seminar report by shashank arora

JIET Group of Institutions

A Seminar Report on

(BRAIN COMPUTER INTERFACE)

In the partial fulfillment for the degree of B.Tech.

Seminar Presentation (8CS9)

Jodhpur Institute of Engineering & Technology (JIET)

Department of Computer Science & Engineering

(IV YEAR)

Guide Submitted by Pooja sexena shashank arora


(Sr. lecturer cse) Batch: B2

1. Acknowledgment

I would like to give a vote of thanks to all JIET family for their healthy and growing environment in seminar duration. I would also appreciate the support and guidance of my faculty in charge Ms. Pooja Saxena and staff of the Dept. of Computer Science and Engineering, JIET, Jodhpur for their invaluable guidance and immense interest shown in the seminar lab at every step.

JODHPUR INSTITUTE OF ENGINEERING AND TECHNOLOGIES(JIET)

Shashank Arora Computer Science JIET, Jodhpur

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2. Preface

Life is a journey, where each one of us crosses number of milestones. Every stoppage

teaches us a lot. I, being the student of B.tech, learnt a lot of things and was

bombarded with lots of learning, events, projects and seminars.

The last three-years of B.tech course helped me in lots of learning. Such has been the

presentations and projects which enhanced our learning by adding on to our world of

knowledge. And seminar is one of the part to enhance our communication skills.

In this seminar I like to represent about the combination of medical science and

Computer science that how this combination makes the phrase possible “FICTION

TO REALITY “that a system BRAIN COMPTURE INTERFACE is a system that

makes this sense possible. In this I present the basic principles of BCI, its

functionality and mainly its mathematical significance that help to understand the how

the BCI actually communicate human brain . BCI is collaboration in which a brain

Accepts and controls a mechanical device as a natural part of its representation

of the body. In this I elaborate the full steps of neural sensing to BCI output

Generating . the sub topics include in this seminar are fully define the BCI system

And mainly its application that are today’s using in advanced computer fields.

And one of the major advantage of controlling the computer by a human in visual way

And make possible vise versa property means controlling the handicap person with

The computer. Brain computer interface depends on different kinds of users which are

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Based on some standards that also shown and mainly the difference between the

Human computer interface (HCI) and brain computer interface (BCI) is compared Here.

3. Index

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ACKNOWLEDGEMENT--------------------------------------------------------------------1

2. PREFACE------------------------------------------------------------------------------------2

3. INDEX ----------------------------------------------------------------------------------------3

4. INTRODUCTION-----------------------------------------------------------------------------4

5. DETAILS ABOUT YOUR SEMINAR------------------------------------------------------5

6. ADVANTAGES----------------------------------------------------------------------------21

7. DISADVANTAGES-----------------------------------------------------------------------22

8. APPLICATION----------------------------------------------------------------------------23

9. CONLUSION------------------------------------------------------------------------------28

10. APPENDIX --------------------------------------------------------------------------------29

11. REFERENCES--------------------------------------------------------------------------------------30

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4. Introduction

Brain Computer Interface is simply an example of combination of medical science and computer science. So for this reason it is called as a system that makes the sense of fiction to reality. 4.1 General definition

A brain computer interface is a collaboration in which a brain accepts and controls a mechanical device as a natural part of its representation of the body. OR A brain computer interface is communication systems that do not depend on peripheral nerves and muscles. 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.

4.2 General principle In healthy subjects , primary motor areas sends movement commands to

muscles via spinal cord. In paralyzed people this pathway is interrupted. Computer based decoder translates this activity into commands for muscle

control.

4.3 Aim to develop brain computer interface (BCI) 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

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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.

5. Details information about the BCI

BCI A brain-computer interface 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.

5.1Components of BCI

Ways of majoring neural signals from the human brain,

Methods and algorithms for decoding brain states from these signals and

Methodology and algorithms for mapping the decoded brain activity to intended behavior or action.

5.2Approaches used in BCI

1. Invasive2. Semi invasive 3. Non-invasive

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5.2.1 Invasive BCI Invasive BCI research has targeted repairing damaged sight and providing new functionality to persons with paralysis. Invasive BCIs are implanted directly into the grey matter of the brain during neurosurgery. As they rest 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 lost as the body reacts to a foreign object in the brain. Invasive techniques, which implant electrodes directly onto a patient’s brain.

Implanted: grey matter Signals: highest quality Scar-tissue build-up Target:

◦ repairing damaged sight◦ providing new functionality to persons with paralysis◦ Artificial Vision System

5.2.2 partially-Invasive BCI 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.

Implanted: skull lower risk of forming scar-tissue in the brain Signal quality between invasive BCIs & non-invasive BCIs

5.2.3 Non-Invasive BCI 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.

poor signal resolution power muscle implants and restore partial movement Interfaces

◦ EEG◦ MEG◦ MRI

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Electroencephalography (EEG)

This procedure is the first non-invasive neuroimaging technique discovered. It measures the electrical activity of the brain. Due to its ease of use, cost and high temporal resolution this method is the most widely used one in BCIs nowadays. However, in practice EEGs are highly susceptible to noise and thus require a significant amount of user training in order to be operable in a BCI.

Magneto encephalography (MEG)

Though similar to the EEG in that it is a non-evasive technology the MEG is a much newer and more accurate technology. Instead of measuring the electrical activity in the brain this technology records magnetic fields produced by it. The main drawbacks of this technology are its high requirements in equipment. “Using MEG requires a room filled with super-conducting magnets and giant super-cooling helium tanks surrounded by shielded walls.”

Functional Magnetic Resonance Imaging (fMRI)

This technique measures the homodynamic response (blood flow and blood oxygenation) related to neural activity in the brain by the use of MRI (Magnetic Resonance Imaging formerly known as Magnetic Resonance Tomography MRT). The fact that there is a correlation between neural activity and the brain’s homodynamic makes the fMRI a neuroimaging tool. In contrast to the MRI which studies the brain’s structure this method studies the brain’s function. As this method requires MRI technology it needs very special equipment and thus is quite costly.

5.3Interfaces used in BCI 5.3.1 Direct Interfaces via EEG In the field of Body-Computer-Interfaces multiple approaches to an interface design have evolved. Many of these are in fact hepatic or linguistic interfaces. But for some people who experienced a cerebral apoplexy and are “locked-in”, trapped in their own body so to say, the operation of those devices is impossible. Therefore, a different technique – the direct communication with the brain via an electro-encephalogram (EEG) is applied here. In the following four different techniques and application examples are presented and compared subsequently

5.3.2 VAP interfaces

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VEP stands for visual evoked potential which is caused by a visual stimulation. When a person is exposed to commonly-used visual stimuli like flashing lights or flickering checkerboards, significant changes in the EEG of the visual cortex can be observed. For measuring an EEG, multiple electrodes have to be attached to the subject’s back of the head over the visual cortex as well as two electrodes on an earlap and on the brow for reference and grounding. When exposed to a checkerboard which changes its colors from black to white and back with a frequency of between 3 and 6 Hz the visual system emits a VEP that can be measured via the electrodes.

A classical application of the VEP-technique is the “Brain Response Interface” developed by Erich E. Sutter in 1992 [JOMA]. Although this is one of the first application examples it still has a remarkable performance in the face of speed. Sutter’s experiment consists of a checkerboard with 64 boxes. Measurements show that the EEGs have different characteristics with each field the subject focused. Sutter put the letters of an alphabet and some frequently-used English words into the 64 boxes and recorded all 64 EEGs for each Brain Computer Interfaces - 8 - Behm / Kollotzek / Hüske box/symbol. During operation a computer program compares the current EEGs with those in the database and then assembles words and sentences together. The problem with this experiment is that it requires the subject to permanently concentrate on the screen which can be very exhausting, especially when it flickers. This can be quite easily solved by Introducing a simple on/off switch in one of the boxes.

Instead of presenting a matrix of letters on the screen a keyboard is displayed. Each of the four rows of keys have a different color. In the four corners of the screen they put flickering LEDs in the corresponding colors. When the computer program registers a significant change in the EEG – so if the subject concentrates on one of the LEDs – it replaces the picture on the screen byanother one which only shows the focused row of keys. This is now again split into four blocks associated with a unique color, etc. The process is repeated until the desired letter is selected. So it requires three steps to select a letter.By reducing the number of possible signals from 64 to 4 the precision and thus the performance could be increased. With an average speed of 15 seconds per letter this is a quite respectable speed. Another idea of optimization is to make frequently used

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letters faster to access by reordering the keys by applying the Huffman code on the English language e. g.

5.3.3 P300 interfaces

P300 is a term for a significant positive curve on the EEG which appears 300ms after a relevant and seldom stimulation that does not necessarily have to be visual. The strongest signal can be obtained at the central parietal region which is located at the upper back of the head. This signal occurs involuntarily so no special training is required. Research has shown that stimuli with an erotic content lead to stronger P300 brain waves. Another study found out that P300 signals were

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weakerin tobacco-smokers, alcoholics and drug-dependent people. Nevertheless, a P300 characterizes a stimulus which is perceived by the subject as eminent. Dr. Larry Farwell and Emanual Donchin made use of the P300 and developed a typing tool that was made up of a matrix of 6 x 6 fields containing letters of the alphabet. To find out what letter the subject wants to type the program first highlights all six columns then the six rows consecutively. The program measures the P300 according to each highlighted column and row and finds out thestrongest signal. On this way the desired letter can be identified. Unfortunately, the error rate of this device initially was quite high. Still with a speed of around 2.3 letters per minute this is an acceptable method.

5.3.4. ERS/ERD-Interface The event related synchronization / desynchronization is a signal type that can be measured when the subject imagines a hand or foot movement. ERS / ERD was researched by a team at the technical university of Graz in Austria [CA_EEG]. They also developed an interface which allows movement of a cursor in a two dimensional space by combining hand and foot movement. After two training sessions three of the five test persons achieved a success rate between 89 and 100 percent, the two other persons only 51 and 60 percent. This could have the cause in different imaginations of hand movement each person has.

After a 62 training sessions with 160 trials a 25-year old paraplegic patient could move the cursor practically error-free. Regarding his immense handicap the accomplishment of 0.95 letters per minute is definitely respectable. Another experiment was done at the technical university Graz. They combined the ERS/ERD Interface with a FES (functional electrical stimulation) which is the stimulation of muscles using surface electrodes. A patient who had lost his ability to grasp with his hand is now no longer impeded.

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Patient using the combined ERS/D and FES technology To grasp a glass

5.3.5 SSVEP-Interface SSVEP stands for steady state visual evoked potential. This occurs when focusing on a flickering neon tube. It is a series of VEPs which is observed as steady amplitude on the EEG. The signal can be amplified using biofeedback which is nothing else than showing the subject current measurements. If properly trained the subject is able to increase or lower this amplitude and thus can trigger events if the amplitude goes above or below a certain threshold. The American research team around Matthew S. Middendorf developed a flight simulator that turned the plane to the right if the amplitude went over a threshold and to the left if below [BCIT_TP]. After an average of 6 hours per test person the success rate lay around 80 percent. Middendorf also experimented with a 2D cursor control using four LEDs flickering in distinct frequencies. The advantage of this method is that no biotraining is required as the subject does not have to control the amplitude of the SSVEPs. He or she only has to concentrate on one of the four LEDs to move the cursor up/down or left/right. It took 2.1 seconds in average for each

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LED with a success rate of 92 percent. The clear disadvantage is the extremely exhausting flickering of lights which also represents a hazard for epileptics.

5.4 Comparison between the interfaces used in BCI

System Training

duration Letters per minute

Error rate

VEP 10-60 mint 30 10%P300 5 mint 4 5%ERS/ERD 2-3h 1 <11%

5.5 Working of BCI

◦ Signal acquisition ◦ Signal processing◦ Devices◦ Interfaces◦ Controller◦ Data handler

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Signals from an array of neurons read. Cerebral electric activity recorded. Signals are amplified. Transmitted to computer Transformed to device control commands. Using computer chips and programs. Signals translated into action.

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http://hi.ctust.edu.tw/mediawiki/index.php/%E5%9C%96%E7%89%87:Bicp2.PNG%00%E5%A1%B9%EF%92%81%E1%B4%BB%E4%A1%BF%E2%B2%AF%E5%B6%82%E8%97%84%E6%8C%A7%00%00%EA%AE%A5


5.6 model of BCI

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6- Channel EEG BCI used. Sensory & motor cortices activated during attempts. Control scheme sends movement intention to Prosthetic Controller. Prosthetic returns force sensory information to Controller. Feedback processed and grip is adjusted.

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5.7 Steps of BCI processing

Preprocessing the Data

The main task of a BCI is to recognize patterns by interpreting sequences of numbers.

This automatically raises the question what these numbers are, i.e. where they com from.

The BCI is confronted with (integer) values coming out of an analog-to-digital converter .digitizing the output of an EEG machine connected to the user – or a digital EEG machine at a rate of (typically) 100 – 300 Hz per channel as raw input data. As there are usually up to 32 channels, the system has to deal with up to 10000 values per second.

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5.8 Aims of BCI

5.9 functinality of BCI

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Multiple aims

Study of brain functionsRehabilitation through substitutionthrough restorationEnhancement of brain-environment pathways“Cyborg-like” applications

5.7 technical requirements of BCI

End userClinical researchers, NeuroscientistsTechnical operators, TherapistsGamers, entertainers

Cost/benefit of requirements vary over User LevelTargeting to the disabled-user, others will be adequately addressed or easily adapted.

5.Cost/benefit of requirements vary over User Level


1. the patient manually visualize the cursor reaching the target ,2. the brain activity is interpreted by computer software ,3. the computer monitor displays the interpreted thought activity.

5.10Mathematical signification of BCI

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FREQUENCY BAND RANGEALPHA 8-13 HzBETA 14-30 HzTHETA 4-7 HzDELTA 0.5-3 Hz

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EEG Data Set: Mental TasksEEG Data Set: Mental Tasks

Resting task Imagined letter writingMental multiplicationVisualized countingGeometric object rotation

Keirn and Aunon, “A new mode of communication between man and his surroundings”, IEEE Transactions on Biomedical Engineering.


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Geometric Filtering of Noisy Time-SeriesGeometric Filtering of Noisy Time-Series

Given a data set

The Q fraction of a basis vector is defined as where

Signal Fraction OptimizationSignal Fraction Optimization

Determine such that D() is a maximum.

Solution via the GSVD equation


5.11Signals in BCI

Unknown (tall) m £ n signal matrix S Unknown mixing n £ n matrix A Observed m £ n data matrix X

Task: recover A and S from X alone.In general it is not possible to solve this problem.

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Signal Fraction Analysis Separation Signal Fraction Analysis Separation

Theorem: The solution to the signal fraction analysis optimization problem solves the signal separation problem X = SA given

1) is observed

2)

3)

In particular,

Where is the solution to the GSVD problem for signal fraction analysis.


5.11 Multiple points of views of BCI

Target users: Researchers (e.g., clinical researchers, neuroscientists, signal processing

experts, etc.); Technical operators (e.g., caregivers, therapists who are in charge of

training someone on BCI operation); End-users (e.g., people with disabilities who rely on the system for

communication) Casual end-users (e.g., those who use a BCI as an alternative input for

entertainment devices) Disciplines involved in research

Engineering Clinical ... Psychological Neuroscience

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Original signals (unknown) Mixed signals (observed)

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5.12Standardization of BCI

5.12.1 Role of technical standards in the development of BCI systems

helpful to foster involvement of companies into the field important to promote cooperation among research groups Topics for standardization: system architecture relationship with existing human-computer devices training procedures signal processing techniques indices of performance communication protocols with external devices

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6. ADVANTAGES OF BRAIN COMPUTER INTERFACE

Improved interoperability of componentso Lowers need for expertiseo Facilitates technology diffusiono Facilitates performance comparison

FDA/CE certification is cheaper Helps to solve legal disputes Decision making tool for operators

Documentation and reference for “good practice

BCI research allows us to develop a new class of bioengineering control devices and robots to provide daily life assistance to handicapped and elderly people.

Several potential applications of BCI hold promise for rehabilitation and improving performance, such as treating emotional disorders (for example, depression or anxiety), easing chronic pain, and overcoming movement disabilities due to stroke.

BCI can expand possibilities for advanced human computer interfaces (HCIs), making them more natural, flexible, efficient, secure, and user-friendly by enhancing the interaction between the brain, the eyes, the body, and a robot or a computer.

7. DISADVANTAGES OF BRAIN COMPUTER INTERFACE

EEGs measure tiny voltage potentials. The signal is weak and prone to interference.

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 equipment heavy (~10 lbs.) & hence not portable.It should have become clear already that the development of a BCI is not at all trivial. There are a lot of drawbacks and problems one is confronted with. In this section, all of the problematic aspects mentioned throughout this report shall be collected. They can best be summarized with the help of the following keywords

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1. _ Universality, 2. Equipment,3. Sensitivity, and4. Timing Issues

8. APPLICATION OF BCI

Apart from being a non-conventional input device for a computer we have found three main application fields for BCIs and BCI related devices which are more or less controversial:– Medical applications– Human enhancement– Human manipulation

Medical applications

BCIs provide a new and possibly only communication channel for people suffering from severe physical disabilities but having intact cognitive functions. For example these devices could help in treating (or rather overcoming) paraplegia or amyotrophia. Somewhat related to this topic is the field of Neuroprosthetics which deals with constructing and surgically implanting devices used for replacing damaged areas of the brain and more generally for neural damages of any kind.

Human enhancementDefinition “Human enhancement describes any attempt (whether temporary or permanent) to overcome the current limitations of human cognitive and physical abilities, whether through natural or artificial means.” Having this definition in mind one can think of many applications of the BCI in this field. For example BCIs could help facilitate communication systems in Cybernetic Organisms, Brainwave Synchronization, or even speculative things such as the Exocortex, among others

Human manipulation The notion that a BCI could allow a two-way communication between a human and a computer gives rise to more controversial potential uses of such a device. Using such a communication mechanism one could imagine directly influencing an individual’s thoughts, decisions, emotions or thinking. Of course, the

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mere “reading” of the mind could be put to criminal use, e.g. unwanted reading of passwords, locations, etc..

Medical applications(restoration of a communication channel for patients with lockedin syndrome and the control of neuroprostheses in patients affected by spinal cord injuries )

Military applications Counter terrorism(10 times faster image search) multimedia and virtual reality applications

BCI-operated robot

BCI-operated Environmental control

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Monitoring Biofeedback Detection of psychological states EEG-EMG-EOG integration

8.1 BCI PROJECTSBerlin Brain-Computer-Interface (BBCI)The Berlin Brain-Computer-Interface is a joint venture of several german research organisations. Members are:

The Institute Computer Architecture and Software Technology of the Fraunhofer Society

The research group Intelligent Data Analysis (IDA) The Neurophysics Research Group The department of Neurology at the Campus Benjamin Franklin of the Charité

University Medicine The Technical University Berlin

The BBCI project is sponsored by the Ministry for Education and Research of the German government.The goal of the project is the development of an EEG based BCI system. The applications of this system are on the one hand computer supported workplaces, to control a cursor via brain waves and on the other hand tools for paralyzed or paraplegic people.The BBCI project aims to shift the main learning effort to the computer. Therefore robust artificial learning and signal processing algorithms need to be developed to classify and interpret the brainwaves correctly.

Graz Brain-Computer-Interface

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The University of Graz, Austia has also a research project for Brain-Computer-Interfaces.Its main topics are:

Brain-Computer-InterfacesUsing EEG signals as input for computers.

Telemonitoring of BCIsRemote monitoring and administration of BCI systems

Combining BCI and virtual reality (VR) technology Using BCI systems to move in virtual realities Functional electrical stimulation Stimulation of limbs by electrical signals

BRAIN CONTROLLED ROBOTS

Robot hand mimics subject’s finger movements. Signals extracted and decoded by computer program. Transferred to hand shaped robot. To simulate original movement performed. Robot executes commands using onboard sensor readings.

‘BRAINGATE’ BCI

The ‘Braingate’ device can provide motor-impaired patients a mode of communication through the translation of thought into direct computer control.

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FEATURES OF BRAINGATE

Neural Interface Device. Consists of signal sensor and external processors. Converts neural signals to output signals. Sensor consists of tiny chip with electrode sensors. Chip implanted on brain surface. Cable connects sensor to external signal processor. Create communication o/p using decoding software.

ATR & HONDA DEVELOP NEW BCI

BCI for manipulating robots using brain signals. Enables decoding natural brain activity. MRI based neural decoding. No invasive incision of head and Brain. By tracking haemodynamic responses in brain. Accuracy of 85%

BCI2000

BCI2000 is an open-source, general purpose system for BCI research. It can also be used for data acquisition, stimulus presentation, and brain

monitoring applications. During operation, BCI2000 stores data in a common format (BCI2000 native

or GDF). BCI2000 also includes several tools for data import or conversion and export facilities into ASCII. BCI2000 also facilitates interactions with other software.

BCI FOR HEALTHY USERS

Induced disability. Ease of use in hardware. Ease of use in software. Otherwise unavailable Information. Improved training or performance. Confidentiality. Speed.

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Novelty

9. CONCLUSION

Depending on the use case each technology has its pros and cons, but when wanting to type letters VEP BCI is still the fastest and most efficient method. In the case of more analogue control ERS/ERD BCI is more applicable. Of course, there are many more techniques and application scenarios than shown in this paper, but these somehow show the basics. These can be combined in order to increase precision and functionality.Future applications will probably leverage a more detailed picture of brain waves and there is also a trend towards implants so that very specific signals can be filtered. Also, implants are of good use if he EEG BCI is too weak – e. g. as a result of a cerebral apoplexy.

A potential therapeutic tool. BCI System is nominated for the European ICT Grand Prize. Potentially high impact technology.

COMPUTATIONAL CHALLENGES AND FUTUREIMPLEMENTATIONS

Minimally invasive surgical methods. Next generation Neuroprosthesis. Vision prosthesis. BCI for totally paralyzed. Minimal number of calibration trials. Development of telemetry chip to collect data without external cables.

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10. APPENDIX

BCI Brain computer interfaceEEG ElectroencephalographyMEG MagnetoencephalographyfMRI Functional Magnetic Resonance

ImagingVEP visual evoked potentialSSVEP steady state visual evoked

potentialERS/ERD synchronization /

desynchronization

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11. REFERENCES

1. Books Human-Computer Interaction

Authors: Hewett, Baecker, Card, Carey, Gasen, Mantei, Perlman, Strong and Verplank

Pioneering research into Brain Computer InterfacesAuthor: Mark Wessel

The brain response interface: communication through visually induced electrical brain responses

Author: E.E. Sutter, 1992 2. Magazines, Journals Journal of Microcomputer Applications, v. 15, pp. 31-45

3. Web links

http://www.cs.man.ac.uk/aig/staff/toby/research/bci/richard.seabrook.brain.computer.interface.txt

http://www.icad.org/websiteV2.0/Conferences/ICAD2004/concert_call.htm http://faculty.washington.edu/chudler/1020.html http://www.biocontrol.com/eeg.html http://www.asel.udel.edu/speech/Spch_proc/eeg.html

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seminar report by shashank arora

Documents

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brain computer interface

brain computer interfacein

possible brain

human brain

computer screen

brain activity

human computer interface