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BRAIN-MACHINE-INTERFACE

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					                      BRAIN MACHINE INTERFACE


                                               critical for the patient to take movements.
              ABSTRACT                         Such a condition can be recovered by this
 “No technology is superior if it              approach.
tends to overrule human faculty.               The main objective of this paper is to
In fact, it should be other way                interface the human and machine, by doing
                                               this several objects can be controlled. This
around”
                                               paper enumerates how Human and Machine
Imagine that you are somewhere else and        can be interfaced and researches undergone
you have to control a machine which is in a    on recovery of paralyzed person in their
remote area, where human can’t withstand       mind.
for a long time. In such a condition we can
move to this BRAIN -MACHINE                                1. INTRODUCTION
INTERFACE. It is similar to robotics but it    The core of this paper is that to operate
is not exactly a robot. In the robot the       machines from a remote area . In the given
interface has a sensor with controller but     BMI DEVELOPMENT SYSTEMS the
here the interface with human and machine.     brain is connected to client interface node
In the present wheel chair movements are       through a neural interface nodes . The client
done according to the patient by controlling   interface node connected to a BMI
the joystick with only up, reverse, left and   SERVER which controls remote ROBOTS
right movements are possible. But if the       through a host control. (fig.1)
patient is a paralyzed person, then it is a
            2. BRAIN STUDY                        By understanding the biological factors that
                                                  control the brain's adaptability.
In the previous research, it has been shown
that a rat wired into an artificial neural        The clinicians could develop improved
system can make a robotic water feeder            drugs and rehabilitation methods for people
move just by willing it. But the latest work      with such damage. The latest work is the
sets new benchmarks because it shows how          first to demonstrate that monkeys can learn
to process more neural information at a           to use only visual feedback and brain
faster speed to produce more sophisticated        signals, without resort to any muscle
robotic movements. That the system can be         movement, to control a mechanical robot
made to work using a primate is also an           arm including both reaching and grasping
important proof of principle.                     movements.

Scientists have used the brain signals from a           3.SIGNAL ANALYSIS USING
monkey to drive a robotic arm. As the                         ELECTRODES
animal stuck out its hand to pick up some
food off a tray, an artificial neural system      A brain-signal recording and analysis
linked into the animal's head mimicked            system that enabled to decipher brain signals
activity in the mechanical limb.                  from monkeys in order to control the
                                                  movement of a robot arm .In the xperiments,
It was an amazing sight to see the robot in       an array of microelectrodes each smaller
my lab move, knowing that it was being            than the diameter of a human hair into the
driven by signals from a monkey brain. It         frontal and parietal lobes of the brains of wo
was as if the monkey had a 600-mile- (950-        female rhesus macaque monkeys. They
km-) long virtual arm. The rhesus monkeys         implanted 96 electrodes in one animal and
consciously controls the movement of a            320 in the other. The researchers reported
robot arm in real time, using only signals        their technology of implanting arrays of
from their brains and visual feedback on a        hundreds of electrodes and recording from
video screen. It is said that the animals         them over long periods. (fig.2)
appeared to operate the robot arm as if it
were their own limb. The technologies
achievement represents an important step
toward technology that could enable
paralyzed        people         to      control
"neuroprosthetic" limbs, and even free-
roaming "neurorobots" using brain signals.
Importantly, the technology that developed
for analyzing brain signals from behaving
animals could also greatly improve
rehabilitation of people with brain and spinal
cord damage from stroke, disease or trauma.
                                                                        1.Monkey Experiment
]]]]]]]]]
                                                  1.Monkey Experiment:

                                                                                      The goal
                                                                             of


                        Fig:2. Signal analysis using electrodes

    The frontal and parietal areas of the brain are chosen because they are known to be
    involved in producing multiple output commands to control complex muscle movement.
    (Fig:3)




                              Fig 3 Placement of electrodes
The faint signals from the electrode arrays       Monkey Experiment:
were detected and analyzed by the computer
system and developed to recognize patterns        The goal of the project is to control a
of signals that represented particular            hexapod robot (RHEX) using neural signals
movements by an animal's arm.                     from monkeys at remote location. To
                                                  explore the optimal mapping of cortical
4.EXPERIMENTS                                     signals to Rhex’s movement parameters, a
                                                  model of Rhex’s movements has been
The experiments conducted for Brain-              generated and human arm control is used to
Machine Interface are:                            approximate cortical control. In preliminary
                                                  investigations, the objective was to explore
different possible mappings or control          After a series of psychometric tests on
strategies for Rhex. Both kinematic             human volunteers, the strategy of controlling
(position, velocity) and dynamic (force,        a model of Rhex depicted above using the
torque) mappings from hand space were           human hand was determined to be the
explored and optimal control strategies were    easiest to use and fastest to learn. The
determined. These mappings will be tested       flexion/extension of the wrist is mapped to
in the next phases of the experiment to         angular velocity and the linear translation of
ascertain the maximal control capabilities of   the hand is mapped to linear (fore/aft)
prefrontal and parietal cortices.               velocity. The monkeys are being trained to
                                                use this technique to control a virtual model
In the initial, output signals from the         of Rhex (fig:5).
monkeys' brains were analyzed and recorded
as the animals were taught to use a joystick
to both position a cursor over a target on a
video screen and to grasp the joystick with a
specified force. After the animal’s initial
training, however the cursor was made a
simple display – now incorporating into its
movement the dynamics, such as inertia and
momentum, of a robot arm functioning in
another room. While the animal’s
performance initially declined when the
robot arm was included in the feedback
loop, they quickly learned to allow for these
dynamics and became proficient in
manipulating the robot-reflecting cursor The
joystick was then removed, after which the
monkeys continued to move their arms in
mid-air to manipulate and "grab" the cursor,     Fig:5;Robotic-arm-movements
thus controlling the robot arm(fig.4).
                                                The most amazing result, though, was that
                                                after only a few days of playing with the
                                                robot in this way, the monkey suddenly
                                                realized that it didn't need to move her arm
                                                at all. "The arm muscles went completely
                                                quiet, it kept the arm at side and controlled
                                                the robot arm using only its brain and visual
                                                feedback.

                                                Our analyses of the brain signals showed
                                                that the animal learned to assimilate the
                                                robot arm into her brain as if it was her own
                                                arm." Importantly the experiments included
       fig : 4 Hand movement
both reaching and grasping movements, but        The finding that their brain-machine
derived from the same sets of electrodes.        interface system can work in animals will
                                                 have direct application to clinical
The neurons from which we were recording         development of neuroprosthetic devices for
could encode different kinds of information.     paralyzed people.
It was surprised to see that the animal can
learn to time the activity of the neurons to     There is certainly a great deal of science and
basically control different types of             engineering to be done to develop this
parameters sequentially. For example, after      technology and to create systems that can be
using a group of neurons to move the robot       used safely in humans. However, the results
to a certain point, these same cells would       so far lead us to believe that these brain-
then produce the force output that the           machine interfaces hold enormous promise
animals need to hold an object.                  for restoring function to paralyzed people.

Analysis of the signals from the animal’s        The researchers are already conducting
brain as they learned revealed that the brain    preliminary studies of human subjects, in
circuitry was actively reorganizing itself to    which they are performing analysis of brain
adapt.                                           signals to determine whether those signals
                                                 correlate with those seen in the animal
5.ANALYSIS OF OUTPUTS:                           models. They are also exploring techniques
                                                 to increase the longevity of the electrodes
It was extraordinary to see that when we         beyond the two years they have currently
switched the animal from joystick control to     achieved in animal studies. To miniaturize
brain control, the physiological properties of   the components, to create wireless interfaces
the brain cells changed immediately. And         and to develop different grippers, wrists and
when we switched the animal back to              other mechanical components of a
joystick control the very next day, the          neuroprosthetic device.
properties changed again.
                                                 And in their animal studies, proceeding to
Such findings tell us that the brain is so       add an additional source of feedback to the
amazingly adaptable that it can incorporate      system in the form of a small vibrating
an external device into its own 'neuronal        device placed on the animal's side that will
space' as a natural extension of the body ,      tell the animal about another property of the
actually, we see this every day, when we use     robot. Beyond the promise of
any tool, from a pencil to a car. As a part of   neuroprosthetic devices, the technology for
that we incorporate the properties of that       recording and analyzing signals from large
tool into our brain, which makes us              electrode arrays in the brain will offer an
proficient in using it, such findings of brain   unprecedented insight into brain function
plasticity in mature animals and humans are      and plasticity.
in sharp contrast to traditional views that
only in childhood is the brain plastic enough    We have learned in our studies that this
to allow for such adaptation.                    approach will offer important insights into
how the large-scale circuitry of the brain      unit has a BMI development system . The
works .Since we have total control of the       brain    is    connected       to   (i.e.   the
system, for example, we can change the          microelectrodes are connected to the frontal
properties of the robot arm and watch in real   and parietal lobes) client interface through
time how the brain adapts.                      neural interface nodes which in turn is
                                                linked with BMI server which controls the
6.BRAIN MACHINE INTERFACE IN                    host device .
HUMAN BEINGS
                                                In the present wheel chair, movements are
The approach of this paper is to control the    done according to the patient by controlling
operations of a robot by means of an human      the joystick with only up, reverse, left and
brain without any links .                       right movements which are only possible.
                                                But if the patient is a paralyzed person, then
The brain signals are taken by electrodes       it is a critical for the patient to take
from the frontal and parietal lobes .The        movements because he is unable to control
signals are conveyed with means of              the wheel-chair. So this technology is a
electrodes and processed by the unit .The       marvelous gift to help them.
7.CONCLUSION:

Thus this technology is a boon to this world.
By this adaptation many Bio-medical
difficulties can be overtaken and many of
our dreams will come true .


8.BIBLIOGRAPHY:

Bio-medical Engineering by Dr. Dan
Koditschek. Neural Engineering by Karen
Coulter and Rahul Bagdia Neural Networks
by Patrick Davalo and Erick Naim.

				
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