Brain–computer interface
Brain–computer interface
Abstract:
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. remarkable
Contents
1.BCI
History
The History of Brain-Computer-Interfaces
(BCI) 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 different 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.
2.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.
3.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
3.1.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 since been achieved by researchers in Japan
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 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.[13]
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.[14]
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]
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
4.Human
BCI research
4.1.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.
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
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.
4.2.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
(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.
4.3.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.
EEG
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
Another research parameter is the type of
waves measured. Birbaumer's later research with Jonathan Wolpaw at New York State University
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 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.[30]
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.[31]
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.[32]
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.
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.
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 Emotiv company has been selling a commercial
video game controller, known as the Epoc, since December 2009. The Epoc uses
electromagnetic sensors.
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
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.[42]
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.
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,[45]
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.
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.
Cell-culture
BCIs
Main article: Cultured neuronal
network
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.
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. The
Caltech chip had room for 16 neurons.
In 2003, a team led by Theodore Berger at the
University of Southern California
Thomas DeMarse at the University of Florida
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.
Important topics in the neuroethical debate
are:
·
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,
·
mind-control,
·
selective enhancement and social
stratification, and
·
communication to the media.
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