- Brain–computer interface
Neuropsychology Mind and Brain Portal
A brain–computer interface (BCI), sometimes called a direct neural interface or a brain–machine interface (BMI), is a direct communication pathway between the 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. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature.
The field of BCI research and development has since focused primarily on 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. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s.
- 1 History
- 2 BCI versus neuroprosthetics
- 3 Animal BCI research
- 4 Human BCI research
- 5 Cell-culture BCIs
- 6 Ethical considerations
- 7 BCI-based toys
- 8 Fiction or speculation
- 9 See also
- 10 References
- 11 External links
The history of brain–computer interfaces (BCIs) starts with Hans Berger's discovery of the electrical activity of human brain and the development of electroencephalography (EEG). In 1924 Berger was the first one who recorded an EEG from a human brain. By analyzing EEGs, Berger was able to identify waves or rhythms which are present in a brain, as the Alpha Wave (8–12 Hz), also known as Berger's Wave.
Berger's first recording device was very rudimentary. He inserted silver wires under the scalp of his patients. Those were replaced by silver foils which were attached to the patients head by rubber bandages later on.
Berger connected these sensors to a Lippmann capillary electrometer, with disappointing results. More sophisticated measuring devices such as the Siemens double-coil recording galvanometer, 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 brain diseases. EEGs permitted completely new possibilities for the research of Human brain activities.
BCI versus neuroprosthetics
Neuroprosthetics is an area of neuroscience concerned with neural prostheses—using artificial devices to replace the function of impaired nervous systems and brain related problems 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. 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. 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
Several laboratories have managed to record signals from monkey and rat cerebral cortices 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. 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. Other research on cats has decoded visual signals.
The operant conditioning studies of Fetz and colleagues first showed that monkeys could learn to control the deflection of a biofeedback meter arm with neural activity. 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.
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.
There has been rapid development in BCIs since the mid-1990s. 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.
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. Similar results in humans have since been 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. 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.
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. 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). Schwartz's group created a BCI for three-dimensional tracking in virtual reality and also reproduced BCI control in a robotic arm. The group created headlines when they demonstrated that a monkey could feed itself pieces of fruit and marshmallows using a robotic arm controlled by the animal's own brain signals.
In addition to predicting kinematic and kinetic parameters of limb movements, BCIs that predict electromyographic or electrical activity of muscles are being developed. Such BCIs could be used to restore mobility in paralyzed limbs by electrically stimulating muscles.
Miguel Nicolelis et al. 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. 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. 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.
The Annual BCI Award, endowed with 3,000 USD, is an accolade to recognize outstanding and innovative research done in the field of Brain-Computer Interfaces. Each year, a renowned research laboratory is asked to judge the submitted projects and to award the prize. The jury consists of world-leading BCI experts recruited by the awarding laboratory. Cuntai Guan, Kai Keng Ang, Karen Sui Geok Chua, Beng Ti Ang from A*STAR in Singapore with the project "Motor imagery-based Brain-Computer Interface robotic rehabilitation for stroke" won the BCI Award 2010. Moritz Grosse-Wentrup, Bernhard Schölkopf from the Max Planck Institute for Intelligent Systems in Germany with the project "What are the neuro-physiological causes of performance variations in brain-computer interfacing?" won the BCI Award 2011.
Human BCI research
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.
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.
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.
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. 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. 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. 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. 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.
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.
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. (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. 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.
In the early 90s Babak Taheri, at UC DAVIS demonstrated the first single and also multichannel dry active electrode arrays using micro-machining. The single channel dry EEG electrode construction and results were published in 1994. The arrayed electrode was also demonstrated to perform well compared to Ag/AgCl electrodes. The device consisted of four sites of sensors with integrated electronics to reduce noise by impedance matching. The advantages of such electrodes are: (1) no electrolyte used, (2) no skin preparation, (3) significantly reduced sensor size, and (4) compatibility with EEG monitoring systems. The active electrode array is an integrated system made of an array of capacitive sensors with local integrated circuitry housed in a package with batteries to power the circuitry. This level of integration was required to achieve the functional performance obtained by the electrode. The electrode was tested on an electrical test bench and on human subjects in four modalities of EEG activity, namely: (1) spontaneous EEG, (2) sensory event-related potentials, (3) brain stem potentials, and (4) cognitive event-related potentials. The performance of the dry electrode compared favorably with that of the standard wet Ag/AgCl electrodes in terms of skin preparation, no gel requirements (dry), and higher signal-to-noise ratio. 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.
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.
Ramaswamy Palaniappan has pioneered the development of BCI for use as biometrics to identify/authenticate a person. The BCI group at University of Essex has also developed analogue cursor control using thoughts.
The first BCI session with 100% accuracy (based on 80 right hand and 80 left hand movement imaginations) was recorded in 1998 by Christoph Guger. The BCI system used 27 electrodes overlaying the sensorimotor cortex, weighted the electrodes with Common Spatial Patterns, calculated the running variance and used a linear discriminant analysis.
Research is ongoing into military use of BCIs. Since the 1970s DARPA is funding research on this topic.  The current idea is user-to-user communication through analysis of neural signals.  The project "Silent Talk" 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. 
MEG and MRI
Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) have both been used successfully as non-invasive BCIs. 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.
fMRI measurements of haemodynamic responses in real time have also been used to control robot arms with a seven second delay between thought and movement.
In 2008, research developed in the Advanced Telecommunications Research (ATR) Computational Neuroscience Laboratories in Kyoto, Japan allowed 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, 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.
In 2011, researchers from UC Berkeley 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 YouTube 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.
Commercialization and companies
John Donoghue and fellow researchers founded Cyberkinetics. 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.
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.
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. 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.
An Austrian company, Guger Technologies, 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.
There are three main consumer-devices commercial-competitors in this area (launch date mentioned in brackets) which have launched such devices primarily for gaming- and PC-users:
- Neural Impulse Actuator (April, 2008)
- Emotiv Systems (December, 2009)
- NeuroSky (MindSet – June, 2009; Uncle Milton Force Trainer – Fall 2009, Mattel MindFlex – Summer, 2009)
2010 the world's first personal EEG-based spelling system came to the market: intendiX. It works with 8 active EEG electrodes and uses the P300 principle to type on a keyboard like matrix. Besides writing a text the patient can also use the system to trigger an alarm, let the computer speak the written text, print out or copy the text into an e-mail or to send commands to external devices. Recently g.tec developed active dry electrodes that work for P300, SSVEP and motor imagery based BCI systems: g.SAHARA.
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.
Development 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 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.
Thomas DeMarse at the University of Florida used a culture of 25,000 neurons taken from a rat's brain to fly a F-22 fighter jet aircraft simulator. After collection, the cortical neurons were cultured in a petri dish and 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.
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.
- obtaining informed consent from people who have difficulty communicating,
- risk/benefit analysis,
- shared responsibility of BCI teams (e.g. how to ensure that responsible group decisions can be made),
- the consequences of BCI technology for the quality of life of patients and their families,
- side-effects (e.g. neurofeedback of sensorimotor rhythm training is reported to affect sleep quality),
- personal responsibility and its possible constraints (e.g. who is responsible for erroneous actions with a neuroprosthesis),
- issues concerning personality and personhood and its possible alteration,
- therapeutic applications and their possible exceedance,
- questions of research ethics that arise when progressing from animal experimentation to application in human subjects,
- mind-reading and privacy,
- use of the technology in advanced interrogation techniques by governmental authorities,
- selective enhancement and social stratification, and
- communication to the media.
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." Clausen concluded 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.
Researchers are well aware that sound ethical guidelines, appropriately moderated enthusiasm in media coverage and education about BCI systems will be of utmost importance for the societal acceptance of this technology. Thus, recently more effort is made inside the BCI community to create consensus on ethical guidelines for BCI research, development and dissemination.
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 Sony patented a neural interface system allowing radio waves to affect signals in the neural cortex.
- In 2007 NeuroSky released the first affordable consumer based EEG along with the game NeuroBoy. This was also the first large scale EEG device to use dry sensor technology.
- In 2008 OCZ Technology developed device for use in video games relying primarily on electromyography.
- In 2008 the Final Fantasy developer Square Enix announced that it was partnering with NeuroSky to create a game, Judecca.
- In 2009 Mattel partnered with NeuroSky to release the Mindflex, 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 Uncle Milton Industries partnered with NeuroSky to release the Star Wars Force Trainer, a game designed to create the illusion of possessing the force.
- In 2009 Emotiv released the EPOC, a 14 channel EEG device. The EPOC is the first commercial BCI to use dry sensor technology, which can be dampened with a saline solution for a better connection.
Fiction or speculation
- Augmented learning
- Emotiv Systems
- Project Cyborg
- Simulated reality
- Thought identification
- Whole brain emulation
- ^ a b Vidal, JJ (1973). "Toward direct brain-computer communication". Annual review of biophysics and bioengineering 2: 157–80. doi:10.1146/annurev.bb.02.060173.001105. PMID 4583653.
- ^ a b J. Vidal (1977). "Real-Time Detection of Brain Events in EEG". IEEE Proceedings 65: 633–641. doi:10.1109/PROC.1977.10542. http://www.cs.ucla.edu/~vidal/Real_Time_Detection.pdf.
- ^ Levine, SP; Huggins, JE; Bement, SL; Kushwaha, RK; Schuh, LA; Rohde, MM; Passaro, EA; Ross, DA et al. (2000). "A direct brain interface based on event-related potentials.". IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society 8 (2): 180–5. PMID 10896180.
- ^ Laura Bailey. "University of Michigan News Service". http://www.umich.edu/news/index.html?Releases/2006/Feb06/r020606a. Retrieved February 6, 2006.
- ^ Miguel Nicolelis et al. (2001) Duke neurobiologist has developed system that allows monkeys to control robot arms via brain signals
- ^ Baum, Michele (2008=09-06). "Monkey Uses Brain Power to Feed Itself With Robotic Arm". Pitt Chronicle. http://www.chronicle.pitt.edu/?p=1478. Retrieved 2009-07-06.
- ^ Fetz, E. E. (1969). "Operant Conditioning of Cortical Unit Activity". Science 163 (3870): 955. Bibcode 1969Sci...163..955F. doi:10.1126/science.163.3870.955. PMID 4974291.
- ^ Schmidt, EM; McIntosh, JS; Durelli, L; Bak, MJ (1978). "Fine control of operantly conditioned firing patterns of cortical neurons.". Experimental neurology 61 (2): 349–69. doi:10.1016/0014-4886(78)90252-2. PMID 101388.
- ^ Georgopoulos, A.; Lurito, J.; Petrides, M; Schwartz, A.; Massey, J. (1989). "Mental rotation of the neuronal population vector". Science 243 (4888): 234. Bibcode 1989Sci...243..234G. doi:10.1126/science.2911737. PMID 2911737.
- ^ Lebedev, MA; Nicolelis, MA (2006). "Brain-machine interfaces: past, present and future.". Trends in neurosciences 29 (9): 536–46. doi:10.1016/j.tins.2006.07.004. PMID 16859758. http://www.cs.uu.nl/docs/vakken/mmpi/papers/Lebedev%202006.pdf.
- ^ Stanley, GB; Li, FF; Dan, Y (1999). "Reconstruction of natural scenes from ensemble responses in the lateral geniculate nucleus.". Journal of Neuroscience 19 (18): 8036–42. PMID 10479703. http://people.deas.harvard.edu/~gstanley/publications/stanley_dan_1999.pdf.
- ^ Nicolelis, Miguel A. L.; Wessberg, Johan; Stambaugh, Christopher R.; Kralik, Jerald D.; Beck, Pamela D.; Laubach, Mark; Chapin, John K.; Kim, Jung et al. (2000). "Real-time prediction of hand trajectory by ensembles of cortical neurons in primates.". Nature 408 (6810): 361. doi:10.1038/35042582. PMID 11099043.
- ^ a b Carmena, JM; Lebedev, MA; Crist, RE; O'Doherty, JE; Santucci, DM; Dimitrov, DF; Patil, PG; Henriquez, CS et al. (2003). "Learning to control a brain-machine interface for reaching and grasping by primates.". PLoS biology 1 (2): E42. doi:10.1371/journal.pbio.0000042. PMC 261882. PMID 14624244. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=261882.
- ^ a b Lebedev, M. A.; Carmena, JM; O'Doherty, JE; Zacksenhouse, M; Henriquez, CS; Principe, JC; Nicolelis, MA (2005). "Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain-Machine Interface". Journal of Neuroscience 25 (19): 4681. doi:10.1523/JNEUROSCI.4088-04.2005. PMID 15888644.
- ^ Serruya, MD; Hatsopoulos, NG; Paninski, L; Fellows, MR; Donoghue, JP (2002). "Instant neural control of a movement signal.". Nature 416 (6877): 141–2. Bibcode 2002Natur.416..141S. doi:10.1038/416141a. PMID 11894084.
- ^ Taylor, D. M.; Tillery, SI; Schwartz, AB (2002). "Direct Cortical Control of 3D Neuroprosthetic Devices". Science 296 (5574): 1829. Bibcode 2002Sci...296.1829T. doi:10.1126/science.1070291. PMID 12052948.
- ^ Pitt team to build on brain-controlled arm, Pittsburgh Tribune Review, 5 September 2006.
- ^ YouTube – Monkey controls a robotic arm
- ^ Velliste, M; Perel, S; Spalding, MC; Whitford, AS; Schwartz, AB (2008). "Cortical control of a prosthetic arm for self-feeding.". Nature 453: 1098–101. doi:10.1038/nature06996. PMID 18509337. http://www.nature.com/nature/journal/v453/n7198/full/nature06996.html.
- ^ Musallam, S.; Corneil, BD; Greger, B; Scherberger, H; Andersen, RA (2004). "Cognitive Control Signals for Neural Prosthetics". Science 305 (5681): 258. Bibcode 2004Sci...305..258M. doi:10.1126/science.1097938. PMID 15247483.
- ^ Santucci, David M.; Kralik, Jerald D.; Lebedev, Mikhail A.; Nicolelis, Miguel A. L. (2005). "Frontal and parietal cortical ensembles predict single-trial muscle activity during reaching movements in primates". European Journal of Neuroscience 22 (6): 1529. doi:10.1111/j.1460-9568.2005.04320.x. PMID 16190906.
- ^ Huber, D; Petreanu, L; Ghitani, N; Ranade, S; Hromádka, T; Mainen, Z; Svoboda, K (2008). "Sparse optical microstimulation in barrel cortex drives learned behaviour in freely moving mice.". Nature 451 (7174): 61–4. Bibcode 2008Natur.451...61H. doi:10.1038/nature06445. PMID 18094685.
- ^ Vision quest, Wired Magazine, September 2002
- ^ Kennedy, PR; Bakay, RA (1998). "Restoration of neural output from a paralyzed patient by a direct brain connection.". Neuroreport 9 (8): 1707–11. doi:10.1097/00001756-199806010-00007. PMID 9665587.
- ^ 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 (7099): 164–171. Bibcode 2006Natur.442..164H. doi:10.1038/nature04970. PMID 16838014.
- ^ 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.
- ^ Teenager moves video icons just by imagination, press release, Washington University in St Louis, 9 October 2006
- ^ Just short of telepathy: can you interact with the outside world if you can't even blink an eye?, Psychology Today, May–June 2003
- ^ Farwell, LA; Donchin, E (1988). "Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials.". Electroencephalography and clinical neurophysiology 70 (6): 510–23. doi:10.1016/0013-4694(88)90149-6. PMID 2461285.
- ^ Press release, University of Rochester, 3 May 2000
- ^ Taheri, B; Knight, R; Smith, R (1994). "A dry electrode for EEG recording☆". Electroencephalography and Clinical Neurophysiology 90 (5): 376. doi:10.1016/0013-4694(94)90053-1. PMID 7514984.
- ^ Active Micromachined Scalp Electrode Array for Eeg Signal Recording
- ^ The Next BrainiacsWired Magazine, August 2001.
- ^ 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.
- ^ Mental ways to make music, Cane, Alan, Financial Times, London (UK), 22 April 2005, p12
- ^ http://www.timesonline.co.uk/tol/comment/columnists/guest_contributors/article723284.ece
- ^ http://www.gazette-news.co.uk/features/woman/8970998.When_mind_over_matter_has_a_whole_new_meaning
- ^ 
- ^ Emotiv Epoc "brain-wave" PC controller delayed until 2009
- ^ C. Guger, H. Ramoser, G. Pfurtscheller, Real-time analysis with subject-specific spatial patterns. IEEE Trans Rehabil Eng. 2000 Dec;8(4):447-56.
- ^ Drummond, Katie (2009-05-14). "Pentagon Preps Soldier Telepathy Push". Wired Magazine. http://www.wired.com/dangerroom/2009/05/pentagon-preps-soldier-telepathy-push. Retrieved 2009-05-06.
- ^ DARPA (2009-05). "Department of Defense Fiscal Year (FY) 2010 Budget Estimates May 2009". DARPA. http://www.darpa.mil/WorkArea/DownloadAsset.aspx?id=538. Retrieved 2011-07-25.
- ^ 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"
- ^ Mental ping-pong could aid paraplegics, Nature, 27 August 2004
- ^ To operate robot only with brain, ATR and Honda develop BMI base technology, Tech-on, 26 May 2006
- ^ Miyawaki, Y; Uchida, H; Yamashita, O; Sato, MA; Morito, Y; Tanabe, HC; Sadato, N; Kamitani, Y (2008). "Decoding the Mind's Eye – Visual Image Reconstruction from Human Brain Activity using a Combination of Multiscale Local Image Decoders". Neuron 60 (5): 915–929. doi:10.1016/j.neuron.2008.11.004. PMID 19081384.
- ^ "Scientists extract images directly from brain". PinkTentacle.com. 12 December 2008. http://www.pinktentacle.com/2008/12/scientists-extract-images-directly-from-brain/.
- ^ "あなたの夢、映像化できるかも！？". Chunichi Web. 11 December 2008. http://www.chunichi.co.jp/article/national/news/CK2008121102000053.html. (Japanese)
- ^ Nishimoto, Shinji; Vu, An T.; Naselaris, Thomas; Benjamini, Yuval; Yu, Bin; Gallant, Jack L. (22 September 2011). "Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies". Current Biology. doi:10.1016/j.cub.2011.08.031. http://www.cell.com/current-biology/fulltext/S0960-9822(11)00937-7.
- ^ Yam, Philip (22 September 2011). "Breakthrough Could Enable Others to Watch Your Dreams and Memories". Scientific American. http://blogs.scientificamerican.com/observations/2011/09/22/breakthrough-could-enable-others-to-watch-your-dreams-and-memories-video/. Retrieved 25 September 2011.
- ^ "Reconstructing visual experiences from brain activity evoked by natural movies (Project page)". The Gallant Lab at UC Berkeley. https://sites.google.com/site/gallantlabucb/publications/nishimoto-et-al-2011. Retrieved 25 September 2011.
- ^ Yasmin Anwar (22 September 2011). "Scientists use brain imaging to reveal the movies in our mind". UC Berkeley News Center. http://newscenter.berkeley.edu/2011/09/22/brain-movies/. Retrieved 25 September 2011.
- ^ Press release, Stony Brook University Center for Biotechnology, 1 May 2006
- ^ Speak Your Mind
- ^ Welcome to Mind Ball
- ^ Interactive Productline|About us
- ^ "Guger Technologies". http://www.gtec.at.
- ^ "ENOBIO". http://starlab.es/products/enobio.
- ^ Mazzatenta, A.; Giugliano, M.; Campidelli, S.; Gambazzi, L.; Businaro, L.; Markram, H.; Prato, M.; Ballerini, L. (2007). "Interfacing Neurons with Carbon Nanotubes: Electrical Signal Transfer and Synaptic Stimulation in Cultured Brain Circuits". Journal of Neuroscience 27 (26): 6931. doi:10.1523/JNEUROSCI.1051-07.2007. PMID 17596441.
- ^ Press release, Caltech, 27 October 1997
- ^ Coming to a brain near you, Wired News, 22 October 2004
- ^ 'Brain' in a dish flies flight simulator, CNN, 4 November 2004
- ^ a b Clausen, Jens (2009). "Man, machine and in between". Nature 457 (7233): 1080. Bibcode 2009Natur.457.1080C. doi:10.1038/4571080a.
- ^ a b Haselager, Pim; Vlek, Rutger; Hill, Jeremy; Nijboer, Femke (2009). "A note on ethical aspects of BCI". Neural Networks 22: 1352. doi:10.1016/j.neunet.2009.06.046.
- ^ Tamburrini, Guglielmo (2009). "Brain to Computer Communication: Ethical Perspectives on Interaction Models". Neuroethics 2: 137. doi:10.1007/s12152-009-9040-1.
- ^ "The future of brain-controlled devices". CNN. 4 January 2010. http://www.cnn.com/2009/TECH/12/30/brain.controlled.computers/index.html. Retrieved 26 April 2010.
- ^ Nijboer, Femke; Clausen, Jens; Allison, Brendan Z; Haselager, Pim. "Stakeholders’ opinions on ethical issues related to brain-computer interfacing". Neuroethics. doi:10.1007/s12152-011-9132-6.
- ^ "Sony patent neural interface". http://www.wikipatents.com/US-Patent-6729337/method-and-system-for-generating-sensory-data-onto-the-human-neural.
- ^ "Mind Games". The Economist. 2007-03-23. http://www.economist.com/science/displaystory.cfm?story_id=8847846.
- ^ a b c Li, Shan (2010-08-08). "Mind reading is on the market". Los Angeles Times. http://www.latimes.com/business/la-fi-mind-reader-20100808,0,6235181,full.story.
- ^ Brains-on with NeuroSky and Square Enix's Judecca mind-control game
- ^ New games powered by brain waves. Physorg.com. Retrieved on 2010-09-12.
- ^ Snider, Mike (2009-01-07). "Toy trains 'Star Wars' fans to use The Force". USA Today. http://www.usatoday.com/life/lifestyle/2009-01-06-force-trainer-toy_N.htm. Retrieved 2010-05-01.
- ^ "Emotiv Systems Homepage". Emotiv.com. http://emotiv.com/. Retrieved 2009-12-29.
- The open-source Electroencephalography project and Programmable chip version, Sourceforge open source EEG projects
- BCI database, Team PhyPA's public hub for BCI data exchange
- The app that can read your mind: iPhone brainwave detector arrives Daily Mail, UK, January 15, 2011.
- Machine Translates Thoughts into Speech in Real Time PhysOrg.com, by Lisa Zyga, December 21, 2009.
- Monkey Neural Interfacing, Monkeys Consciously Control A Robot Arm Using Only Brain Signals
- The Next Brainiacs Wired Magazine, August 2001, article on Jim Jatich’s implant
- Controlling robots with the mind, Scientific American, 16 September 2002, article on Miguel Nicolelis
- Vision quest, Wired Magazine, September 2002, article on artificial vision
- 'Brain' in a dish flies flight simulator, CNN, 4 November 2004, article on cell-culture BCI
- How to talk when you can't speak, Slate, 10 February 2005, article on using EEG to communicate with minimally conscious patients
- Mind Control, Wired Magazine, March 2005, article on Matt Nagle
- ...a step towards neuron-based functional chips, Biosens Bioelectron, January 2006, academic paper on a cell-culture BCI
- Functional alignment of feedback effects from visual cortex to thalamus Nature Neuroscience 9, 1330–1336 (2006), 17 September 2006, recent advances in decoding LGN visual signals
- Lymnaea stagnalis and the development of neuroelectronic technologies, Journal of Neuroscience Research, 2004, academic paper on a cell-culture BCI
- Evolution of brain-computer interfaces: going beyond classic motor physiology, Journal of Neurosurgery, July 2009, a survey
Lectures and videos
- "BCI P300 speller tutorial. A tutorial on an EEG based P300 speller BCI system.
- "Brain–Computer Interfaces" video lecture by Krishna Shenoy (Stanford University)
- "Brain–Computer Interfaces" video lecture by Brendan Allison (now with the Brain–Computer Interfaces Laboratory at the Technical University of Graz)
- Real-time BCI control videos (Twitter, SecondLife, robot control, spelling, smart home, virtual reality,...)
- BCI Lecture (Lecture 2: BCI) with theory, tasks and solutions for teaching.
- Send an Email using Brain Signals for disables with Neuro-muscular disorders (MS, ALS, CP and Spinal cord injury etc.) – Illustrated using reduce stress approach
- Send a Tweet with Brain activity for patients with Neuro-muscular disorders. (MS, ALS, CP and Spinal cord injury etc.)
Brain–computer interface Technologies Scientific phenomena Disciplines Speculative People Other Category · Commons Neuropsychology Topics Brain functions PeopleArthur Lester Benton · David Bohm · António Damásio · Phineas Gage · Norman Geschwind · Elkhonon Goldberg · Kurt Goldstein · Donald O. Hebb · Kenneth Heilman · Edith Kaplan · Muriel Lezak · Benjamin Libet · Rodolfo Llinás · Alexander Luria · Brenda Milner · Karl H. Pribram · Oliver Sacks · Roger Wolcott Sperry · H. M. · K. C. TestsBender-Gestalt Test · Benton Visual Retention Test · Clinical Dementia Rating · Continuous Performance Task · Glasgow Coma Scale · Hayling and Brixton tests · Johari window · Lexical decision task · Mini-mental state examination · Stroop effect · Wechsler Adult Intelligence Scale · Wisconsin card sorting Mind and Brain Portal Emerging technologies List of emerging technologies · Technology FieldsAmpakine · Cryonics · Full genome sequencing · Genetic engineering (Gene therapy) · Personalized medicine · Regenerative medicine (Stem cell treatments · Tissue engineering) · Robotic surgery · Strategies for Engineered Negligible Senescence · Suspended animation · Synthetic biology (Synthetic genomics) · Whole-body transplant (Head transplant · Isolated brain)Brain–computer
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