based on vision. Hence, new tools rely on auditory and/or vibrotactile stimuli. Patients may wear headphones and/or vibrotactile stimulators placed on the wrists, neck, leg, and/or other locations.
Another challenge is that patients may fade in and out of consciousness, and can only communicate at certain times. This may indeed be a cause of mistaken diagnosis. Some patients may only be able to respond to physicians' requests during a few hours per day (which might not be predictable ahead of time) and thus may have been unresponsive during diagnosis. Therefore, new methods rely on tools that are easy to use in field settings, even without expert help, so family members and other persons without any medical or technical background can still use them.
This reduces the cost, time, need for expertise, and other burdens with DOC assessment. Automated tools can ask simple questions that patients can easily answer, such as 'Is your father named George?' Or 'Were you born in the USA?'
Automated instructions inform patients that they may convey yes or no by (for example) focusing their attention on stimuli on the right vs. This focused attention produces reliable changes in EEG patterns that can help determine that the patient is able to communicate.
The results could be presented to physicians and therapists, which could lead to a revised diagnosis and therapy. In addition, these patients could then be provided with BCI-based communication tools that could help them convey basic needs, adjust bed position and (heating, ventilation, and air conditioning), and otherwise empower them to make major life decisions and communicate. Motor recovery People may lose some of their ability to move due to many causes, such as stroke or injury. 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. 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 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. Clinical trials are currently underway.
Functional brain mapping Each year, about 400,000 people undergo during neurosurgery. This procedure is often required for people with tumors or epilepsy that 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 certain tasks, such as moving fingers or repeating words.
This is necessary so that surgeons can remove only the desired tissue while sparing other regions, such as critical movement or language regions. Removing too much brain tissue can cause permanent damage, while removing too little tissue can leave the underlying condition untreated and require additional neurosurgery. Thus, there is a strong need to improve both methods and systems to map the brain as effectively as possible.In several recent publications, BCI research experts and medical doctors have collaborated to explore new ways to use BCI technology to improve neurosurgical mapping. This work focuses largely on high gamma activity, which is difficult to detect with non-invasive means.
Results have led to improved methods for identifying key areas for movement, language, and other functions. A recent article addressed advances in functional brain mapping and summarizes a workshop. Flexible devices are or other flexible materials (e.g., ) that are printed with; the flexible nature of the background materials allowing the electronics created to bend, and the used to create these devices resembles those used to create and (MEMS).
Flexible electronics were first developed in the 1960s and 1970s, but research interest increased in the mid-2000s. Neural dust.
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