CHAPTER neural interface or a brain-machine interface, is
Introduction to Brain gate System
known as Brain gate converts brain activity into computer commands. A sensor is
implanted on the brain, and electrodes are hooked up to wires that travel to a
pedestal on the scalp. From there, a fiber optic cable carries that brain activity
data to a nearby computer. The brain is “hardwired” with connections, which are
made by billions of neurons that make electricity whenever they are simulated. The
electrical patterns are called brain waves. Neurons act like the wires and
gates in a computer. Brain controls motor function. Motor neurons carry signals
from the central nervous system to the muscles, skin and glands of the body, while
sensory neurons carry signals from those outer parts of the body to the central
The ‘Brain Gate’
contains tiny spikes that will extend down about one millimeter into the brain
after being implanted beneath the skull, monitoring the activity from a small
group of Neurons. It will now be possible for a patient with spinal cord injury
to produce brain signals that relay the intention of moving the paralyzed limbs,
as signals to an implanted sensor, which is then output as electronic impulses.
These impulses enable the user to operate mechanical devices with the help of a
A technique called
neurofeedback uses connecting sensors on the scalp to translate brain waves in
to information a person can learn from. The sensors register different
frequencies of the signals produced in the brain. These changes in the brain
wave patterns indicator whether someone is concentrating or suppressing his
impulses, or whether he is relaxed or tense. Mostly the sensor chip uses around
100 hair thin electrodes which sense the neural signals in the specific area of
the brain. The neural signal is then converted to electrically charged signals.
These signals are then sent and decoded using program. The decoder connects to
an external device and can use the brain signals to control an external device,
such as a robotic arm, a computer cursor, or even a wheelchair.
Fig 1.1 shows Brain
–computer interface (BCI), sometimes called a direct neural interface or a brain-machine
interface, is a direct communication pathway between a human or animal brain and
an external device.
developing the brain gate systems underlying core technology in the neuron port
system to enable improved diagnosis and treatment for a number of neurological
conditions, such as epilepsy and brain trauma. Brain gate will be the first
human device that has been designed to record, filter, and amplify multiple
channels of simultaneously recorded neural activity at a very high spatial and
temporal resolution. When a person becomes paralyzed, neural signal from the
brain no longer reach their designated site of termination. However, the brain
continues to send out these signals although they do not reach their
destination. It is these signals that the brain gate system picks up and they
must be present in order for the system to work.
It is found that people
with long-standing, severe paralysis can generate signals in the area of the
brain responsible for voluntary movement and these signals can be detected,
recorded, routed out of the brain to a computer and converted into actions
enabling a paralyzed patient to perform basic tasks. Scientists are to implant
tiny computer chips in the brains of paralyzed patients which could ‘read their
thoughts’. Brain gate consists of a surgically implanted sensor that records
the activity of dozens of brain cells simultaneously. The system also decodes
these signals in real time to control a computer or other external devices. The
brain gate technology platform was designed to take advantage of the fact that
many patients with motor impairment have an intact brain that can produce
movement commands allowing the brain gate system to create an output signal
directly from the brain, bypassing the route through the nerves to the muscles
that cannot be used in paralyzed people.
Brain gate depends upon
the cyberkinetics. In addition to real-time analysis of neurons patterns to
relay movement, the brain gate array is also capable of recording electrical
data for later analysis.
Sensor implanted in brain’s percental gyrus. 1
Brain gate is a brain
implant system developed by a bio-tech company cyberkinetics in 2003 in
conjunction with the department of neuroscience at brown university. The device
was designed to help those who have lost control of their limps, or other
bodily functions, such as patients with amyotrophic lateral sclerosis (ALS) or
spinal cord injury. The computer chip, which was implanted into the brain,
monitors brain activity in the patient and converts the intention of the user
in to the computer commands 1.
first, rats were implanted with Silicon chip. Signals recorded from the cerebral
cortex of rat to operate computer to carry out the movement. The electrode chip
is attached in brain of rat, and it’s brain thinking to eat the food in front
of it. The converter converts the brain signal into digital signal.
Scientists are to implant tiny computer chips in the brains of paralyzed
patients which could read their thoughts. Brain gate consists of a surgically
implanted sensor that records the activity of dozens of brain cells simultaneously.
The system also decodes these signals in real time to control a computer or
other external devices. The brain gate technology platform was designed to take
advantage of the fact that many patients with motor impairment have an intact
brain that can produce movement commands allowing the brain gate system to create
an output signal directly from the brain, bypassing the route through the nerves
to the muscles that cannot be used in paralyzed people 2.
The basic elements of
1. The chip: A
four-millimeter square silicon chip studded with about 100 hair-thin
microelectrodes is embedded in the primary motor cortex-the region of the brain
responsible for controlling movement.
connector: When somebody thinks‚ move cursor up and left his cortical
neurons fire in a distinctive pattern the signal is transmitted through the
pedestal plug attached to the skull.
converter: The signal travels to an amplifier where it is converted to
optical data and bounced by fiber optic cable to a computer.
4. The computer:
Brain gate learns to associate patterns of brain activity with particular
imagined movements up, down, left, right and to connect those movements to a
5. Sensor: A
device entrenched in the brain that records indications directly related to
imagine limb crusade.
Fig 3.1: Neuro Chip 1
3.2 Brains behind
person thinks of moving the computer cursor. Electrodes on the silicon chip
implanted into the person’s brain detect neural activity from an array of neural
impulses in the brains motor cortex. The impulses transfer from the chip to a
pedestal protruding from the scalp through connection wires. The pedestal,
filters out unwanted signals or noise, and then transfers the signal to an
amplifier. The signal is captured by acquisition system and is sent through a
fiber optic cable to a computer. The computer then translates the signal into
an action, causing the cursor to move.
brain gate system is a neuromotor prosthetic device consisting of an array of
one hundred silicon micro-electrodes; each electrode is 1mm long and thinner
than a human hair. The electrodes are arranged less than half a millimeter
apart on the array, which is attached to a 13cm-long cable ribbon cable
connecting it to a computer.
Brain Gate neural interface system is a proprietary, investigational
Brain-Computer Interface (BCI) that consists of an internal sensor to detect
brain cell activity and external processors that convert these brain signals
into a computer-mediated output under the person’s own control. The sensor is
implanted on the surface of the area of the brain responsible for voluntary
movement, the motor cortex. The electrodes penetrate about 1 mm into the
surface of the brain where they pick up electrical signals known as neural
spiking, the language of the brain from nearby neurons and transmit them
through thin gold wires to a titanium pedestal that protrudes about an inch
above the patient’s scalp. An external cable connects the pedestal to
computers, signal processors and monitors. The technology is able to sense the
electrical activity of many individual neurons at one time the data is
transmitted from the neurons in the brain to computers where it is analyzed and
the thoughts are used to control an external device. Even 20 and 200 times a
second and they work in teams.
reason a BCI works at all is because of the way our brains function. Our brains
are filled with neurons, individual nerve cells connected to one another by
dendrites and axons. Every time we think, move, feel or remember something, our
neurons are at work. That work is carried out by small electric signals that
zip from neuron to neuron as fast as 250 mph .The signals are generated by
differences in electric potential carried by ions on the membrane of each
the paths the signals take are insulated by something called myelin, some of
the electric signal escapes. Scientists can detect those signals, interpret
what they mean and use them to direct a device of some kind. It can also work
the other way around. For example, researchers could figure out what signals
are sent to the brain by the optic nerve when someone sees the color red. They
could rig a camera that would send those exact signals into someone’s brain
whenever the camera saw red, allowing a blind person to “see” without eyes.
3.3 Brain Computer
requires at least four components for its activity, of which first is the sensors
that detects the brain activity, next is a signal processing in order to
translate the acquired signals from the brain activity into commands, then this
information needs to be sent to an application on a device (application
displayed on a monitor) etc. and finally there must be an application interface
to determine how these components interact with each other and the user.
User: The user is the person controlling the device in the BCI
system. The user intentionally modifies his or her brain state in order to
generate control signals that operate the BCI system.
Acquisition: Measuring brain
generated oscillations is one of the main components in any BCI based system.
It reflects the voluntary neural actions generated by user’s current activity. There are three general classes of brain acquisition
methods: invasive, partially invasive and non invasive methods.
Ø Invasive capture: It is introduction of implants into user’s encephalic mass,
directly into the gray matter, providing high quality signal reading; however
it causes great inconvenience and risks to human health.
Ø Partially invasive capture: Implants are placed beneath
the skull without drilling the brain. Despite its lower quality signals, this
signal capture form presents lower risks to health as compared with invasive
Ø Non invasive capture: It enables gathering
information without any implant since sensors are placed on the scalp, fully
external to the body. Noninvasive BCIs are more convenient and easy to
use, and due to technological advancements of current solutions, provide
good quality signal capture.
Extraction: The digitized signals are then subjected to one or more
of a variety of feature extraction procedures, such as spatial filtering,
voltage amplitude measurements, spectral analyses. This analysis extracts the
signal features that encode the user’s messages or commands. BCIs can use
signal features that are in the time domain or the frequency domain. A BCI
could conceivably use both time domain and frequency-domain signal features,
and might thereby improve performance.
Algorithm: The first part of signal
processing simply extracts specific signal features. The next stage, the
translation algorithm, translates these signal features into device commands orders
that carry out the user’s intent. This algorithm might use linear methods or
nonlinear methods. Whatever its nature, each algorithm changes independent
variables into dependent variables.
Device: For most current BCIs, the
output device is a computer screen and the output is the selection of targets,
letters, or icons presented on it.
Fig 3.2: Basic Design Operation Of Any BCI System 2
3.4 Types of BCI Signals
The brain generates an amount of neural activity and five types of signals
because of these activities. These signals are divide into two classes: spikes
and field potentials. Spikes reflect the action potentials of individual
neurons and acquired through microelectrodes implanted by invasive techniques.
Field potentials are measure of combined synaptic, neuronal, and axonal
activity of groups of neurons and can be measured by electroencephalogram (EEG) or
implanted electrodes. The following is the classification of EEG signals based
on their frequencies/bands.
Signal: It is captured within the frequency range of 0.5–3.5 Hz. It tends to be
the highest in amplitude and the slowest waves. It is seen normally in adults in
The frequency of this signals ranges from 3.5 to 7.5 Hz. Theta is linked to inefficiency
and daydreaming. In fact, the very lowest waves of theta represent the fine
line between being awake or in a sleep. However, high levels of theta are considered abnormal
signal frequency ranges from 7.5 to 12 Hz. Hans Berger named the first rhythmic EEG activity he saw,
the “alpha wave”. Range seen in the posterior regions of the head on both
sides, being higher in amplitude on the dominant side. It is brought out by
closing the eyes and by relaxation. Several studies have found a rise in alpha
power after smoking marijuana.
is another brain signal in which its frequency ranges from 12 Hz to about 30
Hz. It is seen usually on both sides in a symmetrical distribution and it is
most evident frontally. Beta waves are often divided into ?1 and ?2 to get more
specific range. The waves are small and fast when resisting or suppressing movement,
or solving a math task. It has been noticed in these cases that there is an
increase of beta activity.
is a signal with frequency range of 31 Hz and up. It reflects the mechanism of
Fig 3.3 :Delta wave sample3
Fig 3.4 : Theta wave sample3
Fig 3.5: Alpha wave sample3
Fig 3.6: Beta wave3
Fig 3.7: Gamma wave3
robotic arm: As
technology continues to develop with machine. The person who is physically handicapped
and not able to do any movement of body parts, so that person can use brain gate
to move the robotic arm.
Control the computer:
The patient is admitted in hospital and the computer is
kept in front of patient and patient can control the electricity of room and
the voice through computer that can useful when patient not feeling comfortable
& want to call the services so that the patient just have to think about
Brain gate as
a part of human body: The robotic
arm is fixed at place of damage arm of human. It can be control by the signal
given brain. The Electrode is fixed into the human brain or on the head.
Signals can be sent to the appropriate motor control nerves in the hands,
bypassing a damaged section of the spinal cord and allowing actual movement of
the human own hands, then depending on the specific algorithm the command is
given to device.
thought into action: The Brain Gate
technology platform was designed to take advantage of the fact that many patients
with motor cortex have a working brain that can produce movement commands. This
may allow the Brain Gate system to create an output signal directly from the
brain, bypassing the route through the nerves to the muscles that cannot be
used in paralyzed people.
interact with computer: Brain Gate
provides an interface with a computer that works immediately, without weeks or
months of training. Brain Gate act as the part of the brain.
power of your brain: The human minds
are much more powerful than generally imagined. Most of human just use only a
limited part of full human brain capacity. By embedding Brain Gate with the
human brain, the work human will be easier. Brain Gate reduces the human effort
and save the time.
read the signal: There are about 100
billion neurons in a human brain. Each neuron is constantly sending and
receiving signals through a complex web of connections. There are chemical
processes involved as well, which EEGs can’t pick up on.
complex: The signal is weak and EEGs
measure tiny voltage potentials. Something as simple as the blinking eyelids of
the subject can generate much stronger signals. But for now, reading brain
signals is like listening to a bad phone connection.
It is very
expensive: There are multiple devices are required. The computer
should have all the software and hardware in working condition. This makes all
the devices very expensive.
Gate technology will integrate the future and a step towards learning to read
signals from brain and use computers and algorithms to translate the signals
into action. It has been proved that people can actually use this system to
switch a television on and off, to control the volume and to control the
robotic hand and wheel chair. The knowledge gained from this work will allow
the development of systems that provide improved communication and
environmental control for paralysis people. The idea of moving robots or
prosthetic devices not by manual control, but by mere “thinking” has been a
fascinated approach. Medical cures are unavailable for many forms of neural and
muscular paralysis. So this idea helps many patients to control the prosthetic
devices of their own by simply thinking about the task.
technology is well supported by the latest fields of Biomedical Instrumentation,
Microelectronics; signal processing, Artificial Neural Networks and Robotics
which has overwhelming developments. All of these developing technologies are
sure to help millions of people in world. Hope these systems will be
effectively implemented for many Bio-medical applications.
1 Manjunatha V
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2 Anju S Pillai
“Enhancement of Brain Gate System”, International
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Miranda, Erica E. Cunha de Miranda, Sarah Gomes Sakamoto “A Survey of Interactive
Systems based on Brain Computer Interfaces” ,
SBC Journal on 3D Interactive Systems, VOL 4, number 1, 2013, pp3-13.
Miglani , Surabhi Gupta, “Brain Computer Interface”, International Journal of Emerging Research in Management and Technology,
VOL. 2, NO.8,August 2013, pp.24-27.
5 Steven G.
Mason, Member, IEEE, and Gary E. Birch, Member, IEEE “A General Framework for
Brain-Computer Interface Design” IEEE
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