The document is a seminar report on brain computer interfaces submitted by Rahul Sharma to the Department of Electronics and Communication Engineering at Lakshmi Narain College of Technology and Science in Bhopal. It provides an overview of brain computer interface technologies including their history, the architecture and functions of different parts of the brain, brain imaging techniques used in BCI like EEG and fNIRS, experiments involving monkeys controlling robotic arms and the first human to human brain communication, and applications of BCI in restoring physical disabilities, communication, and robotics.

1. A SEMINAR REPORT
ON
BRAIN COMPUTER INTERFACE
SESSION-2014
Submitted to- Submitted By
Prachi Parashar Rahul Sharma
(Assistant Professor- ECE) (0157EC121066)
DEPARTMENT OF ELECTRONICS & COMMUNICATION
ENGINEERING
LAKSHMI NARAIN COLLEGE OF TECHNOLOGY & SCIENCE,
BHOPAL
I

2. LAKSHMI NARAIN COLLEGE OF TECHNOLOGY &
SCIENCE, BHOPAL
Department of Electronics & Communication Engineering
CERTIFICATE
This is to certify that the seminar report entitled “BRAIN COMPUTER
INTERFACE” has been satisfactorily presented by Rahul Sharma. It is a
certify that, seminar report is submitted to Department of ELECTRONICS &
COMMUNICATION, LAKSHMI NARAIN COLLEGE OF
TECHNOLOGY & SCIENCE, BHOPAL for the fourth semester of Bachelor
of Engineering during the academic year 2014.
Submitted to:-
Prachi Parashar
(Astt. Proff. Of ECE)
LAKSHMI NARAIN COLLEGE OF TECHNOLOGY & SCIENCE, BHOPAL
II

3. LAKSHMI NARAIN COLLEGE OF TECHNOLOGY &
SCIENCE, BHOPAL (M.P.)
Electronics & Communication Engineering
DECLARATION
I, Rahul Sharma, Student of Bachelor of Engineering, Branch Electronics &
Communication Engineering, LAKSHMI NARAIN COLLEGE OF
TECHNOLOGY & SCIENCE BHOPAL hereby declare that the seminar
report presented on the topic ―BRAIN COMPUTER INTERFACE” is
outcome of my own work, is bonafide, correct to the best of my knowledge and
this work has been carried out taking care of Engineering Ethics.
Rahul Sharma
Enrollment no. 0157EC121066
III

4. ACKNOWLEDGEMENT
Every work started and carried out with systematic approach turns out to be
Successful. Any accomplished requires the effort of many people and this work
is no different. This seminar difficult due to numerous reasons some of error
correction was beyond my control. Sometimes, I was like rudderless boat
without knowing what to do next. It was then the timely guidance of that has
seen us through all these odds. I would be very grateful to them for their
inspiration, encouragement and guidance in all phases of the endeavor.
It is my great pleasure to thank Dr. Soni Changlani, HOD of Electronics and
Communication for her constant encouragement and valuable advice for this
seminar. I also wish to express my gratitude towards all other staff members for
their kind help.
Finally, I would thank Pro. Prachi Parashar who was tremendously
contributed to this seminar directly as well as indirectly; gratitude from the
depths of my heart is due to her. Regardless of source I wish to express my
gratitude to those who may contribute to this work, even though anonymously.
IV

5. BRAIN COMPUTER INTERFACE
(BCI/MMI/DNI)
INDEX
TITLE PAGE NO.
ABSTRACT 1
1. INTRODUCTION 2
1.1 HISTORY 3
2. ARCHITECTURE OF BRAIN 4
2.1 CEREBRAL CORTEX SYSTEM 4
2.1.1 GEOGRAPHY OF THOUGHTS 6
2.2 SUB-CORTICAL REIGON 4
3. BRAIN IMAGING TECHNOLOGIES 7
3.1 ELECTROENCEPHALOGRAPHY 9
3.2 FUNCTIONAL NEAR INFRARED 9
SPECTROSCOPY
4. EXPERIMENTS AND RESEARCHES 10
4.1 IMPLANTATION OF ARTIFICIAL EYES 11
4.2 MONKEY OPERATED ROBOTIC ARM 12
4.3 BRAIN TO BRAIN COMMUNICATION 13
V

6. 5.0 APPLICATIONS OF BCI 14
5.1 RESTORING PHYSICAL DISABILITIES 14
5.2 COMMUNICATION 15
5.3 ROBOTICS 15
5.4 VIRTUAL REALITY 16
6.0 CONCLUSION 17
REFERENCES 18
VI

7. Abstract
For generations, humans have fantasized about the ability to communicate and
interact with machines through thought alone or to create devices that can
peer into person’s mind and thoughts. These ideas have captured the
imagination of humankind in the form of ancient myths and modern science
fiction stories. However, it is only recently that advances in cognitive
neuroscience and brain imaging technologies have started to provide us with
the ability to interface directly with the human brain. Primarily driven by
growing societal recognition for the needs of people with physical disabilities,
researchers have used these technologies to build brain computer interfaces
(BCIs), communication systems that do not depend on the brain’s normal
output pathways of peripheral nerves and muscles. In these systems, users
explicitly manipulate their brain activity instead of using motor movements to
produce signals that can be used to control computers or communication
devices.
The impact of this work is extremely high, especially to those who suffer
from devastating neuromuscular injuries and neurodegenerative diseases such
as amyotrophic lateral sclerosis, which eventually strips individuals of
voluntary muscular activity while leaving cognitive function intact.
1

8. Introduction
For generations, humans have fantasized about the ability to communicate and
interact with machines through thought alone or to create devices that can peer
into person’s mind and thoughts. These ideas have captured the imagination of
humankind in the form of ancient myths and modern science fiction stories.
However, it is only recently that advances in cognitive neuroscience and brain
imaging technologies have started to provide us with the ability to interface
directly with the human brain. This ability is made possible through the use of
sensors that can monitor some of the physical processes that occur within the
brain that correspond with certain forms of thought.
Figure1.1 Introduction to BCI
Primarily driven by growing societal recognition for the needs of people
with physical disabilities, researchers have used these technologies to build
brain computer interfaces (BCIs), communication systems that do not depend
on the brain’s normal output pathways of peripheral nerves and muscles.
2

9. ―Brain computer interface is the technology to interact with human brain
to the computer or any communicating device.”
The impact of this work is extremely high, especially to those who suffer from
devastating neuromuscular injuries and neurodegenerative diseases such as
amyotrophic lateral sclerosis, which eventually strips individuals of voluntary
muscular activity while leaving cognitive function intact.
1.1 History
The history of brain–computer interfaces (BCIs) starts with Hans
Berger's discovery of the electrical activity of the human brain and the
development of electroencephalography (EEG). In 1924 Berger was the first to
record human brain activity by means of EEG. Berger was able to
identify oscillatory activity in the brain by analyzing EEG traces. One wave he
identified was the alpha wave (8–13 Hz), also known as Berger's wave.
Berger's first recording device was very rudimentary. He
inserted silver wires under the scalps of his patients. These were later replaced
by silver foils attached to the patients' head by rubber bandages. Berger
connected these sensors to a Lippmann capillary electrometer, with
disappointing results. More sophisticated measuring devices, such as the
Siemens double-coil recording galvanometer, which displayed electric
voltages as small as one ten thousandth of a volt, led to success.
Berger analyzed the interrelation of alternations in his EEG wave diagrams
with brain diseases. EEGs permitted completely new possibilities for the
research of human brain activities
Research on BCIs began in the 1970s at the University of California
Los Angeles (UCLA) under a grant from the National Science Foundation,
followed by a contract from DARPA..
The papers published after this research
also mark the first appearance of the expression brain–computer interface in
scientific literature.
3

10. Architecture of Brain
Contrary to popular simplifications, the brain is not a general-purpose
computer with a unified central processor. Rather, it is a complex assemblage
of competing sub-systems, each highly specialized for particular tasks
(Carey2002). By studying the effects of brain injuries and, more recently, by
using new brain imaging technologies, neuroscientists have built detailed
topographical maps associating different parts of the physical brain with
distinct cognitive functions.
The brain can be roughly divided into two main parts:
2.1 Cerebral Cortex,
2.2 Sub-cortical regions.
2.1 Cerebral Cortex System
The cerebral cortex is evolutionarily much newer. Since this is the largest
and most complex part of the brain in the human, this is usually the part
of the brain people notice in pictures. The cortex supports most sensory and
motor processing as well as ―higher‖ level functions including reasoning,
planning, language processing, and pattern recognition. This is the region
that current BCI work has largely focused on.
2.2 Sub-cortical Regions
Sub-cortical regions are phylogenetically older and include a areas
associated with controlling basic functions including vital functions such as
respiration, heart rate, and temperature regulation, basic emotional and
instinctive responses such as fear and reward, reflexes, as well as learning
and memory.
4

11. Fig2.1 Functional areas of cerebral cortex (Lateral view)
5

12. 2.1.1 Cerebral Cortex System (Geography of Thoughts)
The cerebral cortex is split into two hemispheres that often have very
different functions. For instance, most language functions lie primarily in
the left hemisphere, while the right hemisphere controls many abstract and
spatial reasoning skills. Also, most motor and sensory signals to and from
the brain cross hemispheres, meaning that the right brain senses and
controls the left side of the body and vice versa.
The brain can be further divided into separate regions specialized for
different functions. For example, occipital regions at the very back of the
head are largely devoted to processing of visual information. Areas in the
temporal regions, roughly along the sides and lower areas of the cortex, are
involved in memory, pattern matching, language processing, and auditory
processing. Still other areas of the cortex are devoted to diverse functions
such as spatial representation and processing, attention orienting,
arithmetic, voluntary muscle movement, planning, reasoning and even
enigmatic aspects of human behavior such as moral sense and ambition.
We should emphasize that our understanding of brain structure and
activity is still fairly shallow. These topographical maps are not definitive
assignments of location to function. In fact, some areas process multiple
functions, and many functions are processed in more than one area.
6

13. Brain Imaging Technologies
There are two general classes of brain imaging technologies: invasive
technologies, in which sensors are implanted directly on or in the brain, and
non-invasive technologies, which measure brain activity using external
sensors. Although invasive technologies provide high temporal and spatial
resolution, they usually cover only very small regions of the brain.
Additionally, these techniques require surgical procedures that often lead to
medical complications as the body adapts, or does not adapt, to the implants.
Furthermore, once implanted, these technologies cannot be moved to measure
different regions of the brain. While many researchers are experimenting with
such implants, we will not review this research in detail as we believe these
techniques are unsuitable for human-computer interaction work and general
consumer use.
We summarize and compare the many non-invasive technologies that use
only external sensors. While the list may seem lengthy, only
Electroencephalography (EEG) and Functional Near Infrared
Spectroscopy (fNIRS) present the opportunity for inexpensive, portable, and
safe devices, properties we believe are important for brain-computer interface
applications in HCI work.
7

14. Figure 3.1 Invasive and Non-invasive method
8

15. 3.1 Electroencephalography
EEG uses electrodes placed directly on the scalp to measure the weak (5–100
µV) electrical potentials generated by activity in the brain. Because of the
fluid, bone, and skin that separate the electrodes from the actual electrical
activity, signals tend to be smoothed and rather noisy. Hence, while EEG
measurements have good temporal resolution with delays in the tens of
milliseconds, spatial resolution tends to be poor, ranging about 2–3 cm
accuracy at best, but usually worse. Two centimeters on the cerebral cortex
could be the difference between inferring that the user is listening to music
when they are in fact moving their hands. We should note that this is the
predominant technology in BCI work.
3.2 Functional Near Infrared Spectroscopy
fNIRS technology, on the other hand, works by projecting near infrared light
into the brain from the surface of the scalp and measuring optical changes at
various wavelengths as the light is reflected back out (for a detailed discussion
of fNIRS. The NIR response of the brain measures cerebral hemodynamics
and detects localized blood volume and oxygenation changes.
Since changes in tissue oxygenation associated with brain activity modulate
the absorption and scattering of the near infrared light photons to varying
amounts, fNIRS can be used to build functional maps of brain activity. This
generates images similar to those produced by traditional Functional Magnetic
Resonance Imaging (fMRI) measurement. Much like fMRI, images have
relatively high spatial resolution (<1 cm) at the expense of lower temporal
resolution (>2–5 seconds), limited by the time required for blood to flow into
the region.
9

16. Figure3.2 Schematics of BCI
In brain-computer interface research aimed at directly controlling computers,
temporal resolution is of utmost importance, since users have to adapt their
brain activity based on immediate feedback provided by the system. For
instance, it would be difficult to control a cursor without having interactive
input rates. Hence, even though the low spatial resolution of these devices
leads to low information transfer rate and poor localization of brain activity,
most researchers currently adopt EEG because of the high temporal resolution
it offers. However, in more recent attempts to use brain sensing technologies to
passively measure user state, good functional localization is crucial for
modeling the users’ cognitive activities as accurately as possible. The two
technologies are nicely complementary and researchers must carefully select
the right tool for their particular work.
10

17. Experiments and Researches
The experiments and researches of BCI are as follows:
4.1 Implantation of Artificial Eyes (1978)
In 2002, Jens Naumann, also blinded in adulthood, became the first in a series
of 16 paying patients to receive Dobelle’s second generation implant, marking
one of the earliest commercial uses of BCIs. The second generation device
used a more sophisticated implant enabling better mapping of phosphenes into
coherent vision. Phosphenes are spread out across the visual field in what
researchers call "the starry-night effect". Immediately after his implant, Jens
was able to use his imperfectly restored vision to drive an automobile slowly
around the parking area of the research institute.
Figure4.1 Jens Naumann ( Artificial vision patient)
11

18. 4.2 Monkey Operated a Robotic Arm (2008)
In May 2008, a monkey controlled a robotic arm to feed himself In
University of Pittsburgh Medical Center.
Figure4.2 BCI research on monkey
4.3 First Human Brain to Brain Communication (2012)
University of Washington researchers have performed what they believe is the
first noninvasive human-to-human brain interface, with one researcher able
to send a brain signal via the Internet to control the hand motions of a fellow
researcher. Using electrical brain recordings and a form of magnetic
stimulation, Rajesh Rao sent a brain signal to Andrea Stocco on the other
side of the UW campus, causing Stocco’s finger to move on a keyboard.
While researchers at Duke University have demonstrated brain-to-brain
communication between two rats, and Harvard researchers have demonstrated
it between a human and a rat, Rao and Stocco believe this is the first
demonstration of human-to-human brain interfacing.
12

19. Figure4.3 Fist human brain to brain communication
In above figure, University of Washington researcher Rajesh Rao, left, plays
a computer game with his mind. Across campus, researcher Andrea Stocco,
right, wears a magnetic stimulation coil over the left motor cortex region of
his brain. Stocco’s right index finger moved involuntarily to hit the ―fire‖
button as part of the first human brain-to-brain interface demonstration.
13

20. Applications of BCI
5.1 Restoring Physical Disabilities
One of the most critical needs for people with severe physical disabilities is
restoring the ability to communicate. The field of BCI research and
development has since focused primarily on neuroprosthetics applications that
aim at restoring damaged hearing, sight and movement.
Figure5.1 Basic diagram of a handicapped controlling computer with BCI
14

21. 5.2 Communication
Communication systems that do not depend on the brain’s normal output
pathways of peripheral nerves and muscles. In these systems, users explicitly
manipulate their brain activity instead of using motor movements to produce
signals that can be used to control computers or communication devices.
The impact of this work is extremely high, especially to those who
suffer from devastating neuromuscular injuries and neurodegenerative
diseases such as amyotrophic lateral sclerosis, which eventually strips
individuals of voluntary muscular activity while leaving cognitive function
intact
5.3 Robotics
Controlling robots with thought has long been a popular science fiction
concept. Recent work with BCIs, however, has shown that robotic control is
indeed possible with brain signals. Applications for neurally-controlled robots
currently center on assistive technologies—―helper‖ robots—but BCI control
has been proposed for military and industrial applications as well. One of the
earliest BCI-controlled robots, the experiment explored the effects of real-
world feedback (the movement of the robot) in conjunction with a P300-based
BCI, which depends on user attention. The robot was configured to perform
the steps to make coffee, such as getting powdered coffee, sugar, and cream,
and stirring the mixture with a spoon. The results showed that users can
effectively attend to real-world feedback while operating an attention-based
BCI.
15

22. Figure5.2 A lady controlling a robotic arm to feed herself
5.4 Virtual Reality
In the BCI research world that have more practical purposes. The early work
in virtual environments is described in Bayliss and Ballard (2000), which
details a study of a P300 BCI controlling a virtual apartment and a virtual
driving simulator.
Subsequent work as detailed in Pfurtscheller et al. (2006) incorporates the
ReaCTor ―cave‖ environment, an immersive virtual world which the user
navigates using a BCI. The subject can ―walk‖ through the virtual world by
imagining foot movement, and can ―touch‖ things in the virtual world by
imagining reaching and hand movement.
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23. Conclusion
Research and development in Brain Computer Interfaces has exploded in the
last ten years, both in the technologies available and the number of
organizations involved in the field. BCIs have now evolved beyond laboratory
experimental systems and some are now offered as commercial products. No
longer the realm of science fiction, BCIs are becoming a viable and effective
alternative for assistive technology and a plethora of mainstream applications.
New paradigms of interaction open even more possibilities for BCI and create
new fields of study, such as neural imaging for computational user
experience.
However, many obstacles remain for BCI researchers. BCIs are still
notoriously slow and error-prone compared to traditional input technologies.
More research is essential in order to develop techniques to reduce both
neural and environmental artifacts, to reduce error rates, and to increase
accuracy. For BCI systems to be feasible for mainstream real-world use in the
home and office, they must be simple, small, wearable, and unobtrusive. New
sensor technologies such as dry EEG electrodes and fNIR emitter/detectors
must be perfected. Adaptive systems must be sufficient to automatically
calibrate and ―tune‖ BCIs to an individual’s brain signal patterns without
expert assistance. These are daunting challenges, but as the BCI field matures,
effectiveness and accuracy are increasing. The BCI field is rapidly
approaching critical mass to develop the human-computer interaction methods
of the future.
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interface research. IEEE Trans Rehabil Eng 8(2):188–190
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