BRAIN-
COMPUTER INTERFACE
CONTENTS
v INTRODUCTION
v HISTORY
v BCI VERSUS NEUROPROSTHETICS
v ANIMAL BCI RESEARCH
v HUMAN BCI RESEARCH
v CELL-CULTURE BCIS
v LOW COST BCI-BASED INTERFACE
v COCLUSION
v REFERENCE
INTRODUCTION
Brain-computer interface (BCI) is a direct connection between computer(s) and the human
brain. It is the ultimate in development of human-computer interfaces or HCI.
Recently advances have been made with Brain-Machine Interfaces (BMI).
Currently
research is being conducted the fields of neuroscience and neuroengineering regarding BCI and BMI. Using chips
implanted against the brain that have hundreds of pins less than the width of a
human hair protruding from them and penetrating the cerebral cortex, scientists
are able to read the firings of hundreds of neurons in the brain. The language
of the neural firings is then sent to a computer translator that uses special
algorithms to decode the neural language into computer language. This is then
sent to another computer that receives the translated information and tells the
machine what to do. Applications of this technology range from protheses to
control of robotic UAVs to non-verbal human communication.
As far as
real-world testing of this technology, the majority has been conducted using
rats and monkeys in laboratories. Using the rewards / punishment system
researchers train animals to do a certain task with their bodies, and then,
using the chip, the animal eventually figures out it doesn’t actually have to
do the task, it just has to think the task, and the reward will be received.
There are
other means of reading brain activity than direct neural contact via pins. The
first and most common is electroencephalographies (EEG) where electrodes are
placed against the scalp are used to pick up brain signals. However, this
approach is not nearly as accurate as direct neural contact and can only pickup
blurry, weak readings. And much more accurate non-invasive technology is
magneto encephalography (MEG) but is also more equipment intensive. Using MEG
requires a room filled with super-conducting magnets and giant super-cooling
helium tanks surrounded by shielded walls. This technology, while providing the
speed and accuracy needed for a successful non-invasive BMI, will require
significant improvement of technology in order to be realistic for everyday
use.
What is a Brain-Computer
Interface
A brain-computer interface uses
electrophysiological signals to control remote devices. Most current BCIs are
not invasive. They consist of electrodes applied to the scalp of an individual or worn in an electrode cap such as
the one shown in 1-1 (Left). These electrodes pick up the brain’s electrical
activity (at the microvolt level) and carry it into amplifiers such as the ones
shown in 1-1 (Right). These amplifiers amplify the signal approximately ten thousand
times and then pass the signal via an analog to digital converter to
a computer for processing. The computer processes the
EEG signal and uses it in order to accomplish tasks such as communication and environmental control. BCIs are slow in comparison with normal human actions, because of the complexity and noisiness of the signals used, as well as the time necessary to complete recognition and signal processing.
EEG signal and uses it in order to accomplish tasks such as communication and environmental control. BCIs are slow in comparison with normal human actions, because of the complexity and noisiness of the signals used, as well as the time necessary to complete recognition and signal processing.
The phrase brain-computer interface (BCI)
when taken literally means to interface an individual’s electrophysiological
signals with a computer. A true BCI only uses signals from the brain and as
such must treat eye and muscle movements as artifacts or noise. On the other
hand, a system that uses eye, muscle, or other body potentials mixed with EEG
signals, is a brain-body actuated system.
The BCI system uses oscillatory
electroencephalogram (EEG) signals, recorded during specific mental activity,
as input and provides a control option by its output. The obtained output
signals are presently evaluated for different purposes, such as cursor control,
selection of letters or words, or control of prosthesis. People who are paralyzed or have other severe
movement disorders need alternative methods for communication and control.
Currently available augmentative communication methods require some muscle
control. Whether they use one muscle group to supply the function normally
provided by another (e.g., use extra ocular muscles to drive a speech synthesizer)
.Thus, they may not be useful for those who are totally paralyzed (e.g., by
amyotrophic lateral sclerosis (ALS) or brainstem stroke) or have other severe
motor disabilities. These individuals need an alternative communication channel
that does not depend on muscle control. The current and the most important application of a BCI is the restoration of
communication channel for patients with locked-in-syndrome
HISTORY
The
history of brain–computer interfaces (BCIs) starts with Hans Berger's discovery of the electrical activity
of the human brain and the development of electroencephalography (EEG). In 1924 Berger was the first to
record human brain activity by means of EEG. By analyzing EEG traces, Berger
was able to identify oscillatory activity in the brain, such 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 scalps of his patients. These were later replaced by silver foils
attached to the patients' head by rubber bandages. 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
Narrower
class systems which interface with the central nervous system. The terms are
sometimes, however, 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. Neuroprosthetics
is an area of neuroscience concerned
with neural prostheses. That is, using artificial devices to replace the
function of impaired nervous systems and brain related problems, or of sensory
organs. The most widely used neuroprosthetic device is the cochlear implant which, as of 2006, had been implanted
in approximately 100,000 people worldwide.
The difference between BCIs and neuroprosthetics is mostly in how 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
ANIMAL BCI
RESEARCH
Early Work
A rat
implanted with a BCI as part of Theodore Berger's experiments several
laboratories have managed to record signals from monkey and rat cerebral cortices to operate BCIs to produce movement.
Monkeys have navigated computer on
screen and commanded robotic arms to perform simple tasks simply by thinking
about the task and seeing the visual feedback, but without any motor output. In May 2008 photographs that showed a
monkey at the University
of Pittsburgh Medical Center operating
a robotic arm by thinking were published in a number of well known science
journals and magazines.
Prominent
research successes
In 1969
the operant conditioning Monkey studies of Fetz and colleagues,
at the Regional Primate Research Center and Department of Physiology and
Biophysics, University in Seattle, showed for the first time that
monkeys could learn to control the deflection of a biofeedback meter
arm with neural activity.
Kennedy and Yang Dan
In 1999,
researchers led by Yang Dan at the University
of California, Berkeley decoded
neuronal firings to reproduce images seen by cats. The team used an array of
electrodes embedded in the thalamus of
sharp-eyed cats. Researchers targeted 177 brain cells in the thalamus lateral
geniculate nucleus area,
which decodes signals from the retina.
Nicolelis
Miguel Nicolelis, a professor at Duke University, in Durham, North
Carolina, 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.
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.
Donoghue, Schwartz and Andersen
Other
laboratories which have developed BCIs and algorithms that decode neuron
signals include those run by John Donoghue at Brown University, Andrew Schwartz at the University of
Pittsburgh and Richard
Andersen at Caltech.
Other research
In
addition to predicting kinematic and kinetic parameters
of limb movements, BCIs that predict electromyographic or electrical activity of the muscles
of primates are being developed. Such BCIs could be used to restore mobility in
paralyzed limbs by electrically stimulating muscles.
The BCI Award
The Annual BCI Award, endowed with 3,000 USD, is
awarded in recognition of outstanding and innovative research 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 and Beng Ti Ang, from A*STAR in Singapore, with their project "Motor
imagery-based Brain-Computer Interface robotic rehabilitation for stroke",
won the BCI Award 2010.
HUMAN BCI
RESEARCH
Invasive BCIs
Vision
Invasive
BCI research has targeted repairing damaged sight and providing new
functionality for people 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 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 an
automobile slowly around the parking area of the research institute.
Movement
BCIs
focusing on motor
neuroprosthetics aim to
either restore movement in individuals with paralysis or provide devices to
assist them, such as interfaces with computers or robot arms.
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.
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
Background
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.
Dry
active electrode arrays
In the
early 1990s Babak Taheri, at University
of California, 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.[34] The
arrayed electrode was also demonstrated to perform well compared to Silver/Silver Chloride 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.
Other
research
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.
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
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.
LOW-COST BCI-BASED INTERFACES
Recently a
number of companies have scaled back medical grade EEG technology 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 Mind Flex.
§ 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 Mind flex, 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.
CONCLUSION
Brain
Computer Interface (BCI) technology provides a direct electronic interface and
can convey messages and commands directly from the human brain to a computer.
BCI technology involves monitoring conscious brain electrical activity via
electroencephalogram (EEG) signals and detecting characteristics of EEG
patterns via digital signal processing algorithms that the user generates to
communicate. It has the potential to enable the physically disabled to perform
many activities, thus improving their quality of life and productivity,
allowing them more independence and reducing social costs. The challenge with
BCI, however, is to extract the relevant patterns from the EEG signals produced
by the brain each second. Recently, there has been a great progress in the
development of novel paradigms for EEG signal recording, advanced methods for
processing them, new applications for BCI systems and complete software and
hardware packages used for BCI applications. In this book a few recent advances
in these areas are discussed
REFERENCES
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Vidal, JJ (1973). "Toward direct
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review of biophysics
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"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.
5.
Miguel
Nicolelis et al. (2001) Duke neurobiologist has developed system that allows
monkeys to control robot arms via brain signals
7. Fetz, E.
E. (1969). "Operant Conditioning of Cortical Unit Activity". Science 163 (3870): Schmidt,
EM; McIntosh, JS; Durelli, L; Bak, MJ (1978). "Fine control of operantly
conditioned firing patterns of cortical neurons". Experimental
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