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Page 1: Brain Gate

SEMINAR ON BRAIN GATE

By SIMRANMTECH(CSE)U.R.No:100236582172

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Contents

• What is Brain Gate?• Objectives of Brain Gate• Types • History• Research• Literature Survey• Base paper Discussion• Conclusion• Applications• Challenges• Further Concerns

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What is Brain Gate/Brain Machine Interface/Brain Computer Interface???

Brain gate is the electrode chip which can be implemented in the brain.

When it is implemented in brain , the

Electrical signal exchanged by neurons

within the brain.

Those signals are sent to the brain and

it executes body movement.All the

signalling process is handled by special

software.The signal sends to the

computer and then the computer is

controlled by patient.

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Objective of Brain Gate!!!!

The goal of Brain Gate program is to develop a fast and reliable connection between the brain of a severely disabled person and a personal computer . The ‘Brain Gate’ device can provide paralysed or motor-impaired patients a mode of communication through the translation of thought into direct computer control.

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Types of Brain Computer Interface(BCI)

One way BCI Computers either accept commands

from the brain or send signals to it.

Two way BCI Allow brains and external devices to

exchange information in both directions.

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History of Brain Gate…

Research on BCIs has been going on for more than 30 years ,but from the mid-1990s there has been a dramatic increase in working experimental implants.

Brain gate was developed by the bio-tech company Cyberkinetics in 2003 conjunction with the department of Neuroscience at Brown University.

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Brain Gate Research In animals:

At first , rats were implanted with BCI.

Signals recorded from the cerebral cortex of rat to operate BCI to carry out the movement.

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FACT!!!!

Researchers at university of Pittsburgh has demonstrated on a monkey that can feed itself with a robotic arm simply by using signals from its brain

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It Worked !

• Using only its mind the monkey was able to control a cursor on a computer monitor via Brain Gate.

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Since there were no complications in trials with monkeys

Next Step: Humans!

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In December 7,2004,brain-computer interface had been clinically tested on a human by an American company Cyberkinetics.

Over a period of nine months ,he took part in 57 sessions during which the implanted Brain Gate sensor recorded activity in motor cortex.

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PRINCIPLE:

The system consists of a sensor that is implanted on the motor cortex of the brain(Pedestal) and a Brain Gate Neural Interface Device that Analyzes the brain signal.

The principle is that the intact brain functions , brain signals are generated even though they are not sent to the arms, hands and legs .The signals are interpreted and translated into cursor movements ,offering the user an alternate “BRAIN GATE PATHWAY”.

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This is one of electrode implemented in brain.Individual electrodes are 1-mm long and spaced 4mm apart ,in a 10x10 grid.The electrical signal exchanged by neurons within brain . Those signals are sent when the brain executes a body movement.

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Software behind Brain Gate…

The computers translate brain activity and create the communication output usingCustom decoding software.

System uses adaptive algorithms and pattern-Matching techniques to Facilitate communication.The algorithms are written in C,JAVA,MATLAB

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ILLUSTRATION:In Foxborough ,a 25-year old quadriplegic sits in a wheelchair with wires coming out of a bottle-cap-size connector stuck in his skull. Using just his thoughts , he was playing the computer game Pong.

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LITERATURE SURVEY

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The research in this area has been ongoing over the past three decades. In reviewing the literature it is noted that, “Some chose to develop devices that could be steered by brain waves detected outside the head; research more than 30 years ago showed that people can be trained to control these patterns.”1 Past research has included the implantation of electrodes into rhesus monkeys for activities such as controlling robotic arms. Rebecca Zacks notes, 2 “…the electrodes intercept signals from individual neurons in the brain and a specially developed computer algorithm translated these signals into trajectories and velocities for the computer cursor. The researchers’ ambitions, however, extend way beyond video-game playing monkeys. Their hope is that their brain-machine-interface system will give patients paralyzed by spinal-cord injuries or neurodegenerative diseases new abilities to Interact with the world around them – using nothing more than the power of their thoughts.”

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Base Paper Discussion

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Simultaneous Design of FIR Filter Banks and Spatial Patterns for EEG Signal Classification.IEEE Trans Biomed Eng. 2012 Sep 4. [Epub ahead of print]AbstractThe spatial weights for electrodes called common spatial pattern (CSP) are known to be effective in EEG signal classification for motor imagery based brain computer interface (MI-BCI). To achieve accurate classification in CSP, it is necessary to find frequency bands that relate to brain activities associated with BCI tasks. Several methods that determine such a set of frequency bands have been proposed. However, the existing methods cannot find the multiple

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frequency bands by using only learning data. To address this problem, we propose discriminative filter bank CSP (DFBCSP) that designs finite impulse response filters and the associated spatial weights by optimizing an objective function which is a natural extension of that of CSP. The optimization is conducted by sequentially and alternatively solving subproblems into which the original problem is divided. By experiments, it is shown that DFBCSP can effectively extract discriminative features for MI-BCI. Moreover, experimental results exhibit that DFBCSP can detect and extract the bands related to brain activities of motor imagery.

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The analytic common spatial patterns method for EEG-based BCI data.J Neural Eng. 2012 Aug;9(4):045009. Epub 2012 Jul 25.

AbstractOne of the most important stages in a brain-computer interface (BCI) system is that of extracting features that can reliably discriminate data recorded during different user states. A popular technique used for feature extraction in BCIs is the common spatial patterns (CSP) method, which provides a set of spatial filters that optimally discriminate between two classes of data in

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the least-squares sense. The method also yields a set of spatial patterns that are associated with the most relevant activity for distinguishing between the two classes. The high recognition rates that have been achieved with the method Have led to its widespread adoption in the field. The high recognition rates that have been achieved with the method have led to its widespread adoption in the field. Here, a variant of the CSP method that considers EEG data in its complex form is described. By explicitly considering the amplitude and phase information in the data, the analytic CSP (ACSP) technique can provide a more comprehensive picture of the underlying activity, resulting in improved classification accuracies and more informative spatial

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spatial patterns than the conventional CSP method. In this paper, we elaborate on the theoretical aspects of the ACSP algorithm and demonstrate the advantages of the method through a number of simulations and through tests on EEG data.PMID: 22832090 [PubMed - in process]

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People with paralysis control robotic arms using brain-computer interfacePROVIDENCE, R.I. [Brown University] — On April 12, 2011,

One small stepA 58-year-old woman, paralyzed by a stroke for almost 15 years, uses her thoughts to control a robotic arm, grasp a bottle of coffee, serve herself a drink, and return the bottle to the table.

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CONCLUSION:

According to the Cyberkinetics’ website , two patients have been implanted with the Brain Gate System.

Using the system,called Brain Gate , the patient can read e-mail , play games, turn lights on or off and change channels or adjust the volume of a T.V. set.

In early test sessions, the patient was able to control the TV and carry on conversation and move his head at the same time.The results are spectacular and almost unbelievable.

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APPLICATIONS:

In classification of EEG signal.In multimedia communication.In evaluation of spike detection algorithms.Actuated control of mobile robot by human EEG.In evaluating the machine learning algorithms.

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Challenges faced by Brain Gate…

It is very expensive.Limitation in information transform rate. The latest technology is 20 bits/min.Difficulty in adaptation and learning.

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Further concerns…

Emphasis should be on improving the information transform rate.

Stronger algorithm should be implemented.