Brain-Computer Interface (BCI)-Seminar Report

Seminar Report
BRAIN COMPUTER INTERFACE
Submitted By
JOSNA PV
.

Seminar Report 2020-21 Brain Computer Interface
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Dept.Computer Engineering
ABSTRACT
The human brain is of the size of a deflated volleyball which weighs
about 3 pounds. We live at a time when the disabled are on the leading
edge of a broader societal trend toward the use of assistive technology
known as Brain Computer Interface. Brain-computer interface (BCI) is a
collaboration between a brain and a device that enables signals from the
brain to direct some external activity, such as control of a cursor or a
prosthetic limb.
The interface enables a direct communications pathway between the
brain and the object to be controlled with the advent of miniature wireless
tech, electronic gadgets have stepped up the invasion of the body through
innovative techniques.
Firstly this paper deals with as to how this mechanism is supported
by the brain. In the later sections describes its applications, current
research on this technique, real life examples and concluding it with its
advantages and drawbacks.

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CONTENTS
1. Introduction……………………………………………………………………..6
2. What is Brain Computer Interface (BCI)? …………………………………...7
3. Working of BCI…………………………………………………………………8
4. BCI Types……………………………………………………………………….11
4.1. Introduction…………………………………………………………………..11
4.2. Invasive BCI…………………………………………………………………11
4.3. Partially invasive BCIs………………………………………………………13
4.4. Non-invasive BCIs…………………………………………………………..14
5. Animal BCI …………………………………………………………………….15
5.1. Early work……………………………………………………………………15
5.2. Prominent research successes………………………………………………..16
6. The Current BCI Techniques………………………………………………….19
6.1. EEG……………………………………………………………………….....19
6.2. MEG and fMRI………………………………………………………………21
7. BCI Applications……………………………………………………………….23
7.1. Device control……………………………………………………………….23
7.2. User-state monitoring ……………………………………………………….23
7.3. Evaluation……………………………………………………………………23
7.4. Training and education……………………………………………………....24
7.5. Gaming and entertainment…………………………………………………..24
7.6. Cognitive improvement……………………………………………………...24
7.7. Safety and security…………………………………………………………..24
8. BCI Challenges and Innovators……………………………………………….26
8.1. Challenges…………………………………………………………………...26
8.2. Innovators……………………………………………………………………26
9. Advantages and Disadvantages of BCI ……………………………………….28
9.1. Advantages…………………………………………………………………..28
9.2. Disadvantages………………………………………………………………..28
10. Conclusion……………………………………………………………………....29

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1.INTRODUCTION
A Brain-Computer Interface (BCI) provides a new communication channel between
the human brain and the computer. The100 billion neurons communicate via minute
electrochemical impulses, shifting patterns sparking like fireflies on a summer
evening, that produce movement, expression, words. Mental activity leads to
changes of electrophysiological signals.
The BCI system detects such changes and transforms it into a control signal .
In the caseof cursor control, for example, the signal is transmitted directly from the
brain to the mechanism directing the cursor, rather than taking the normal route
through the body's neuromuscular system from the brain to the finger on a mouse.
By reading signals from an array of neurons and using computer chips and
programs to translate the signals into action, BCI can enable a person suffering from
paralysis to write a book or control a motorized wheelchair or prosthetic limb
through thought alone Many physiological disorders such as Amyotrophic Lateral
Sclerosis (ALS) or injuries such as high-level spinal cord injury can disrupt the
communication path between the brain and the body. This is where brain computer
interface comes into play contributing for beneficial real time services and
applications.

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2.WHAT IS BRAIN COMPUTER INTERFACE
(BCI)?
The Wonder Machine – Human Brain
The 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 neuron.
Although 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.
Basic block diagram of a BCI system incorporating signal detection, processing and deployment

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3.WORKING OF BCI
One of the biggest challenges facing brain-computer interface researchers
today is the basic mechanics of the interface itself. The easiest and least invasive
method is a set of electrodes -- a device known as an electroencephalograph (EEG)
-- attached to the scalp. The electrodes can read brain signals. However, the skull
blocks a lot of the electrical signal, and it distorts what does get through.
To get a higher-resolution signal, scientists can implant electrodes directly
into the gray matter of the brain itself, or on the surface of the brain, beneath the
skull. This allows for much more direct reception of electric signals and allows
electrode placement in the specific area of the brain where the appropriate signals
are generated. This approach has many problems, however. It requires invasive
surgery to implant the electrodes, and devices left in the brain long-term tend to
cause the formation of scar tissue in the gray matter. This scar tissue ultimately
blocks signals.
Regardless of the location of the electrodes, the basic mechanism is the same:
The electrodes measure minute differences in the voltage between neurons. The
signal is then amplified and filtered. In current BCI systems, it is then interpreted by
a computer program, although you might be familiar with older analogue
encephalographs, which displayed the signals via pens that automatically wrote out
the patterns on a continuous sheet of paper.
In the case of a sensory input BCI, the function happens in reverse. A
computer converts a signal, such as one from a video camera, into the voltages
necessary to trigger neurons. The signals are sent to an implant in the properarea of
the brain, and if everything works correctly, the neurons fire and the subject receives
a visual image corresponding to what the camera sees.
Another way to measure brain activity is with a Magnetic Resonance Image
(MRI). An MRI machine is a massive, complicated device. It produces very high-
resolution images of brain activity, but it can't be used as part of a permanent or

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semipermanent BCI. Researchers use it to get benchmarks forcertain brain functions
or to map where in the brain electrodes should be placed to measure a specific
function. For example, if researchers are attempting to implant electrodes that will
allow someone to control a robotic arm with their thoughts, they might first put the
subject into an MRI and ask him or her to think about moving their actual arm. The
MRI will show which area of the brain is active during arm movement, giving them
a clearer target for electrode placement.
A similar method is used to manipulate a computer cursor, with the subject
thinking about forward, left, right and back movements of the cursor. With enough
practice, users can gain enough controlover a cursorto draw a circle, access
computer programs and control a TV.
It could theoretically be expanded to allow users to "type" with their
thoughts.
Once the basic mechanism of converting thoughts to computerized or
robotic action is perfected, the potential uses for the technology are almost

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limitless. Instead of a robotic hand, disabled users could have robotic braces
attached to their own limbs, allowing them to move and directly interact with the
environment. This could even be accomplished without the "robotic" part of the
device. Signals could be sent to the appropriate motor controlnerves in the hands,
bypassing a damaged section of the spinal cord and allowing actual movement of
the subject's own hands.

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4.BCI TYPES
4.1.Introduction:
Before moving to real implications of BCI and its application let us first
discuss the three types of BCI. These types are decided on the basis of the
technique used for the interface. Each of these techniques has some advantages as
well as some disadvantages. The three types of BCI are as follows with there
features:
4.2.Invasive BCIs:
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. Because they lie 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 non-
existent, as the body reacts to a foreign object in the brain.
In vision science, direct brain implants have been used to treat non-congenital
(acquired) blindness. One of the first scientists to producea 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 cortexand succeeded in producingphosphenes, 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 mainframe
computer, but shrinking electronics and faster computers made his artificial eye
more portable and now enable him to perform simple tasks unassisted.
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

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sophisticated implant enabling better mapping of phosphenes into coherent vision.
Phosphenes are spread outacross 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 ofthe research
institute. Unfortunately, Dr. Dobelle died in 2004 before his processes and
developments were documented. Subsequently, when Mr. Naumann and the other
patients in the program began having problems with their vision, there was no relief
and they eventually losttheir "sight" again. Mr. Naumann wrote abouthis experience
with Dr. Dobelle's work in Search for Paradise: A Patient's Account of the Artificial
Vision Experiment and has returned to his farm in Southeast Ontario, Canada, to
resume his normal activities.
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, led by Philip Kennedy and Roy
Bakay, were first to install a brain implant in a human that produced signals of high
enough quality to simulate movement. Their patient, Johnny Ray (1944–2002),
suffered from ‘locked-in syndrome’ after suffering a brain-stem stroke in 1997.
Ray’s implant was installed in 1998 and he lived long enough to start working with
the implant, eventually learning to control a computer cursor; he died in 2002 of a
brain aneurysm.
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’s
BrainGate chip-implant. Implanted in Nagle’s right precentral gyrus (area of the
motorcortex 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.One year later, professorJonathan 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. More
recently, research teams led by the Braingate group at Brown University and a group
led by University of Pittsburgh Medical Center, both in collaborations with the
United States Department of Veterans Affairs, have demonstrated further success in

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direct controlof robotic prosthetic limbs with many degrees of freedom using direct
connections to arrays of neurons in the motor cortex of patients with tetraplegia.
Dummy unit illustrating the design of a BrainGate interface
4.3.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.ECoG
technologies were first trialed in humans in 2004 by Eric Leuthardt and Daniel
Moran from Washington University in St Louis. In a later trial, the researchers
enabled a teenage boyto play SpaceInvaders using his ECoG implant.This research
indicates that controlis rapid, requires minimal training, and may bean ideal tradeoff
with regards to signal fidelity and level of invasiveness.
(Note: these electrodes had not beenimplanted in the patient with the intention
of developing a BCI. The patient had been suffering from severe epilepsy and the
electrodes were temporarily implanted to help his physicians localize seizure foci;
the BCI researchers simply took advantage of this.)

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Signals can be either subdural or epidural, but are not taken from within the
brain parenchyma itself. It has not been studied extensively until recently due to the
limited access ofsubjects. 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 less
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 peoplewith motordisabilities.Light Reactive Imaging BCI
devices are still in the realm oftheory. Thesewould 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.
4.4.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.

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5.ANIMAL BCI
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.
Rats implanted with BCIs in Theodore Berger's experiments
5.1.Earlywork:
Studies that developed algorithms to reconstructmovements from motor
cortex neurons, which control movement, date back to the 1970s. Work by groups
led by Schmidt, Fetz and Baker in the 1970s established that monkeys could
quickly learn to voluntarily control the firing rate of individual neurons in the
primary motor cortex after closed-loop operant conditioning, a training method
using punishment and rewards.
In the 1980s, Apostolos Georgopoulos at Johns Hopkins University found a
mathematical relationship between the electrical responses ofsingle motor-cortex
neurons in rhesus macaque monkeys and the direction that monkeys moved their
arms (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 becauseof technical
limitations imposed by his equipment.

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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 neural ensembles (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.
5.2.Prominentresearchsuccesses:
Phillip Kennedy and colleagues built the first intracortical brain-computer
interface by implanting neurotrophic-cone electrodes into monkeys.
.
Monkey operating a robotic arm with brain–computer interfacing (Schwartz lab, University of Pittsburgh)
In 1999, researchers led by Garrett Stanley at Harvard University decoded
neuronal firings to reproduceimages 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.
Miguel Nicolelis has been a prominent proponentof 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.

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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.The
BCI operated in real time and could also control a separate robotremotely 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
robotarm. 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 robotarm were
hidden. The monkeys were later shown the robotdirectly 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 decodeneuron signals include John Donoghue
from Brown University, Andrew Schwartz from the University of Pittsburgh and
Richard Andersen from Caltech. These researchers were able to produceworking

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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. The group created
headlines when they demonstrated that a monkey could feed itself pieces of
zucchini using a robotic arm powered by the animal's own brain signals.
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. In addition to predicting kinematic and
kinetic parameters of limb movements, BCIs that predict electromyographic or
electrical activity of muscles are being developed.Such BCIs could be used to
restore mobility in paralysed limbs by electrically stimulating muscles.

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6.THE CURRENT BCI TECHNIQUES
6.1.EEG:
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. The technology is highly susceptibility to noise however. Another
substantial barrier to using EEG as a brain–computer interface is the extensive
training required before users can work the technology. Forexample, in experiments
beginning in the mid-1990s, Niels Birbaumer at the University of Tübingen in
Germany trained severely paralysed people to self-regulate the slow cortical
potentials in their EEG to suchan extent that these signals could be used as a binary
signal to control a computer cursor.(Birbaumer had earlier trained epileptics to
prevent impending fits by controlling this low voltage wave.) The experiment saw
ten patients trained to move a computer cursorby controlling their brainwaves. The
process was slow, requiring more than an hour for patients to write 100 characters
with the cursor, while training often took many months.
Another research parameter is the type of oscillatory activity that is measured.
Birbaumer's later research with Jonathan Wolpaw at New York State University has
focused ondeveloping technology that would allow users to choosethe brain signals
they found easiest to operate a BCI, including mu and beta rhythms.
Recordings of brainwaves produced by an electroencephalogram
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

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(stimulus-feedback) when peoplesee 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 controlbrainwaves so the
resulting brain activity can be detected.
Lawrence Farwell and Emanuel Donchin developed an EEG-based brain–
computer interface in the 1980s.Their "mental prosthesis" used the P300 brainwave
responseto 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 Rochestershowed 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.
While an EEG based brain-computer interface has been pursued extensively
by a number of research labs, recent advancements made by Bin He and his team at
the University of Minnesota suggest the potential of an EEG based brain-computer
interface to accomplish tasks close to invasive brain-computer interface. Using
advanced functional neuroimaging including BOLD functional MRI and EEG
source imaging, Bin He and co-workers identified the co-variation and co-
localization of electrophysiological and hemodynamic signals induced by motor
imagination. Refined by a neuroimaging approach and by a training protocol, Bin
He and co-workers demonstrated the ability of a non-invasive EEG based brain-
computerinterface to controlthe flight ofa virtual helicopter in 3-dimensional space,
based upon motor imagination. In June 2013 it was announced that Bin He had
developed the technique to enable a remote-control helicopter to be guided through
an obstacle course.
In addition to a brain-computer interface based on brain waves, as recorded
from scalp EEG electrodes, Bin He and co-workers explored a virtual EEG signal-
based brain-computer interface by first solving the EEG inverse problem and then
used the resulting virtual EEG for brain-computer interface tasks. Well-controlled
studies suggested the merits of such a source analysis based brain-computer
interface.

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6.2.MEG and fMRI
Magnetoencephalography (MEG) and functional magnetic resonanceimaging
(fMRI) have both been used successfully as non-invasive BCIs. 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.
In 2008 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 in black and white at a resolution of 10x10 pixels. The article announcing
these achievements was the cover storyof the journal Neuron of 10 December 2008.
ATR Labs' reconstruction of human vision using fMRI
(top row: original image; bottom row: reconstruction from mean of combined readings)
In 2011 researchers from UC Berkeley published a study reporting second-by-
second reconstruction of videos watched by the study's subjects, from fMRI data.
This was achieved by creating a statistical model relating visual patterns in videos

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shown to the subjects, to the brain activity caused by watching the videos. This
model was then used to look up the 100 one-second video segments, in a database
of 18 million seconds of random YouTube videos, whose visual patterns most
closely matched the brain activity recorded when subjects watched a new video.
These 100 one-second video extracts were then combined into a mashed-up image
that resembled the video being watched.

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7.BCI APPLICATION
7.1.Device control:
Research on BCIs to assist users lacking full limb development has matured
to the point that such users are already benefiting, even though the devices offer
limited speed, accuracy, and efficiency.
Nonmedical device control is more problematic. Users with full muscular
control cannot benefit as easily because a BCI lacks the bandwidth and accuracy to
compete with a standard input device, such as a mouse or keyboard. Introducing a
shared control scheme would enable the user to give high-level, open-loop
commands while the device takes care of low-level control.
Additional control channels or hands-free control could benefit users such as
drivers, divers, and astronauts, who must keep their hands on controls to operate
equipment. Brain-based control paradigms could supplement other forms of hands-
free control, such as a voice command or eye movement.
7.2.User-state monitoring:
Future interfaces must beable to understand and anticipate the user's state and
intentions. Automobiles could alert sleepy drivers, or virtual humans could convince
users to stick to their diet.
BCIs might also be useful in neuroscientific research. Because they can
monitor the acting brain in real time and in the real world, BCIs could help scientists
understand the role of functional networks during behavioral tasks.
7.3.Evaluation:
Evaluation applications can be either online or offline. The former
continuously provide evaluations, in real or near real time; the latter provide
evaluations only once, after the experimental study is finished. Neuroergonomics
and neuromarketing are two application subareas.

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7.4.Training and education:
Most training aspects relate to the brain and its plasticity - the brain's ability
to change, grow, and remap itself. Measuring plasticity can help improve training
methods and individual training regimens.
7.5.Gaming and entertainment:
Over the pastfew years, companies such as Neurosky, Emotiv, Uncle Milton,
Mind Games, and Mattel have released numerous products. Most developers are
convinced that BCIs will enrich the gaming and entertainment experience in games
tailored to the user's affective state - immersion, flow, frustration, surprise, and so
on.
7.6.Cognitive improvement:
A common nonmedical application involving a BCI is neurofeedback
training, in which operant conditioning alters brain activity to improve attention,
working memory, and executive functions.
The line between medical and nonmedical neurofeedback applications is
likely to be thin, but a nonmedical application might be the optimized presentation
of learning content.
7.7.Safety and security:
Safety and security EEG alone or combined EEG and eye movement data
from expert observers could supportthedetection of deviant behavior and suspicious
objects. Also, image inspection might be faster than is possible with current
methods.
There are several categories of technological challenges that need to be met if
wider use of BCIs are to find wide acceptance in non-medical (or perhaps more
broadly, non-assistive-care) applications. One key area is usability. Users in these
areas don't want to have to undergo extensive training or calibration sesssions, and

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won't accept using gelled electrodes on their scalp. In the long term, they suggest,
alternative sensors to EEG electrodes should be developed.
Software must be robust to environmental noise and nonstationary. It will be
essential to reduce calibration times. And new algorithms to perform user-state
monitoring will be required.
Finally, progress will be needed in integrating BCI equipment into existing
systems. (Think 'plug-and-play'). They suggest that hardware and software
standardization will be key in this area.
The prospects are bright for rapid growth of BCI in non-medical areas. This
is especially true of gaming, with its large economic impact. The authors propose
measures that can assist the realization of BCI's potential. One observation is that
the coordination of medical and nonmedical BCI research efforts is vital. Such
alignment could produce a shared roadmap and research agenda that would benefit
both areas.

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8.BCI CHALLENGES AND INNOVATORS
8.1.BCIChallenges
Although we already understand the basic principles behind BCIs, they don't work
perfectly. There are several reasons for this.
The brain is incredibly complex. To say that all thoughts or actions are the
result of simple electric signals in the brain is a gross understatement. There are
about 100 billion neurons in a human brain [source: Greenfield]. 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.
The signal is weak and prone to interference. EEGs measure tiny voltage
potentials. Something as simple as the blinking eyelids of the subject can generate
much stronger signals. Refinements in EEGs and implants will probably overcome
this problem to some extent in the future, but for now, reading brain signals is like
listening to a bad phone connection. There's lots of static.
The equipment is less than portable. It's far better than it used to be -- early systems
were hardwired to massive mainframe computers. But some BCIs still require a
wired connection to the equipment, and those that are wireless require the subject to
carry a computer that can weigh around 10 pounds. Like all technology, this will
surely become lighter and more wireless in the future.
8.2.BCI Innovators
A few companies are pioneers in the field of BCI. Mostof them are still in the
research stages, though a few products are offered commercially.
Neural Signals is developing technology to restore speechto disabled people.
An implant in an area of the brain associated with speech (Broca's area) would
transmit signals to a computer and then to a speaker. With training, the subject could
learn to think each of the 39 phonemes in the English language and reconstruct
speech through the computer and speaker.

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NASA has researched a similar system, although it reads electric signals from
the nerves in the mouth and throat area, rather than directly from the brain. They
succeeded in performing a Web search by mentally "typing" the term "NASA" into
Google.
Cyber kinetics Neurotechnology Systems is marketing the Brain Gate, a
neural interface system that allows disabled people to control a wheelchair, robotic
prosthesis or computer cursor.
Japanese researchers have developed a preliminary BCI that allows the user
to control their avatar in the online world Second Life.

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9.ADAVANTAGE AND DISADVANTAGE
9.1.Advantages of BCI:
Eventually, this technology could:
 allow paralyzed people to control prosthetic limbs with their mind
 transmit visual images to the mind of a blind person, allowing them to see
 transmit auditory data to the mind of a def person, allowing them to hear
 allow gamers to control video games with their minds
 allow a mute person to have their thoughts displayed and spoken by a
computer
9.2.Disadvantages of BCI:
 Research is still in beginning stages
 The current technology is crude
 Ethical issues may prevent its development
 Electrodes outside of the skull can detect very few electric signals from the
brain
 Electrodes placed inside the skull create scar tissue in the brain

27
10.CONCLUSION
The ability of computers to enhance and augment both mental and physical
abilities and potential is no longer the exclusive realm of science fiction writers. It
is becoming a reality. Brain Computer Interface technology will help define the
potential of the human race. It holds the promise of bringing sight to the blind,
hearing to the deaf, and the return of normal functionality to the physically
impaired. A miracle? Hardly. But perhaps the next closestthing.
As BCI technology further advances, brain tissue may one day give way to
implanted silicon chips thereby creating a completely computerized simulation of
the human brain that can be augmented at will. Futurists predict that from there,
superhuman artificial intelligence won't be far behind.

Brain-Computer Interface (BCI)-Seminar Report

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Brain-Computer Interface (BCI)-Seminar Report