Brain-computer interface: Wikis


Note: Many of our articles have direct quotes from sources you can cite, within the Wikipedia article! This article doesn't yet, but we're working on it! See more info or our list of citable articles.


(Redirected to Brain–computer interface article)

From Wikipedia, the free encyclopedia

A brain–computer interface (BCI), sometimes called a direct neural interface or a brain–machine interface, is a direct communication pathway between a brain and an external device. BCIs are often aimed at assisting, augmenting or repairing human cognitive or sensory-motor functions.

Research on BCIs began in the 1970s at the University of California Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA.[1][2] The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature.

The field of BCI has since blossomed spectacularly, mostly toward neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels.[3] Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-nineties.


BCI versus neuroprosthetics

Neuroprosthetics is an area of neuroscience concerned with neural prostheses—using artificial devices to replace the function of impaired nervous systems or sensory organs. The most widely used neuroprosthetic device is the cochlear implant, which, as of 2006, has been implanted in approximately 100,000 people worldwide.[4]There are also several neuroprosthetic devices that aim to restore vision, including retinal implants.

The differences between BCIs and neuroprosthetics are mostly in the ways the terms are used: neuroprosthetics typically connect the nervous system to a device, whereas BCIs usually connect the brain (or nervous system) with a computer system. Practical neuroprosthetics can be linked to any part of the nervous system—for example, peripheral nerves—while the term "BCI" usually designates a narrower class of systems which interface with the central nervous system.

The terms are sometimes used interchangeably, and for good reason. 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.

Animal BCI research

Rats implanted with BCIs in Theodore Berger's experiments

Several laboratories have managed to record signals from monkey and rat cerebral cortices in order to operate BCIs to carry out movement. Monkeys have navigated computer cursors on screen and commanded robotic arms to perform simple tasks simply by thinking about the task and without any motor output[5]. In May 2008 photographs that showed a monkey operating a robotic arm with its mind at the Pittsburgh University Medical Center were published in a number of well known science journals and magazines.[6] Other research on cats has decoded visual signals.


Early work

The operant conditioning studies of Fetz and colleagues first demonstrated that monkeys could learn to control the deflection of a biofeedback meter arm with neural activity [7]. Such work in the 1970s established that monkeys could quickly learn to voluntarily control the firing rates of individual and multiple neurons in the primary motor cortex if they were rewarded for generating appropriate patterns of neural activity.[8]

Monkey operating a robotic arm with brain–computer interfacing

Studies that developed algorithms to reconstruct movements from motor cortex neurons, which control movement, date back to the 1970s. In the 1980s, Apostolos 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 that monkeys moved their arms (based on a cosine function). He also found that dispersed groups of neurons in different areas of the brain collectively controlled motor commands but was only able to record the firings of neurons in one area at a time because of technical limitations imposed by his equipment.[9]

There has been rapid development in BCIs since the mid-1990s.[10] Several groups have been able to capture complex brain motor centre signals using recordings from neural ensembles (groups of neurons) and use these to control external devices, including research groups led by Richard Andersen, John Donoghue, Phillip Kennedy, Miguel Nicolelis, and Andrew Schwartz.

Prominent research successes

Phillip Kennedy 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, researchers led by Yang Dan at University of California, Berkeley decoded neuronal firings to reproduce images seen by cats. The team used an array of electrodes embedded in the thalamus (which integrates all of the brain’s sensory input) of sharp-eyed cats. Researchers targeted 177 brain cells in the thalamus lateral geniculate nucleus area, which decodes signals from the retina. 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.[11] Similar results in humans have been since then achieved by researchers in Japan (see below).

Miguel Nicolelis 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. Such neural ensembles are said to reduce the variability in output produced by single electrodes, which could make it difficult to operate a BCI.

After conducting initial studies in rats during the 1990s, Nicolelis and his colleagues developed BCIs that decoded brain activity in owl monkeys 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 joystick or reached for food.[12] The BCI operated in real time and could also control a separate robot remotely over Internet protocol. But the monkeys could not see the arm moving and did not receive any feedback, a so-called open-loop BCI.

Diagram of the BCI developed by Miguel Nicolelis and colleagues for use on Rhesus monkeys

Later experiments by Nicolelis using rhesus monkeys, succeeded in closing the feedback loop and reproduced monkey reaching and grasping movements in a robot arm. With their deeply cleft and furrowed brains, rhesus monkeys are considered to be 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.[13][14] The monkeys were later shown the robot directly and learned to control it by viewing its movements. The BCI used velocity predictions to control reaching movements and simultaneously predicted hand gripping force.

Other labs that develop BCIs and algorithms that decode neuron signals include John Donoghue from Brown University, Andrew Schwartz from the University of Pittsburgh and Richard Andersen from Caltech. These researchers were able to produce working BCIs even though they recorded signals from far fewer neurons than Nicolelis (15–30 neurons versus 50–200 neurons).

Donoghue's group reported training rhesus monkeys to use a BCI to track visual targets on a computer screen with or without assistance of a joystick (closed-loop BCI).[15] Schwartz's group created a BCI for three-dimensional tracking in virtual reality and also reproduced BCI control in a robotic arm.[16] The group created headlines when they demonstrated that a monkey could feed itself pieces of zucchini using a robotic arm controlled by the animal's own brain signals.[17][18]

Andersen's group used recordings of premovement activity from the posterior parietal cortex in their BCI, including signals created when experimental animals anticipated receiving a reward.[19]

In addition to predicting kinematic and kinetic parameters of limb movements, BCIs that predict electromyographic or electrical activity of muscles are being developed.[20] Such BCIs could be used to restore mobility in paralyzed limbs by electrically stimulating muscles.

Miguel Nicolelis worked with John Chapin, Johan Wessberg, Mark Laubach, Jose Carmena, Mikhail Lebedev, Antonio Pereira, Jr., Sidarta Ribeiro and other colleagues showed that activity of large neural ensembles can predict arm position. This work made possible creation of brain–machine interfaces — electronic devices that read arm movement intentions and translate them into movements of artificial actuators. Carmena et al. (2003) programmed the neural coding in a brain–machine interface allowed a monkey to control reaching and grasping movements by a robotic arm, and Lebedev et al. (2005) 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.

The biggest impediment of BCI technology at present is the lack of a sensor modality that provides safe, accurate, and robust access to brain signals. It is conceivable or even likely that such a sensor will be developed within the next twenty years. The use of such a sensor should greatly expand the range of communication functions that can be provided using a BCI.

Development and implementation of a Brain–Computer Interface (BCI) system is complex and time consuming. In response to this problem, Dr. Gerwin Schalk has been developing a general-purpose system for BCI research, called BCI2000. BCI2000 has been in development since 2000 in a project led by the Brain–Computer Interface R&D Program at the Wadsworth Center of the New York State Department of Health in Albany, New York, USA.

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 the context of a simple learning task, illumination of transfected cells in the somatosensory cortex influenced the decision making process of freely moving mice.[21]

Human BCI research

Invasive BCIs

Invasive BCI research has targeted repairing damaged sight and providing new functionality to persons with paralysis. Invasive BCIs are implanted directly into the grey matter of the brain during neurosurgery. As they rest 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 become weaker or even lost as the body reacts to a foreign object in the brain.

Jens Naumann, a man with acquired blindness, being interviewed about his vision BCI on CBS's The Early Show

In vision science, direct brain implants have been used to treat non-congenital (acquired) blindness. One of the first scientists to come up with 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 two-ton mainframe, but shrinking electronics and faster computers made his artificial eye more portable and now enable him to perform simple tasks unassisted.[22]

Dummy unit illustrating the design of a BrainGate interface

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, 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 drive slowly around the parking area of the research institute.

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 Emory University in Atlanta 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 ‘locked-in syndrome’ after suffering 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.[23]

Tetraplegic Matt 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 Neurotechnology’s BrainGate chip-implant. Implanted in Nagle’s right precentral gyrus (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.[24]One year later, professor Jonathan Wolpaw received the prize of the Altran Foundation for Innovation to develop a Brain Computer Interface with electrodes located on the surface of the skull, instead of directly in the brain.

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 better 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.

Electrocorticography (ECoG) measures the electrical activity of the brain taken from beneath the skull in a similar way to non-invasive electroencephalography (see below), but the electrodes are embedded in a thin plastic pad that is placed above the cortex, beneath the dura mater.[25] ECoG technologies were first trialed 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 using his ECoG implant.[26] This research indicates that control is rapid, requires minimal training, and may be an ideal tradeoff with regards to signal fidelity and level of invasiveness.

(Note: These electrodes were not implanted in the patients for BCI experiments. Implanting foreign objects into people's brains solely for experimental purposes and without consent would be unethical. The patient was suffering from severe epilepsy and had the electrodes temporarily implanted to help his physicians localize seizure foci; the researchers simply took advantage of this.)

Light Reactive Imaging BCI devices are still in the realm of theory. These would involve implanting a laser inside the skull. The laser would be trained on a single neuron and the neuron's reflectance measured by a separate sensor. When the neuron fires, the laser light pattern and wavelengths it reflects would change slightly. This would allow researchers to monitor single neurons but require less contact with tissue and reduce the risk of scar-tissue build-up.

This signal can be either subdural or epidural, but is not taken from within the brain parenchyma itself. It has not been studied extensively until recently due to the limited access of subjects. Currently, the only manner to acquire the signal for study is through the use of patients requiring invasive monitoring for localization and resection of an epileptogenic focus.

ECoG is a very promising intermediate BCI modality because it has higher spatial resolution, better signal-to-noise ratio, wider frequency range, and lesser training requirements than scalp-recorded EEG, and at the same time has lower technical difficulty, lower clinical risk, and probably superior long-term stability than intracortical single-neuron recording. This feature profile and recent evidence of the high level of control with minimal training requirements shows potential for real world application for people with motor disabilities.

Non-invasive BCIs

As well as invasive experiments, there have also been experiments in humans using non-invasive neuroimaging technologies as interfaces. Signals recorded in this way have been used to power muscle implants and restore partial movement in an experimental volunteer. Although they are easy to wear, non-invasive implants produce poor signal resolution because the skull dampens signals, dispersing and blurring the electromagnetic waves created by the neurons. Although the waves can still be detected it is more difficult to determine the area of the brain that created them or the actions of individual neurons.


Recordings of brainwaves produced by an electroencephalogram

Electroencephalography (EEG) is the most studied potential non-invasive interface, mainly due to its fine temporal resolution, ease of use, portability and low set-up cost. But as well as the technology's susceptibility to noise, another substantial barrier to using EEG as a brain–computer interface is the extensive training required before users can work the technology. For example, in experiments beginning in the mid-1990s, Niels Birbaumer of the University of Tübingen in Germany trained severely 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.[27] (Birbaumer had earlier trained epileptics to prevent impending fits by controlling this low voltage wave.) The experiment saw ten patients trained to move a computer cursor by controlling their brainwaves. The process was slow, requiring more than an hour for patients to write 100 characters with the cursor, while training often took many months.

Another research parameter is the type of waves measured. Birbaumer's later research with Jonathan Wolpaw at New York State University has 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 and this is 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 patients first. By contrast, the biofeedback methods described above require learning to control brainwaves so the resulting brain activity can be detected.

Lawrence Farwell and Emanuel Donchin developed an EEG-based brain–computer interface in the 1980s.[28] Their "mental prosthesis" used the P300 brainwave response to allow subjects, including one paralyzed Locked-In syndrome patient, to communicate words, letters, and simple commands to a computer and thereby to speak through a speech synthesizer driven by the computer. A number of similar devices have been developed since then. In 2000, for example, research by Jessica Bayliss at the University of Rochester showed that volunteers wearing virtual reality helmets could control elements in a virtual world using their P300 EEG readings, including turning lights on and off and bringing a mock-up car to a stop.[29]

In 1999, researchers at Case Western Reserve University led by Hunter Peckham, used 64-electrode EEG skullcap to return limited hand movements to quadriplegic Jim Jatich. As Jatich concentrated on simple but opposite concepts like up and down, his beta-rhythm EEG output was analysed using software to identify patterns in the noise. A basic pattern was identified and used to control a switch: Above average activity was set to on, below average off. As well as enabling Jatich to control a computer cursor the signals were also used to drive the nerve controllers embedded in his hands, restoring some movement.[30]

Electronic neural networks have been deployed which shift the learning phase from the user to the computer. Experiments by scientists at the Fraunhofer Society in 2004 using neural networks led to noticeable improvements within 30 minutes of training.[31]

Experiments by Eduardo Miranda aim to use EEG recordings of mental activity associated with music to allow the disabled to express themselves musically through an encephalophone.[32]

The Emotiv company plans to produce a commercial video game controller (known as the Epoc) in December 21, 2009, which uses electromagnetic sensors.[33]


ATR Labs' reconstruction of human vision using fMRI (top row: original image; bottom row: reconstruction from mean of combined readings)

Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) have both been used successfully as non-invasive BCIs.[34] In a widely reported experiment, fMRI allowed two users being scanned to play Pong in real-time by altering their haemodynamic response or brain blood flow through biofeedback techniques.[35]

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.[36]

More recently, research developed in the Advanced Telecommunications Research (ATR) Computational Neuroscience Laboratories in Kyoto, Japan allowed the scientists to reconstruct images directly from the brain and display them on a computer. The article announcing these achievements was the cover story of the journal Neuron of 10 December 2008,[37]While the early results are limited to black and white images of 10x10 squares (pixels), according to the researchers further development of the technology may make it possible to achieve color images, and even view or record dreams.[38][39]

Commercialization and companies

John Donoghue and fellow researchers founded Cyberkinetics. Now listed on a US stock exchange and known as Cyberkinetic Neurotechnology Inc, the company markets its electrode arrays under the BrainGate product name and has set the development of practical BCIs for humans as its major goal. The BrainGate is based on the Utah Array developed by Dick Normann.

Philip Kennedy founded Neural Signals in 1987 to develop BCIs that would allow paralysed patients to communicate with the outside world and control external devices. As well as an invasive BCI, the company also sells an implant to restore speech. Neural Signals' Brain Communicator BCI device uses glass cones containing microelectrodes coated with proteins to encourage the electrodes to bind to neurons.

Although 16 paying patients were treated using William Dobelle's vision BCI, new implants ceased within a year of Dobelle's death in 2004. A company controlled by Dobelle, Avery Biomedical Devices, and Stony Brook University are continuing development of the implant, which has not yet received Food and Drug Administration approval in the United States for human implantation.[40]

Ambient, at a TI developers conference in early 2008, demoed a product they have in development call The Audeo. The Audeo is being developed to create a human–computer interface for communication without the need of physical motor control or speech production. Using signal processing, unpronounced speech representing the thought of the mind can be translated from intercepted neurological signals.[41]

Mindball is a product developed and commercialized by Interactive Productline in which players compete to control a ball's movement across a table by becoming more relaxed and focused.[42] Interactive Productline is a Swedish company whose objective is to develop and sell easy understandable EEG products that train the ability to relax and focus.[43]

An Austrian company, Guger Technologies[44], g.tec, has been offering Brain Computer Interface systems since 1999. The company provides base BCI models as development platforms for the research community to build upon, including the P300 Speller, Motor Imagery, and mu-rhythm. They commercialized a Steady State Visual Evoked Potiential BCI solution in 2008 with 4 degrees of machine control.

A Spanish company, Starlab, has entered this market in 2009 with a wireless 4-channel system called ENOBIO. Designed for research purposes the system provides a platform for application development. [45]

There are three main consumer-devices commercial-competitors in this area (expected launch date mentioned in brackets) which are going to launch such devices primarily for gaming- and PC-users:

Military applications

The United States military has been exploring applications for BCIs, to enhance troop performance as well as develop systems to interfere with the communications of perceived adversaries.[46] As one report concluded,

The most successful implementation of invasive interfaces has occurred in medical applications in which nerve signals are used as the mechanism for information transfer.[47]

The DARPA budget for the fiscal year 2009 to 2010 includes $4 million for a program named Silent Talk, which aims to "allow user-to-user communication on the battlefield without the use of vocalized speech through analysis of neural signals."[48]A further $4 million was allocated by the Army to the University of California to investigate computer-mediated "synthetic telepathy".[48] The research aims to detect and analyze the word-specific neural signals, using EEG, which occur before speech is vocalized, and to see if the patterns are generalizable.[48] The research is part of a wider $70 million project that began in 2000 which aims to develop hardware capable of adapting to the behavior of its user.[49]

Cell-culture BCIs

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 or neurochips.[50]

World first: Neurochip developed by Caltech researchers Jerome Pine and Michael Maher

Development of the first working neurochip was claimed by a Caltech team led by Jerome Pine and Michael Maher in 1997.[51] The Caltech chip had room for 16 neurons.

In 2003, a team led by Theodore Berger at the University of Southern California started work on a neurochip designed to function as an artificial or prosthetic hippocampus. The neurochip was designed to function in rat brains and is 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.[52]

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.[53] After collection, the cortical neurons were cultured in a petri dish 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 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.

Ethical considerations

There has not been a vigorous debate about the ethical implications of BCIs, even though there are several commercially available systems such as brain pacemakers used to treat neurological conditions, and could theoretically be used to modify other behaviours.

Emory University neuroscience professor Michael Crutcher has expressed concern about BCIs, specifically ear and eye implants: "If only the rich can afford it, it puts everyone else at a disadvantage."[54]

Theme in fiction

The prospect of BCIs and brain implants of all kinds have been important themes in science fiction. See brain implants in fiction and philosophy for a review of this literature.

See also


  1. ^ J. Vidal, "Toward Direct Brain–Computer Communication", in Annual Review of Biophysics and Bioengineering, L.J. Mullins, Ed., Annual Reviews, Inc., Palo Alto, Vol. 2, 1973, pp. 157-180.
  2. ^ J. Vidal, "Real-Time Detection of Brain Events in EEG", in IEEE Proceedings, May 1977, 65-5:633-641.
  3. ^ S. P. Levine, J. E. Huggins, S. L. BeMent, R. K. Kushwaha, L. A. Schuh, M. M. Rohde, E. A. Passaro, D. A. Ross, K. V. Elisevich, and B. J. Smith, "A direct brain interface based on event-related potentials," IEEE Trans Rehabil Eng, vol. 8, pp. 180-5, 2000
  4. ^ Laura Bailey. "University of Michigan News Service". Retrieved February 6, 2006. 
  5. ^ Miguel Nicolelis et al. (2001) Duke neurobiologist has developed system that allows monkeys to control robot arms via brain signals
  6. ^ Baum, Michele (2008=09-06). "Monkey Uses Brain Power to Feed Itself With Robotic Arm". Pitt Chronicle. Retrieved 2009-07-06. 
  7. ^ Fetz E E 1969 Operant conditioning of cortical unit activity Science 163: 955-958
  8. ^ Schmidt E M et al. 1978 Fine control of operantly conditioned firing patterns of cortical neurons Exp. Neurol. 61 349–69
  9. ^ Georgopoulos AP, Lurito JT, Petrides M, Schwartz AB, Massey JT (1989) Mental rotation of the neuronal population vector. Science 243: 234-236
  10. ^ Lebedev MA, Nicolelis MA (2006),Brain–machine interfaces: past, present and future. Trends Neurosci 29: 536-546 Loaded 18 October 2006
  11. ^ G. B. Stanley, F. F. Li, and Y. Dan. Reconstruction of natural scenes from ensemble responses in the LGN, J. Neurosci., 19(18):8036-8042, 1999
  12. ^ Wessberg J, Stambaugh CR, Kralik JD, Beck PD, Laubach M, Chapin JK, Kim J, Biggs SJ, Srinivasan MA, Nicolelis MA. (2000) Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 16: 361-365
  13. ^ Carmena, J.M., Lebedev, M.A., Crist, R.E., O’Doherty, J.E., Santucci, D.M., Dimitrov, D.F., Patil, P.G., Henriquez, C.S., Nicolelis, M.A.L. (2003) Learning to control a brain–machine interface for reaching and grasping by primates. PLoS Biology, 1: 193-208
  14. ^ Lebedev, M.A., Carmena, J.M., O’Doherty, J.E., Zacksenhouse, M., Henriquez, C.S., Principe, J.C., Nicolelis, M.A.L. (2005) Cortical ensemble adaptation to represent actuators controlled by a brain machine interface. J. Neurosci. 25: 4681-4693
  15. ^ Serruya M.D., Hatsopoulos, N.G., Paninski, L., Fellows, M.R., Donoghue, J.P., (2002) Instant neural control of a movement signal. Nature 416: 141-142
  16. ^ Taylor DM, Tillery SI, Schwartz AB (2002) Direct cortical control of 3D neuroprosthetic devices. Science 296: 1829-1832
  17. ^ Pitt team to build on brain-controlled arm, Pittsburgh Tribune Review, 5 September 2006.
  18. ^ YouTube - Monkey controls a robotic arm
  19. ^ Musallam S, Corneil BD, Greger B, Scherberger H, Andersen RA (2004) Cognitive control signals for neural prosthetics. Science 305: 258-262
  20. ^ Santucci, D.M., Kralik, J.D., Lebedev, M.A., Nicolelis, M.A.L. (2005) Frontal and parietal cortical ensembles predict single-trial muscle activity during reaching movements. Eur. J. Neurosci., 22: 1529-1540
  21. ^ Huber et al. (2008) Sparse optical microstimulation in barrel cortex drives learned behaviour in freely moving mice. Nature 2008, 451(7174):61-4.
  22. ^ Vision quest, Wired Magazine, September 2002
  23. ^ Kennedy, P.R., Bakay R.A. (1998) Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport. June 1;9(8):1707-11
  24. ^ Leigh R. Hochberg; Mijail D. Serruya, Gerhard M. Friehs, Jon A. Mukand, Maryam Saleh, Abraham H. Caplan, Almut Branner, David Chen, Richard D. Penn and John P. Donoghue (2006-07-13). "Neuronal ensemble control of prosthetic devices by a human with tetraplegia". Nature 442: 164–171. doi:10.1038/nature04970. 
  25. ^ Serruya MD, Donoghue JP. (2003) Chapter III: Design Principles of a Neuromotor Prosthetic Device in Neuroprosthetics: Theory and Practice, ed. Kenneth W. Horch, Gurpreet S. Dhillon. Imperial College Press.
  26. ^ Teenager moves video icons just by imagination, press release, Washington University in St Louis, 9 October 2006
  27. ^ Just short of telepathy: can you interact with the outside world if you can't even blink an eye?, Psychology Today, May-June 2003
  28. ^ Farwell, L. A. and Donchin, E. (1988)Talking Off The Top Of Your Head: A Mental Prosthesis Utilizing Event-Related Brain Potentials. Electroencephalography and Clinical Neurophysiology, 70: 510-513.
  29. ^ Press release, University of Rochester, 3 May 2000
  30. ^ The Next BrainiacsWired Magazine, August 2001.
  31. ^ Artificial Neural Net Based Signal Processing for Interaction with Peripheral Nervous System. In: Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering. pp. 134-137. March 20-22, 2003.
  32. ^ Mental ways to make music, Cane, Alan, Financial Times, London (UK), 22 April 2005, p12
  33. ^ Emotiv Epoc "brain-wave" PC controller delayed until 2009
  34. ^ Ranganatha Sitaram,Andrea Caria,Ralf Veit,Tilman Gaber,Giuseppina Rota,Andrea Kuebler and Niels Birbaumer(2007)"FMRI Brain–Computer Interface: A Tool for Neuroscientific Research and Treatment"
  35. ^ Mental ping-pong could aid paraplegics, Nature, 27 August 2004
  36. ^ To operate robot only with brain, ATR and Honda develop BMI base technology, Tech-on, 26 May 2006
  37. ^ Miyawaki, Y (10 December 2008), "Decoding the Mind's Eye - Visual Image Reconstruction from Human Brain Activity using a Combination of Multiscale Local Image Decoders", Neuron (Elsevier, Cell Press) 60 (5): 915–929, doi:10.1016/j.neuron.2008.11.004, 
  38. ^ "Scientists extract images directly from brain". 12 December 2008. 
  39. ^ "あなたの夢、映像化できるかも!?". Chunichi Web. 11 December 2008.  (Japanese)
  40. ^ Press release, Stony Brook University Center for Biotechnology, 1 May 2006
  41. ^ Speak Your Mind
  42. ^ Welcome to Mind Ball
  43. ^ Interactive Productline | About us
  44. ^ "Guger Technologies". 
  45. ^ "ENOBIO". 
  46. ^ [1] Noah Shachtman,, 7/09/08.
  47. ^ [2] Commission pentagon report on brain–computer interfaces and neuroplasticity, March 2008.
  48. ^ a b c Drummond, Katie (2009-05-14). "Pentagon Preps Soldier Telepathy Push". Wired Magazine. Retrieved 2009-05-06. 
  49. ^ Noah, Shachtman (2007-03-21). "Pentagon’s PCs Bend to Your Brain". Wired Magazine. Retrieved 2009-06-13. 
  50. ^ Andrea Mazzatenta,Michele Giugliano,Stephane Campidelli,Luca Gambazzi,Luca Businaro,Henry Markram,Maurizio Prato and Laura Ballerini(2007)"Interfacing Neurons with Carbon Nanotubes: Electrical Signal Transfer and Synaptic Stimulation in Cultured Brain Circuits"
  51. ^ Press release, Caltech, 27 October 1997
  52. ^ Coming to a brain near you, Wired News, 22 October 2004
  53. ^ 'Brain' in a dish flies flight simulator, CNN, 4 November 2004
  54. ^ The future of brain-controlled devices -

External links


(for a list of universities see Neural Engineering - Neural Engineering Labs)

Researchers listed in above text



Lectures and videos


Got something to say? Make a comment.
Your name
Your email address