BRAIN GATE SYSTEM

Submitted by

Brati Sundar Nanda

1011016238

ECE-E

CONTENTS:

Introduction History Principle behind BCI Types of BCI Implementation Applications Limitations Future Concerns Conclusion References

INTRODUCTIONBrain Gate is a brain implant system built by Cyberkinetics which implements the technology Brain-computer interface (BCI).

Brain-computer interface (BCI) is a fast-growing emergent technology, in which researchers aim to build a direct channel between the human brain and the computer.

A Brain Computer Interface (BCI) is a collaboration in which a brain accepts and controls a mechanical device as a natural part of its representation of the body.

BCI is often called a mind-machine interface (MMI), or sometimes called a direct neural interface(DNI) or a brain–machine interface (BMI)

HISTORY Research on BCIs has been going on for more than 20

years, but from the mid-1990s there has been a dramatic increase in working experimental implants.

Brain Gate system was commercially developed by the bio-tech company Cyberkinetics in 2003 in conjunction with the Department of Neuroscience at Brown University.

First of all it was implemented on rats then monkeys and after no complication on human beings

BCI RESEARCH ON ANIMALS

At first, rats were implanted

with BCI .Signals recorded from the

cerebral cortex of rat operate

BCI to carry out the movement

CONTINUED…… Researchers at the University

of Pittsburgh had demonstrated

on a monkey that can feed itself

with a robotic arm simply by

using signals from its brain.

PRINCIPLE BEHIND BCI

This technology is based on to sense, transmit, analyze the language of neurons and translate it in to computer commands .

It consist of a sensor that is implanted in the motor cortex of the brain and a device that analyses brain signals. The signals generated by brain are interpreted and translated into computer commands.

It consists of a silicon array about the size of an Aspirin tablet that contains about 100 electrodes each thinner than a human hair.

BLOCK DIAGRAM

Preprocessing

Detection

Control

Bio Feedback

Signal by neurochip

CONSTRUCTION AND WORKING

NEURO CHIP

Neuro chip

Chip uses 100 hair-thin electrodes that detects the communication between neurons in specific areas of the brain

INVASIVE AND SEMI INVASIN BCI

Invasive BCIs are implanted directly into the grey matter present in the motor cortex of the brain during neurosurgery.

As they rest in the grey matter, invasive devices produce the highest quality signals of all BCI devices.

But they are prone to scar- tissue buildup , causing the signal to become weaker or even lost as the body reacts to a foreign object in the brain.

Electrocorticography (ECoG) measures the electrical activity of the brain taken from beneath the skull

The electrodes are embedded in a thin plastic pad that is placed above the cortex, beneath the dura mater.

NON INVASIVE Electroencephalography (EEG), the recording is obtained by

placing electrodes on the scalp with a conductive gel or paste. Usually recording is done after preparing the scalp area by light

abrasion to reduce impedance due to dead skin cells Functional Magnetic Resonance Imaging (fMRI) exploits the

changes in the magnetic properties of hemoglobin which contains oxygen.

Activation of a part of the brain increases oxygen levels there increasing the ratio of oxyhemoglobin to deoxyhemoglobin.

Magnetoencephalography (MEG) detects the tiny magnetic fields created as individual neurons "fire" within the brain.

It can pinpoint the active region with a millimeter, and can follow the movement of brain activity .

Structure of HUMAN BRAIN

HOW BCI IMPLEMENTS?

A more difficult task is interpreting the brain signals for movement in someone who can't physically move his own arm. With a task like that, the subject must "train" to use the device.

With an implant in place, the subject would visualize closing his or her disabled hand. After many trials, the software can learn to recognize the signals associated with the thought of hand-closing.

Software connected to a robotic hand is programmed to receive the "close hand" signal and interpret it to mean that the robotic hand should close. At that point, when the subject thinks about closing the hand, the signals are sent and the robotic hand closes.

BCI ON HUMAN Over a period of nine months,

Mathew-Nagel(25-year-old man who had sustained a spinal cord injury leading to paralysis in all four limbs ) took part in 57 sessions.

During which the implanted Brain Gate sensor recorded activity in his motor cortex region while he imagined moving his paralyzed limbs and then used that imagined motion for several computer-based tasks such as, moving a computer cursor to open e-mail,draw shapes and play simple video games.

APPLICATIONS Provide disabled people with communication, environment ,

control, and movement restoration. Provide enhanced control of devices such as

wheelchairs ,vehicles , or assistance robots for people with disabilities

This technology provides the ability to control a video game by thought , ability to change TV channels with your mind etc.

Control robots that function in dangerous or inhospitable situations.

LIMITATIONS

At present the biggest obstacle of BCI technology is the lack of sensor mode that provides safe, accurate, and strong access to brain signals.

It is very expensive. Processing and Information transformation of data is

very time taking. Difficulty in adaptation and learning

FUTURE IMPLEMENTATION

Researches are going on for brain-to-brain communication.

Memory Upload/Download Dream Capture

CONCLUSION The results of BCI are spectacular and almost

unbelievable. BCI can help paralyzed people to move by

controlling their own electric wheelchairs, to communicate by using e-mail and Internet-based phone systems, and to be independent by controlling items such as televisions and electrical appliances.

Conclusively, BCI has proved to be a boon for paralyzed patients and all human being in future.

REFERENCES Dias, N.S. ; Jacinto, L.R. ; Mendes, P.M. ; Correia, J.H. “Visual gate for brain

computer interface” Engineering in Medicine and Biology Society, EMBC 2009. Annual International Conference of the IEEE year 2009

Levine, S.P. ; Huggins, J.E. ; BeMent, S.L. ; Kushwaha, R.K. ; Schuh, L.A. ; Rohde, M.M. ; Passaro, E.A. Ross, D.A. ; Elisevich, K.V. ; Smith, B.J. “A direct brain interface based on event-related potentials” Rehabilitation Engineering, IEEE Transactions on Volume:8 , Issue: 2 Publication Year: 2000

Huang, D. Kai Qian ; Oxenham, S. ; Ding-Yu Fei ; Ou Bai “Event-related desynchronization/ synchronization-based brain-computer interface towards volitional cursor control in a 2D center-out paradigm” Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2011 IEEE Symposium 11-15 April 2011

Dietmar Dietrich, Roland Lang, Dietmar Bruckner, Georg Fodor, and Brit Muller, "Limitations, Possibilities and Implications of Brain-Computer Interfaces“ Institute of Computer Technology, Vienna University of Technology, Austria, May 13-15, 2010

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