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A brain-computer interface (BCI), sometimes called a direct neural interface or a brain-machine interface, is a direct communication pathway between a either accept commands from the brain or send signals to it (for example, to human or animal brain and an external device. In one-way BCIs, computers restore vision) but not both. Two-way BCIs would allow brains and external devices to exchange information in both directions but have yet to be successfully implanted in animals or humans. In this definition, the word brain means the brain or nervous system of an organic life form rather than the mind. Computer means any processing or computational device, from simple circuits to silicon chips. Research on BCIs began in the 1970s, but it wasn't until the mid-1990s that the first working experimental implants in humans appeared. Following years of animal experimentation, early working implants in humans now exist, designed to restore damaged hearing, sight and movement. With recent advances in technology and knowledge, pioneering researchers could now conceivably attempt to produce BCIs that augment human functions rather than simply restoring them, previously only a possibility in science fiction

Brain Computer Interface
Chapter 1.
Man machine interface has been one of the growing fields of research and development in recent years. Most of the effort has been dedicated to the design of user- friendly or ergonomic systems by means of innovative interfaces such as voice recognition, virtual reality. A direct brain-computer interface would add a new dimension to man-machine interaction. A brain-computer interface, sometimes called a direct neural interface or a brain machine interface, is a direct communication pathway between a human or animal brain(or brain cell culture) and an external device. In one BCIs, computers either accept commands from the brain or send signals to it but not both. Two way BCIs will allow brains and external devices to exchange information in both directions but have yet to be successfully implanted in animals or humans. Brain-Computer interface is a staple of science fiction writing. In its earliest incarnations no mechanism was thought necessary, as the technology seemed so far fetched that no explanation was likely. As more became known about the brain however, the possibility has become more real and the science fiction more technically sophisticated. Recently, the cyberpunk movement has adopted the idea of 'jacking in', sliding 'biosoft' chips into slots implanted in the skull(Gibson, W.1984).Although such biosofts are still science fiction, there have been several recent steps toward interfacing the brain and computers. In this definition, the word brain means the brain or nervous system of an organic life form rather than the mind. Computer means any processing or computational device, from simple circuits to silicon chips (including hypothetical future technologies like quantum computing).
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for more information read
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please request here or for new topic information request.....
Post: #5

Brain Computer Interface
1. Abstract

A brain-computer interface (BCI) is literally a direct technological interface between a brain and a computer not requiring any motor output from the user. That is, neural impulses in the brain are intercepted and used to control an electronic device. The brain's electrical output is translated by a computer into physical outputs,. such as moving a cursor on a computer screen. To make the computer understand what the brain intends to communicate necessitates monitoring the brain activity. Among the possible brain monitoring methods, the scalp recorded electroencephalogram (EEG) constitutes an adequate alternative because of its good time resolution, relative simplicity and noninvasiveness. The EEG signals are analyzed and mapped into actions inside the computer rendered environment. A BCI allows a person to communicate with or control the external world without using the brain's normal output pathways of peripheral nerves and muscles. Messages. and commands are expressed not by muscle contractions, but rather by electrophysiological signals from the brain. BCls provide an alternative communication and control option for the severely disabled. There has been great success in using cochlear implants in humans as a treatment for non-congenital deafness. There is also promising research in vision science indicating retinal implants may some day prove to be similarly successful in treating non-congenital blindness.
Brain Computer Interface 2. Introduction
2.1. What is a Brain-Computer Interface (BCI)? Since the creation of computer rendered environments, the computers power has been increasing to an amazing speed. Each year, the possibilities offered by a computer are growing extraordinarily. However, the communications with a machine have unfortunately not significantly evolved. We still have to content ourselves of an old keyboard, a mouse and a screen display. It is undeniable that it would be a wonderful evolution to improve our interactions with the machines. Following this idea, eminent computer professionals were concerned about developing a new way of communication using the brain. Based on measures of the electrical activity of the brain, the goal was to discover the user's thoughts in order to execute his orders. To accomplish this prowess, the natural properties of the brain were used: to identify a particular mental activity of the subject, we observe the variation of his mental activities in frequency and in time using an appropriate material. A mathematical algorithm will next analyze the collected data and guess about the subject thoughts. This technology offers creating a completely new way of communication offering lots of possibilities. For example, for persons with movements' disabilities, a BCI could help them to command their electric wheelchair without needing somebody else help, giving them more independence. As another example, we can image substitute some damaged nerves by computers intercepting the brain messages and redirecting them to the muscle. For people having to deal with epilepsy crisis, it would be interesting to improve their knowledge of the different processes that stimulate a crisis and try to avoid them, controlling their own brain as another muscle. ,
Brain Computer Interface The idea of using our brains to directly control a machine isn't particularly new. As far back as 1967, Edmond Dewan described experiments using subjects wired to an electroencephalograph (EEG), which records and graphs the electrical activity of the brain. With practice, the subjects were able to reduce the amplitude of their brain's alpha rhythms, to transmit Morse code to a teleprinter. Research into the Brain Computer Interface, or BCI, began in earnest in the early 70's, when the United States Department of Defense saw the promise of fighter pilots using their minds to directly control their planes. Given the technology of the time, there was limited success, and the program was cancelled. But the groundwork was laid for a field of research now growing rapidly. A major motivation has been to help patients suffering from conditions such as cerebral palsy, or spinal injuries, which inhibit physical control, but which leave intellectual faculties intact. Over the last decade, great advances have been made. Automatic systems capable of understanding different facets of human communication will be at the heart of human-computer interfaces (HCI) in the near future. An HCI which is built on the guiding principle: "think and make it happen without any physical effort" is called a brain-computer interface (BCI). Indeed, the "think" part of this principle involves the human brain, "make it happen" implies that an executor is needed (here: a computer) and "without any physical effort" means that a direct interface between the brain and the computer is required. To make the computer understand what the brain intends to communicate necessitates monitoring the brain activity. Among the possible brain monitoring methods, the scalp recorded electroencephalogram (EEG)
Post: #6
your computer brain interface report is excellent and superb..i found very interesting...i thank
u for giving me this topic
Post: #7
hey i really liked this site and will surely contribute hop it will help others.... as it rescued me at eleventh hour
Post: #8
can u plz me mail the full report on brain interface so tht i can prepare my ppt for colg seminars...
i will be very grateful to u if u will
my email id is hot1989[at]
i will w8 fr ur mail...
Post: #9
hiiii i have ppts only and no seminars reports. still im posting few things check out if they r of any help...
also there exit sm reports on same at this site....
Post: #10
Post: #11
Thank you all for the appreciation. Avi_1824, you can just copy the report at the beginning of this thread to make a report. Just check the first page in this thread.
I think the ppt of this topic can be found in these links:
Post: #12
I want a good seminars report on brain computer interface.
Post: #13
A brain-machine interface

A brain-machine interface is a communication system that does not depend on the brains normal output pathways of peripheral nerves and muscles. It is a new communication link between a functioning human brain and the outside world.These are electronic interfaces with the brain, which has the ability to send and receive signals from the brain. BMI uses brain activity to command, control, actuate and communicate with the world directly through brain integration with peripheral devices and systems. The signals from the brain are taken to the computer via the implants for data entry without any direct brain intervention. BMI transforms mental decisions and/or reactions into control signals by analyzing the bioelectrical brain activity.

While linking the brain directly with machines was once considered science fiction, advances over the past few years have made it increasingly viable. It is an area of intense research with almost limitless possibilities. The human brain is the most complex physical system we know of, and we would have to understand its operation in great detail to build such a device. An immediate goal of brain-machine interface study is to provide a way for people with damaged sensory/motor functions to use their brain to control artificial devices and restore lost capabilities. By combining the latest developments in computer technology and hi-tech engineering, paralyzed persons will be able to control a motorized wheel chair, computer painter, or robotic arm by thought alone. In this era where drastic diseases are getting common it is a boon if we can develop it to its full potential. Recent technical and theoretical advances, have demonstrated the ultimate feasibility of this concept for a wide range of space-based applications. Besides the clinical purposes such an interface would find immediate applications in various technology products also.
Post: #14
can i get a detailed report about this semiar?Smile
Post: #15
Brain Computer Interfaces
Controlling computers by thoughts

The field of Human Computer Interaction (HCI) is an interdisciplinary development having roots in
computer graphics, operating systems, human factors, ergonomics, cognitive psychology, and
others [SIGCHI_HCI]. This term implies that there is a bidirectional communication between the
computer and the user where each communication channel may utilize very different techniques and
devices for effective communication (one channel for user input and one channel for feedback to
the user by the computer).
Current devices for achieving input into the computer mainly require physical or more precisely
mechanical operation by the user, e.g. mouse and keyboard. Feedback from the computer is
commonly given by audio/visual elements, e.g. speakers and monitors showing GUIs. However, the
limitations in terms of usability and accessibility are well understood and have become apparent
throughout the course of time. The main principle in overcoming these limitations is called
Multimodal Interaction and there is a lot of ongoing research in this field. For example, current
developments in Multimodal Interaction propose a combination of a visual modality with a voice
modality for better usability and accessibility.
A somewhat unconventional approach to achieving human-computer-interaction involves directly
translating thoughts of the user into commands to the computer. In this context the term “thought”
refers to the computer-aided interpretation of neuronal activities of the user. Neuronal activities may
be recorded either at certain extremities of the human (arms, legs, etc.) or at the brain itself by
analyzing brain waves. In principle, this approach is not limited to input into the computer but
moreover may include methods for the computer to give feedback to the user by directly stimulating
This paper gives an overview of this small subset of HCI putting an emphasis on Brain-Computer-
Interaction (BCI). We will elaborate on its historical background, technologies used for
implementing it and finally we will discuss possible and current applications.
Post: #16
Presented by:
S V Rupesh



A brain-computer interface (BCI) is a direct communication pathway between a human or animal brain and an external device.

1924: Hans Berger discovers the EEG
Analyses the interrelation of EEG and brain diseases
1970: First developments to use brain waves as input
ARPA has vision of enhanced human
First step in the right direction
Post: #17


Brain-Computer interface is a staple of science fiction writing. Init's earliest incarnations nomechanism was thought necessary, as the technology seemed so far fetched that no explanation was likely. As more became known about the brain however, the possibility has become more real and the science fiction more technically sophisticated. Recently, the cyberpunk movement has adopted the idea of "jacking in", sliding "biosoft" chips into slots implanted in the skull (Gibson, W. 1984).
Although such biosofts are still science fiction, there have been several recent steps toward interfacing the brain and computers. Chief among these are techniques for stimulating and recording from areas of the brain with permanently implanted electrodes and using conscious control of EEG to control computers.

Some preliminary work is being done on synapsing neurons on silicon transformers and on growing neurons into neural networks on top of computer chips.The most advanced work in designing a brain-computer interface has stemmed from the evolution of traditional electrodes. There are essentially two main problems, stimulating the brain (input) and recording from the brain (output).

Traditionally, both input and output were handled by electrodes pulled from metal wires and glass tubing.Using conventional electrodes, multi-unit recordings can be constructed from mutlibarrelled pipettes. In addition to being fragile and bulky, the electrodes in these arrays are often too far apart, as most fine neural processes are only .1 to 2 µm apart.
Post: #18

Computer Science and Engineering
School of Engineering,

For measuring brain function, neuroimaging modalities such as fMRI, EEG and MEG are providing clinicians and neuroscientists with a variety of powerful tools. Without a doubt EEGs have been the best tool so far for this type of research. From the different parts of the brain such as frontal, occipital, parietal & cortical different brain activities are measured with either invasive or non-invasive real time techniques.

After obtaining EEG signals they are applied to signal processing unit, which includes amplifier, special function filters, ICA components (artifact rejection), ADC etc.

Now our task is to classify different EEG patterns according to its features such as frequency and amplitude in different states of consciousness like alertness, lethargy and dreaming. Our approach is generally based on an artificial neural network that recognizes and classifies different brain activation patterns associated with carefully selected mental tasks. Then the classified signal is translated into the control command signal using software to perform mental recognized task and is applied to the control device.

By watching the control action of the device on the computer screen, visual feedback from the eye is given to brain and the next control action can be decided by the user.
This whole close loop system is known as brain computer interface .
Post: #19
new technology use in computer brain interface
Post: #20

what u want?
Post: #21
Hello can i get detailed information abiut brain computer will be really helpful for me..i find it very intresting topic..i need it for ma class i hope that i will get it..thank you.Angel
Post: #22
more on BRAIN COMPUTER INTERFACE are there. please go through the below link.
Post: #23
Brain Computer Interface
A Seminar Report
Shiju S.S
Department of Computer Science & Engineering
College of Engineering Trivandrum
Kerala - 695016

A brain computer interface presents a direct communication channel from the brain to the
computer. The BCI processes the brain activity and translates it into system commands using
feature extraction and classi cation algorithms. EEG-based BCI experiments have been de-
signed and conducted. The experiments are designed to nd distinctive brain patterns which
are generated voluntary. Various researches have been going on in EEG Based BCI. While
most current brain computer interface research (BCI) is designed for direct use with disabled
users, This seminars is to explain functional near-infrared spectroscopy (fNIRS), a non- invasive
brain measurement device, to augment an interface so it uses brain activity measures as an
additional input channel. Future work in BCI will focus on creating an interface that responds
to one of those measures by adapting the interface. By combining brain signal measured with
an adaptive interface it is expect to contribute a functional passive brain-computer interface
that measures and adapts to the users brain signal.


1 Introduction
What is a Brain Computer Interface? As mentioned in the preface a BCI represents a direct
interface between the brain and a computer or any other system. BCI is a broad concept
and comprehends any communication between the brain and a machine in both directions:
e ectively opening a completely new communication channel without the use of any peripheral
nervous system or muscles.
In principle this communication is thought to be two way. But present day BCI is mainly
focusing on communication from the brain to the computer. To communicate in the other
direction, inputting information in to the brain, more thorough knowledge is required concerning
the functioning of the brain. Certain systems like implantable hearing-devices that convert
sound waves to electrical signal which in turn directly stimulate the hearing organ already exist
today. These are the rst steps. The brain on the other hand is on a whole other complexity
level compared to the workings of the inner ear.
Figure 1: Basic BCI layout
From here on the focus is on communication directly from the brain to the computer. Most
commonly the electrical activity ( elds) generated by the neurons is measured, this measuring
technique is known as EEG (Electroencephalography). An EEG-based BCI system measures
speci c features of the EEG-activity and uses these as control signals.
Over the past 15 years the eld of BCI has seen a rapidly increasing development rate and
obtained the interest of many research groups all over the world. Currently in BCI-research
the main focus is on people with severe motor disabilities. This target group has little (other)
means of communication and would be greatly assisted by a system that would allow control
by merely thinking.
The concept of thinking is perhaps too broad a concept and can actually better be replaced
by generating brain patterns. The general picture of a BCI thus becomes that the subject
is actively involved with a task which can be measured and recognized by the BCI. This
task consists of the following: evoked attention, spontaneous mental performance or mental
imagination. The BCI then converts the 'command' into input control for a device (see gure
This is the basic idea. With the continuously increasing knowledge of the brain and advances
in BCI over time, perhaps BCI will be able to extract actual intentions and thoughts. This
however does not appear to be on the cards for the very near future.
2 Concepts
The de nition of BCI as quoted from the rst international meeting devoted to BCI research
in 1999.'A brain-computer interface is a communication system that does not depend on the
brain's normal output pathways of peripheral nerves and muscles'.The goal is to acquire knowl-
edge of the intentions of the user either consciously or unconsciously by means of measurement
of brain activity. This goal can be achieved in various ways, but it all starts with the brain and
thus with the most basic element of the brain.
2.1 The neuron
A neuron is a cell that uses biochemical reactions to receive, process and transmit information.
It consists of the cell body (Soma) in which the cell core (Nucleus) resides (see gure 2). Each
neuron has one axon; this is a long 'cable'-like part of the neuron which is used to reach other
neurons. The soma of a neuron is branched out into dendrites to which axon-ends from other
neurons connect.
Figure 2: Overview of the neuron
The dendrites are not in actual physical contact with the axons of other neurons; a small cleft
exists between them: the synaptic gap. This is the location where the impulse is transferred.
When a neuron res, it sends signals to all the neurons that are connected to its axon via
the dendrites. The dendrites can be connected to thousands of axons; all incoming signals
combined are added through spatial and temporal summation. If the aggregate input reaches
a certain threshold, the neuron will re and send a signal along its own axon. The strength of
this output signal is always the same, no matter the magnitude of the input.
This single signal of a neuron is very weak. The numerous neurons in the brain are constantly
active. The generated activity can be measured. It appears to be impossible to measure the
individual activity of every neuron. Moreover it is questionable whether it would be a real gain,
since neurons work in groups to achieve a certain goal. The activity from a group of neurons
however can be measured. For the signals of neurons to be visible using EEG in particular, a
couple of conditions need to be met, which are summarized schematically in gure 3.
 The electrical activity of the neuron must be perpendicular to the scalp in order for the
EEG to fully pick up the eld.
 A large number of neurons must re parallel to each other.
Figure 3: Cross-cut of the head: only the green neuronal activity can be measured using EEG
 The neurons must re in synchrony with the same polarity, in order not to cancel each
other out.
2.2 The Brain
Combining about 100 billion neurons results in what is called the human brain. The brain
consists of the following elements ( gure4)
 The brainstem is an important relay station. It controls the re
exes and automatic
functions, like heart rate and blood pressure and also sleep control.
 The Cerebellum integrates information about position and movement from the vestibular
system to coordinate limb movement and maintaining equilibrium.
 Mid-brain: amongst others the Hypothalamus and pituitary gland control visceral func-
tions, body temperature and behavioral functions like, the body's appetite, sleep patterns,
the sexual drive and response to anxiety, aggression and pleasure.
 The Cerebrum (or cerebral cortex) receives and integrates information from all of the
sense organs and controls the motor functions. Furthermore it contains the higher cerebral
functions like: language, cognitive functions and memories. Emotions are also processed
in the cerebrum.
The cortex of the cerebrum is part of the brain which is of the most interest for BCI. It is
responsible for the higher order cognitive tasks and is near the surface of the scalp. In addition
that functionality in the brain appears to be highly local.
The cerebrum is divided into two hemispheres, left and right. The left halve senses and
controls the right half of the body and vice versa. Each hemisphere can be divided into four
lobes, the frontal, the parietal, the occipital and the temporal (see gure4). The cortex can also
by divided in certain areas each of which is specialized for a di erent function. Especially the
sensorimotor cortex is important for BCI. Over this part the entire human body is represented.
Figure 4: Brain overview
Figure 5: Homunculus
The size of area corresponds with the importance and complexity of movement of that particular
body part (see gure5).
3 Electroencephalogram
The best brain measurement method would have a high spatial and temporal resolution, be
very cheap, portable and easy to apply non-invasively. This method does not (yet) exist.
Of all methods listed in the previous section, EEG is by far the most commonly used in BCI.
The prime reason for this is the excellent temporal resolution which is a necessity for real-time
BCI. And although the spatial data resulting from EEG is often distorted and far from perfect,
EEG o ers direct functional correlation of brain activity.
Another major plus is the ease of applying this method. With a cap containing only a few
electrodes measurements can start. For practical uses and applications it is small and relatively
portable, which improves prospects of future applications.
Aside from the ease of appliance, this is also a relatively low-cost method, certainly compared
to methods like MEG, which require expensive equipment and skilled professionals to operate.
Although EEG is the most commonly used, this does not mean that others methods are
not feasible. With the continuous improvement of the techniques involved, they can become a
viable option in the future.
EEG comes in two
avors; the most commonly used in BCI is the non-invasive variant. The
electrode is placed on the scalp. The obvious advantage is that it can be safely applied to
anyone at any moment without a lot of preparation.
The second variant is the invasive EEG. Instead of attaching the electrode on the skull, it
is placed inside. The advantage of this variant is the higher spatial resolution obtained by it.
With non-invasive EEG, the skull causes signi cant spatial smearing of the measured activity:
leading to more dicult localization of the original signal, which degrades the quality of the
3.1 10-20 system
A cap with a number of electrodes is placed on the user's head. At the TU Delft the 10-20
system of electrode placement is used. This is an international standard used for comparing
results among di erent research. The system is based on the relationship of the electrode
placement and the underlying area of the cerebral cortex. Each location on the scalp has a
letter to identify the hemisphere location (Frontal, Temporal, Central, Parietal and Occipital
Lobe) and a number to de ne the hemisphere. Ranging from 1 to 8, with the even number
referring to the right hemisphere and the odd numbers to the left hemisphere (see gure 6).
The 10-20 refers to the distance (in percentage) between the di erent electrodes. Reference is
needed to measure voltage. Reference electrodes are usually attached to relative stable points
where the potential remains constant. Points like the earlobes or mastoid bones behind the
This a-periodic and unpredictable activity is constantly present and is a result of the total
activity generated by all the neurons in the brain. The frequency range is divided into di erent
band: The Delta (0.1-3-5Hz), Theta (4-7.5Hz), Alpha (8-13Hz), Beta (14-30Hz) and Gamma
Figure 6: The international 10-20 system
(>30Hz)(see gure 7). The mu-rhythm is a speci c part of the Alpha rhythm (10-12Hz) and is
located over the sensorimotor cortex. The main advantage of the mu-rhythm over the Alpha
rhythm is that is does not appear to be in
uenced by eye-blinking therefore it is mainly used
in BCI. Users can learn to voluntary control the rhythms after training to some extent. This
concerns the synchronization of the rhythm.
Figure 7: Overview of the categorization of brain waves
Motor Imagery is a commonly used method in BCI. To obtain MI, the user should imagine
moving a hand, nger or leg but not actually moving it. Thereby generating the pattern in the
brain that goes with this movement, but not disturbing the EEG measurement by the actual
activity of muscles. Measurement of muscle activity is called EMG (electromyography) and
this activity overwhelms the EEG.
3.2 Artifacts
The EEG signals are always imperfect and always contaminated with artifacts. Artifacts are
undesirable disturbances in the signal. These artifacts range from bioelectrical potentials pro-
duced by movement of body parts like, eyes, tongue, arms or heart or
uctuation in skin re-
sistance(sweating). And can also have a source out side the body like interference of electrical
equipment nearby or varying impedance of the electrodes.
3.3 Artifact removal
Whenever artifacts are detected the a ected portion of the signal can be rejected. This can
be a valid pre-processing step and does not have to be a problem. However the problem with
deleting a speci c piece of data is that it can result in strange anomalies where the two pieces
are connected. Secondly, EEG data in general is relatively scarce. For that reason a better
approach is to remove the artifact from the EEG data. This goes one step further than artifact
3.4 Independent Component Analysis
Higher-order statistical methods simultaneously use the information of all the electrodes avail-
able. This o ers the possibility to locate a certain component and remove it from the data. One
method often applied is Independent Component Analysis (ICA) also known as blind source
ICA is a statistical computational spatial ltering method that decomposes the multi-electrode
data into underlying independent components (or as independent as possible). The goal is to
reveal hidden factors which underlie a certain dataset. ICA assumes linear independence of the
sources and that the sources are a linear combination of the witnessed output. ICA does not
take into account any 'ground-truth'-labels, which makes it an unsupervised method.
4 Magnetoencephalogram
Magneto-encephalography (MEG) is a technique for mapping brain activity by recording mag-
netic elds produced by electrical currents occurring naturally in the brain, using arrays of
SQUIDs (superconducting quantum interference devices). Applications of MEG include local-
izing regions a ected by pathology before surgical removal, determining the function of various
parts of the brain, and neurofeedback.
4.1 The basis of the MEG signal
Synchronized neuronal currents induce weak magnetic elds. At 10 femtotesla (fT) for cortical
activity and 103 fT for the human alpha rhythm, the brain's magnetic eld is considerably
smaller than the ambient magnetic noise in an urban environment, which is on the order of 108
fT or 10 T. The essential problem of biomagnetism is thus the weakness of the signal relative
to the sensitivity of the detectors, and to the competing environmental noise.
Figure 8: Magnetic eld of Brain
The MEG (and EEG) signals derive from the net e ect of ionic currents
owing in the
dendrites of neurons during synaptic transmission. In accordance with Maxwell's equations,
any electrical current will produce an orthogonally oriented magnetic eld(see gure 8). It
is this eld which is measured. The net currents can be thought of as electric dipoles, i.e.
currents with a position, orientation, and magnitude, but no spatial extent. According to the
right-hand rule, a current dipole gives rise to a magnetic eld that
ows around the axis of its
vector component.
5 f-NIRS
Functional near-infrared spectroscopy (fNIRs) has been introduced as a new neuroimaging
modality with which to conduct functional brain-imaging studies. fNIRs technology uses speci c
wavelengths of light, introduced at the scalp, to enable the noninvasive measurement of changes
in the relative ratios of deoxygenated hemoglobin (deoxy-Hb) and oxygenated hemoglobin (oxy-
Hb) during brain activity( see gure9). Wireless fNIRs system consists of personal digital
assistant (PDA) software controlling the sensor circuitry, reading, saving, and sending the
data via a wireless network. This technology allows the design of portable, safe, a ordable,
noninvasive, and minimally intrusive monitoring systems.
Figure 9: fNIR Device Working
The qualities of fNIRs make it an ideal candidate for monitoring cortical function in the
brain while subjects are engaged in various real life or experimental tasks. However, the noise
including in fNIRs is an important limitation on the use of optical data in these applications.
Motion artifact caused by moving of the head. Head movement can cause the NIR detectors to
shift and lose contact with the skin, exposing them to either ambient light or to light emitted
directly from the NIR sources or re
ected from the skin, rather than being re
ected from tissue
in regions of interest. These e ects cause sudden increases in the NIR data. Another noise can
cause the blood to move toward (or away from) the area that is being monitored, increasing
(or decreasing) the amount of oxygen, hence result in an increase (or decrease) in the measured
data. Hence, canceling noise from fNIRs signals is an important and necessary task in order to
deploy fNIRs as a brain monitoring technology in its full potential to many real life application
Adaptive ltering is one approach to dealing with noise signals. Adaptive ltering has been
widely used for noise reduction in other biomedical applications involving electrocardiogram
(ECG), EEG , and fNIRs.
5.1 Structure of fNIRs signals classi cation
Neural networks are very powerful tools for pattern recognition . The most well-known ad-
vantage is that after training them, neural networks can be readily used for process parameter
(or state) assessment without requiring any knowledge of the underlying system. In general, it
is necessary to preprocess their input information to eliminate irrelevant information from the
inputs and extract features of signals.
Figure 10: Structure of fNIRs signals classi cation
Here describe signal analysis to lter noises, feature extractions by wavelets techniques and
oine classi cation of the NIRS signal using Neural Networks. The structure of entire signals
processing is shown in Fig 10.
6 Conclusion
Measuring brain signals related to interfaces can lead to applications such as interface eval-
uation and adapta-tion. My thesis explores brain signals measured with fNIRS, use them to
adapt the interface and close the loop by connecting brain signals to the adaptable inter-face.
I am really enthusiastic about the potential for fNIRS and similar techniques to greatly en-
hance how people interact with computers. The creation of a brain-computer interface will
open opportunities for adapta-tion on di erent brain signals, with a device that is portable,
non-invasive and safe.

[1] \The future of brain-controlled devices". ACM, April 2010.
[2] \Audrey Girouard Computer Science Department Tufts University 161 College Ave Medford
USA audrey.girouard[at]". IBM J. Res. Dev.
[3] Pearlmutter B.A. Ward T.E. So-raghan C. Matthews, F. and Markham. \Hemodynamics
for Brain-Computer Interfaces". Signal Processing Magazine,IEEE, 2008.
[4] Leuthold AC Lewis SM Lynch JK Alonso AA Aslam Z Carpenter AF Georgopoulos A
Hemmy LS Koutlas IG Langheim FJ McCarten JR McPherson SE Pardo JV Pardo PJ
Parry GJ Rottunda SJ Segal BM Sponheim SR Stanwyck JJ Stephane M Westermeyer JJ
Georgopoulos AP, Karageorgiou E. \Synchronous neural interactions assessed by magne-
toencephalography: a functional biomarker for brain disorders". Signal Processing Maga-
zine,IEEE, Dec 2007.
Post: #24
i want seminars report on brain computer interface
Post: #25
can you send more info about brain computer interface

thank you

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