Paper presentation

Actions from Thoughts…!

Real-time Direct Brain–Machine Interfaces

ABSTRACT:

Science has made great strides in the past few decades towards uncovering the basic principles underlying the brain’s ability to receive sensation and control movement. These discoveries, along with revolutionary advances in computing power and microelectronics technology, have led to an emerging view that neural prosthetics, or electronic interfaces within the brain for restoration or augmentation of physiological function, may one day be possible.

Real-time direct interfaces between the brain and electronic and mechanical disease. Hybrid brain–machine interfaces have the potential to enhance our perceptual, motor and cognitive capabilities by revolutionizing the way we use computers and interact with remote environments. Brain-machine interface provides a way for people with damaged sensory/motor functions to use their brain to control artificial devices and restore lost capabilities

New techniques for microstimulating neuronal tissue and emerging developments in microchip design, computer science and robotics have the potential to coalesce into a new technology devoted to creating interfaces between the human brain and artificial devices. Such technology could allow patients to use brain activity to control electronic, mechanical or even virtual devices, leading to new therapeutic alternatives for restoring lost sensory, motor and even cognitive functions

INTRODUCTION

A brain-machine interface is an interface in which a brain accepts and controls a mechanical device as a natural part of its representation of the body. An immediate goal of brain-machine interface study is to provide a way for people with damaged sensory/motor functions to use their brain to control artificial devices and restore lost capabilities. By combining the latest developments in computer technology and hi-tech engineering, a person suffering from paralysis might be able to control a motorized wheelchair or a prosthetic limb by just thinking about it.

Before humans can use brain-interface techniques to control artificial devices, they must first understand how the brain gives commands. Brain-interface might work by recording neurological activity over long periods of time. The electrical activity of millions of brain cells (neurons) can be translated into precise sequences of skilled movements. Coordinated neuronal activity also provides with exquisite perceptual and sensorimotor capabilities.

The new technologies augment the human performance through the ability to noninvasive access codes in the brain in real time and integrate them into peripheral device or system operations.

BRAIN COMPUTER INTERFACE – AN EARLY DEVELOPMENT!

This describes the principles of a communication system called Brain Computer Interface (BCI). With this system user can control applications by using his/hers brain activity alone, no peripheral muscles or nerves are required. The brain activity can used for communication by classifying the activity to different tasks, which correspond to the functions in used application e.g. pressing a key or moving a mouse. The user concentrates to different mental tasks, which activate different functional areas

of the brain. This activity is measured as the Electroencepephalogaphy (EEG), and from

its certain features, usually the power spectrum of the EEG are extracted.

BCI is an interface in which a brain can talk with computer by

1. The computer system can learn what the brain is doing or going to do.

2. The brain can accept the command from computer.

EEG SIGNALS & MEASUREMENT - AN OVERVIEW:

The neurons in our brain communicate with each other by firing electrical impulses, this creates an electric field which travels though the cortex, the dura, the skull and the scalp. These electrical impulses are referred to as EEG. The fundamental assumption behind the EEG signal is that it reflects the dynamics of electrical activity in populations of neurons.

Frequency bands of the EEG :

.

Band	Frequency [Hz]	Amplitude [_V]	Location
Alpha (_)	8-12	10 -150	Occipital/Parietal regions
μ-rhythm	9-11	varies	Precentral/Postcentral regions
Beta (_)	14 -30	25	typically frontal regions
Theta (_)	4-7	varies	varies
Delta (_)	<3	varies	varies

......................processing updating data ..soon will be updated

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About Aditya Jaiswal

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