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Introduction :- How does the brain control motor function?
The brain is "hardwired" with
connections, which are made by billions of
neurons that make electricity whenever they
are stimulated. The electrical patterns are
called brain waves. Neurons act like the wires
and gates in a computer, gathering and
transmitting electrochemical signals over
distances as far as several feet. The brain
encodes information not by relying on single
neurons, but by spreading it across large
populations of neurons, and by rapidly
adapting to new circumstances.
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 nervous system. Receptors sense
things like chemicals, light, and sound and
encode this information into electrochemical
signals transmitted by the sensory neurons.
And interneurons tie everything together by
connecting the various neurons within the brain
and spinal cord. The part of the brain that
controls motor skills is located at the ear of the
frontal lobe.
How does this communication happen?
Muscles in the body's limbs contain embedded
sensors called muscle spindles that measure the
length and speed of the muscles as they stretch
and contract as you move. Other sensors in the
skin respond to stretching and pressure. Even if
paralysis or disease damages the part of the
brain that processes movement, the brain still
makes neural signals. They're just not being
sent to the arms, hands and legs.
A technique called neuro feedback uses
connecting sensors on the scalp to translate
brain waves into information a person can learn
from. The sensors register different frequencies
of the signals produced in the brain. These
changes in brain wave patterns indicate
whether someone is concentrating or
suppressing his impulses, or whether he is
relaxed or tense.
Human Brain The human brain is the center of the human
nervous system. It has the same general
structure as the brains of other mammals, but is
larger than expected on the basis of body size
among other primates. Estimates for the
number of neurons (nerve cells) in the human
brain range from 80 to 120 billion. Most of the
expansion comes from the cerebral cortex,
especially the frontal lobes, which are
associated with executive functions such as
self-control, planning, reasoning, and abstract
thought. The portion of the cerebral cortex
devoted to vision is also greatly enlarged in
human beings, and several cortical areas play
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specific roles in language, a skill that is unique
to humans.
Despite being protected by the thick bones of
the skull, suspended in cerebrospinal fluid, and
isolated from the bloodstream by the blood–
brain barrier, the human brain is susceptible to
many types of damage and disease. The most
common forms of physical damage are closed
head injuries such as a blow to the head, a
stroke, or poisoning by a variety of chemicals
that can act as neurotoxins. Infection of the
brain, though serious, is rare due to the
biological barriers which protect it. The human
brain is also susceptible to degenerative
disorders, such as Parkinson's disease, multiple
sclerosis, and Alzheimer's disease. A number
of psychiatric conditions, such as schizophrenia
and depression, are thought to be associated
with brain dysfunctions, although the nature of
such brain anomalies is not well understood.
Frontal Lobe
The frontal lobe is an area in the brain of
mammals, located at the front of each cerebral
hemisphere and positioned anterior to (in front
of) the parietal lobe and superior and anterior
to the temporal lobes. It is separated from the
parietal lobe by a space between tissues called
the central sulcus, and from the temporal lobe
by a deep fold called the lateral (Sylvian)
sulcus. The precentral gyrus, forming the
posterior border of the frontal lobe, contains
the primary motor cortex, which controls
voluntary movements of specific body parts.
The frontal lobe contains most of the
dopamine-sensitive neurons in the cerebral
cortex. The dopamine system is associated with
reward, attention, short-term memory tasks,
planning, and motivation. Dopamine tends to
limit and select sensory information arriving
from the thalamus to the fore-brain. A report
from the National Institute of Mental Health
says a gene variant that reduces dopamine
activity in the prefrontal cortex is related to
poorer performance and inefficient functioning
of that brain region during working memory
tasks, and to slightly increased risk for
schizophrenia.
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Parietal Lobe The parietal lobe is a part of the brain
positioned above (superior to) the occipital
lobe and behind (posterior to) the frontal lobe.
The parietal lobe integrates sensory
information from different modalities,
particularly determining spatial sense and
navigation. For example, it comprises
somatosensory cortex and the dorsal stream of
the visual system. This enables regions of the
parietal cortex to map objects perceived
visually into body coordinate positions.
The parietal lobe plays important roles in
integrating sensory information from various
parts of the body, knowledge of numbers and
their relations and in the manipulation of
objects. Portions of the parietal lobe are
involved with visuospatial processing.
Although multisensory in nature, the posterior
parietal cortex is often referred to by vision
scientists as the dorsal stream of vision. This
dorsal stream has been called both the 'where'
stream and the 'how' stream. The posterior
parietal cortex (PPC) receives somatosensory
and/or visual input, which then, through motor
signals, controls movement of the arm, hand, as
well as eye movements.
Occipital Lobe The occipital lobe is the visual processing
center of the mammalian brain containing most
of the anatomical region of the visual cortex.
The primary visual cortex is Brodmann area
17, commonly called V1 (visual one). Human
V1 is located on the medial side of the occipital
lobe within the calcarine sulcus; the full extent
of V1 often continues onto the posterior pole of
the occipital lobe. V1 is often also called striate
cortex because it can be identified by a large
stripe of myelin, the Stria of Gennari. Visually
driven regions outside V1 are called
extrastriate cortex. There are many extrastriate
regions, and these are specialized for different
visual tasks, such as visuospatial processing,
color discrimination and motion perception.
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A significant functional aspect of the occipital
lobe is that it contains the primary visual
cortex.
Retinal sensors convey stimuli through the
optic tracts to the lateral geniculate bodies,
where optic radiations continue to the visual
cortex. Each visual cortex receives raw sensory
information from the outside half of the retina
on the same side of the head and from the
inside half of the retina on the other side of the
head. The cuneus (Brodmann's area 17)
receives visual information from the
contralateral superior retina representing the
inferior visual field. The lingula receives
information from the contralateral inferior
retina representing the superior visual field.
The retinal inputs pass through a "way station"
in the lateral geniculate nucleus of the thalamus
before projecting to the cortex. Cells on the
posterior aspect of the occipital lobes' gray
matter are arranged as a spatial map of the
retinal field. Functional neuroimaging reveals
similar patterns of response in cortical tissue of
the lobes when the retinal fields are exposed to
a strong pattern.
If one occipital lobe is damaged, the result can
be homonomous vision loss from similarly
positioned "field cuts" in each eye. Occipital
lesions can cause visual hallucinations. Lesions
in the parietal-temporal-occipital association
area are associated with color agnosia,
movement agnosia, and agraphia. Damage to
the primary visual cortex which is located on
the surface of the posterior occipital lobe, can
cause blindness due to the holes in the visual
map on the surface of the visual cortex that
resulted from the lesions.
Temporal Lobe The temporal lobe is a region of the cerebral
cortex that is located beneath the Sylvian
fissure on both cerebral hemispheres of the
mammalian brain. The temporal lobe is
involved in auditory perception and is home to
the primary auditory cortex. It is also important
for the processing of semantics in both speech
and vision. The temporal lobe contains the
hippocampus and plays a key role in the
formation of long-term memory.
The superior temporal gyrus includes an area
(within the Sylvian fissure) where auditory
signals from the cochlea (relayed via several
subcortical nuclei) first reach the cerebral
cortex. This part of the cortex (primary
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auditory cortex) is involved in hearing.
Adjacent areas in the superior, posterior and
lateral parts of the temporal lobes are involved
in high-level auditory processing. In humans
this includes speech, for which the left
temporal lobe in particular seems to be
specialized. Wernicke's area, which spans the
region between temporal and parietal lobes,
plays a key role (in tandem with Broca's area,
which is in the frontal lobe). The functions of
the left temporal lobe are not limited to low-
level perception but extend to comprehension,
naming, verbal memory and other language
functions. The underside (ventral) part of the
temporal cortices appear to be involved in
high-level visual processing of complex stimuli
such as faces (fusiform gyrus) and scenes
(parahippocampal gyrus). Anterior parts of this
ventral stream for visual processing are
involved in object perception and recognition.
The medial temporal lobes (near the Sagittal
plane that divides left and right cerebral
hemispheres) are thought to be involved in
episodic/declarative memory. Deep inside the
medial temporal lobes lie the hippocampi,
which are essential for memory function –
particularly the transference from short to long
term memory and control of spatial memory
and behavior. Damage to this area typically
results in anterograde amnesia.
Cerebellum The cerebellum (Latin for little brain) is a
region of the brain that plays an important role
in motor control. It may also be involved in
some cognitive functions such as attention and
language, and in regulating fear and pleasure
responses,[1] but its movement-related
functions are the most solidly established. The
cerebellum does not initiate movement, but it
contributes to coordination, precision, and
accurate timing. It receives input from sensory
systems of the spinal cord and from other parts
of the brain, and integrates these inputs to fine
tune motor activity.[2] Because of this fine-
tuning function, damage to the cerebellum does
not cause paralysis, but instead produces
disorders in fine movement, equilibrium,
posture, and motor learning.[2]
In terms of anatomy, the cerebellum has the
appearance of a separate structure attached to
the bottom of the brain, tucked underneath the
cerebral hemispheres. The surface of the
cerebellum is covered with finely spaced
parallel grooves, in striking contrast to the
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broad irregular convolutions of the cerebral
cortex. These parallel grooves conceal the fact
that the cerebellum is actually a continuous
thin layer of tissue (the cerebellar cortex),
tightly folded in the style of an accordion.
Within this thin layer are several types of
neurons with a highly regular arrangement, the
most important being Purkinje cells and
granule cells. This complex neural network
gives rise to a massive signal-processing
capability, but almost all of its output is
directed to a set of small deep cerebellar nuclei
lying in the interior of the cerebellum.
In addition to its direct role in motor control,
the cerebellum also is necessary for several
types of motor learning, the most notable one
being learning to adjust to changes in
sensorimotor relationships. Several theoretical
models have been developed to explain
sensorimotor calibration in terms of synaptic
plasticity within the cerebellum. Most of them
derive from early models formulated by David
Marr and James Albus, which were motivated
by the observation that each cerebellar Purkinje
cell receives two dramatically different types of
input: On one hand, thousands of inputs from
parallel fibers, each individually very weak; on
the other hand, input from one single climbing
fiber, which is, however, so strong that a single
climbing fiber action potential will reliably
cause a target Purkinje cell to fire a burst of
action potentials. The basic concept of the
Marr-Albus theory is that the climbing fiber
serves as a "teaching signal", which induces a
long-lasting change in the strength of
synchronously activated parallel fiber inputs.
Observations of long-term depression in
parallel fiber inputs have provided support for
theories of this type, but their validity remains
controversial.
Neuron A neuron is an electrically excitable cell that
processes and transmits information by
electrical and chemical signaling. Chemical
signaling occurs via synapses, specialized
connections with other cells. Neurons connect
to each other to form neural networks. Neurons
are the core components of the nervous system,
which includes the brain, spinal cord, and
peripheral ganglia. A number of specialized
types of neurons exist: sensory neurons
respond to touch, sound, light and numerous
other stimuli affecting cells of the sensory
organs that then send signals to the spinal cord
and brain. Motor neurons receive signals from
the brain and spinal cord, cause muscle
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contractions, and affect glands. Interneurons
connect neurons to other neurons within the
same region of the brain or spinal cord.
All neurons are electrically excitable,
maintaining voltage gradients across their
membranes by means of metabolically driven
ion pumps, which combine with ion channels
embedded in the membrane to generate
intracellular-versus-extracellular concentration
differences of ions such as sodium, potassium,
chloride, and calcium. Changes in the cross-
membrane voltage can alter the function of
voltage-dependent ion channels. If the voltage
changes by a large enough amount, an all-or-
none electrochemical pulse called an action
potential is generated, which travels rapidly
along the cell's axon, and activates synaptic
connections with other cells when it arrives.
NEUROPROSTHETIC DEVICE: A neuroprosthetic device known as Braingate
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 the brain activity data
to a nearby computer.
PRINCIPLE: "The principle of operation of the BrainGate
Neural Interface System is that with intact
brain function, neural signals are generated
even though they are not sent to the arms,
hands and legs. These signals are interpreted by
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the System and a cursor is shown to the user on
a computer screen that provides an alternate
"BrainGate pathway". The user can use that
cursor to control the computer, just as a mouse
is used."
BrainGate is a brain implant system developed by the 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 limbs, or other bodily functions, such as patients with amyotrophic lateral sclerosis (ALS) or spinal cord injury. The computer chip, which is implanted into the patient and converts the intention of the user
into computer commands.
NUERO CHIP:
Currently the chip uses 100 hair-thin electrodes
that 'hear' neurons firing in specific areas of the
brain, for example, the area that controls arm
movement. The activity is translated into
electrically charged signals and are then sent
and decoded using a program, which can move
either a robotic arm or a computer cursor.
According to the Cyberkinetics' website, three
patients have been implanted with the
BrainGate system. The company has confirmed
that one patient (Matt Nagle) has a spinal cord
injury, whilst another has advanced ALS.
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In addition to real-time analysis of neuron
patterns to relay movement, the Braingate array
is also capable of recording electrical data for
later analysis. A potential use of this feature
would be for a neurologist to study seizure
patterns in a patient with epilepsy.
Braingate is currently recruiting patients with a
range of neuromuscular and neurodegenerative
conditions for pilot clinical trials in the United
States.
WORKING: Operation of the BCI system is not simply
listening the EEG of user in a way that let’s tap
this EEG in and listen what happens. The user
usually generates some sort of mental activity
pattern that is later detected and classified.
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PREPROCESSING:
The raw EEG signal requires some
preprocessing before the feature extraction.
This preprocessing includes removing
unnecessary frequency bands, averaging the
current brain activity level, transforming the
measured scalp potentials to cortex potentials
and denoising. Frequency bands of the EEG :
. Band
Frequency [Hz]
Amplitude [_V]
Location
Alpha (_) 8-12 10 -150 Occipital/Parietal regions µ-rhythm 9-11 varies Precentral/Postcentral regions Beta (_) 14 -30 25 typically frontal regions Theta (_) 4-7 varies varies Delta (_) <3 varies varies DETECTION:
The detection of the input from the user and
them translating it into an action could be
considered as key part of any BCI system. This
detection means to try to find out these mental
tasks from the EEG signal. It can be done in
time-domain, e.g. by.
comparing amplitudes of the EEG and in
frequency-domain. This involves usually
digital signal processing for sampling and band
pass filtering the signal, then calculating these
time -or frequency domain features and then
classifying them. These classification
algorithms include simple comparison of
amplitudes linear and non-linear equations and
artificial neural networks. By constant
feedback from user to the system and vice
versa, both partners gradually learn more from
each other and improve the overall
performance.
CONTROL:
The final part consists of applying the will of
the user to the used application. The user
chooses an action by controlling his brain
activity, which is then detected and classified
to corresponding action. Feedback is provided
to user by audio-visual means e.g. when typing
with virtual keyboard, letter appears to the
message box etc.
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TRAINING:
The training is the part where the user adapts
to the BCI system. This training begins with
very simple exercises where the user is
familiarized with mental activity which is used
to relay the information to the computer.
Motivation, frustration, fatigue, etc. apply also
here and their effect should be taken into
consideration when planning the training
procedures
BIO FEEDBACK: The definition of the
biofeedback is biological information which is
returned to the source that created it, so that
source can understand it and have control over
it. This biofeedback in BCI systems is usually
provided by visually, e.g. the user sees cursor
moving up or down or letter being selected
from the alphabet.
A boon to the paralyzed -Brain Gate Neural Interface System
The first patient, Matthew Nagle, a 25-year-old
Massachusetts man with a severe spinal cord
injury, has been paralyzed from the neck down
since 2001. Nagle is unable to move his arms
and legs after he was stabbed in the neck.
During 57 sessions, at New England Sinai
Hospital and Rehabilitation Center, Nagle
learned to open simulated e-mail, draw circular
shapes using a paint program on the computer
and play a simple videogame, "neural Pong,"
using only his thoughts. He could change the
channel and adjust the volume on a television,
even while conversing. He was ultimately able
to open and close the fingers of a prosthetic
hand and use a robotic limb to grasp and move
objects. Despite a decline in neural signals after
few months, Nagle remained an active
participant in the trial and continued to aid the
clinical team in producing valuable feedback
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concerning the BrainGate` technology.
NAGLE’S STATEMENT:
“I can't put it into words. It's just—I use my
brain. I just thought it. I said, "Cursor go up to
the top right." And it did, and now I can control
it all over the screen. It will give me a sense of
independence.”
Software Behind Braingate:
System uses algorithms and pattern-matching
techniques to facilitate communication. The
algorithms are written in C, JAVA and
MATLAB. .
Signal processing software algorithms analyze
the electrical activity of neurons and translate it
into control signals for use in various
computer-based applications.
OTHEAPPLICATIONS: OTHE
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Rats implanted with BCIs in Theodore Berger's
experiments. Several laboratories have
managed to record signals from monkey and
rat cerebral cortexes 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. Other research on cats has decoded
visual signals.
Garrett Stanley's 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 Garrett Stanley at
Harvard University 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 recognisable scenes and moving
objects.
In the 1980s, Apostolos Georgopoulos at Johns
Hopkins University found a mathematical
relationship between the (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 (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.
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Diagram of the BCI developed by Miguel
Nicolelis and collegues for use on Rhesus
onkeys
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.
CONCLUSION:
The idea of moving robots or prosthetic
devices not by manual control, but by mere
“thinking” (i.e., the brain activity of human
subjects) has been a fascinated approach.
Medical cures are unavailable for many forms
of neural and muscular paralysis. The enormity
of the deficits caused by paralysis is a strong
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motivation to pursue BMI solutions. So this
idea helps many patients to control the
prosthetic devices of their own by simply
thinking about the task.
This technology is well supported by the latest
fields of Biomedical Instrumentation,
Microelectronics, signal processing, Artificial
Neural Networks and Robotics which has
overwhelming developments. Hope these
systems will be effectively implemented for
many Biomedical applications.