A SEMINAR REPORT ON BRAIN COMPUTER INTERFACE by Rahul Sharma PDF

Preview

Summary

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 LAKSHMI NARAIN COLLEGE OF...

Description

Accelerat ing t he world's research.

A SEMINAR REPORT ON BRAIN COMPUTER INTERFACE by Rahul Sharma Rahul Sharma

Related papers

Download a PDF Pack of t he best relat ed papers 

Rehabilit at ion of gait aft er st roke: a review t owards a t op-down approach Jose Pons Cort ically-Coupled Comput er Vision Paul Sajda EEG-Based Navigat ion from a Human Fact ors Perspect ive Anne-Marie Brouwer

A SEMINAR REPORT ON

BRAIN COMPUTER INTERFACE SESSION-2014

Submitted toPrachi Parashar (Assistant Professor- ECE)

Submitted By Rahul Sharma (0157EC121066)

DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING LAKSHMI NARAIN COLLEGE OF TECHNOLOGY & SCIENCE, BHOPAL I

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

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

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

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

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

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

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

―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

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

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

5

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

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

Figure 3.1 Invasive and Non-invasive method

8

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 (2–5 seconds), limited by the time required for blood to flow into the region.

9

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

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

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

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

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

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 realworld 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

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 ...