ARVIND PANDEYMCA 5TH SEM.

BRAIN MACHINE INTERFACE

OBJECTIVES

WHAT IS BRAIN MACHINE INTERFACE

NEED OF BRAIN MACHINE INTERFACE

MAIN PRINCIPLE AND WORKING

CHALLENGES

FUTURE EXPANSION

INTRODUCTION

Brain machine interface has several name like direct neural interface , brain–computer interface and it is a direct communication link between a brain and the outside world.

BMI uses brain activity to command, control, activate and communicate with the world by using peripheral devices and systems.

UNDERSTANDING BMI

The field of BMI has emerged in neuroprosthetics applications that aim at damaged hearing, sight and physical challenged.

WHY BMI

BMI PLATFORM

• Main principle behind this interface is the bioelectrical activity of nerves and muscles.

• Brain is composed of millions of neurons.

• When the neuron activates, there is a voltage change across the cell which generates signals on the surface of the brain.

• By monitoring and analyzing these signals we can understand the working of brain.

MAIN PRINCIPLE

Neural Interface Neural Signals

Signal Processing Algorithms/Command Extraction

Control Command

Vehicle State Signal

Sensors

Environmental Feedback

Directional

control

Brain-Controlled Vehicle

BLOCK DIAGRAM

IMPLANT DEVICE

SIGNAL PROCESSING SECTION

EXTERNAL DEVICE

FEEDBACK SECTION

Multichannel Acquisition Systems

Spike Detection

Signal Analysis

COMPONENTS OF BMI

EEG

ELECTRO ENCEPHALOGRAPHY (EEG) IS MEASUREMENT OF ELECTRICAL ACTIVITY PRODUCED BY BRAIN AS RECORDED FROM ELECTRODES PLACED ON THE SCALP.

IMPLANT DEVICE

• The EEG is measured with electrodes, which are placed on the scalp.

• Electrodes are small plates, which conduct electricity.

• They provide the electrical contact between the skin and the EEG recording apparatus.

Multichannel Acquisition Systems

At this section of amplification, initial filtering of EEG signal and sending these signal from instrument into a computer.

Spike Detection

Spike detection will allow the BMI to transmit only the action potential waveforms and their respective arrival times instead of the low signal, raw signal .

Signal Analysis

In this stage, digitized EEG signal which are input to the classifier.

Classifier recognize different mental tasks.

SIGNAL PROCESSING SECTION

EXTERNAL DEVICEThe classifier’s output is the input for the device control.

The device control simply transforms the classification to a particular action.

Examples are robotic arm, thought controlled wheel chair etc.

FEEDBACK DEVICE Feedback is needed for learning and for control.

Real-time feedback can dramatically improve the performance of a brain–machine interface.

classifier

1.Auditory and visual prosthetics

2.Functional-neuromuscular stimulation (FNS)

3.Prosthetics limb control

APPLICATIONS

CHALLENGES

CHALLENGES & LIMITATIONS

CHALLENGES Permanent damage to brain.

Virus attack on brain

Thought control and prediction of future thoughts.

Deletion or recording of memories.

LIMITATIONS

The brain is incredibly complex.

The signals are weak and interference can happen.

There are chemical processes Involved as well, which electrodes can’t pick up.

FUTURE EXPANSIONThought-communication device.

Super intelligent machines- Cyborgs.

• New research has demonstrated that it is possible

for communication from person to person through

the power of thought alone.

• A Cyborg is a Cybernetic Organism, human part machine.

• This will mean that robots, not humans, make all the important decisions .This may bring serious effects for humankind.

THANK YOU !!!

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