presented by:

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P.MADHAVI

St.Ann’s college of engg & tech.

III B.Tech(E.C.E)

Chirala

Prakasam(Dt)

Email:

V.PADMALATHA

St.Ann’s college of engg & tech

III B.Tech(E.C.E)

Chirala

Prakasam(Dt)

Email:

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ABSTRACT:

Many physiological disorders such as Amyotrophic Lateral Sclerosis (ALS) or injuries such as high-level spinal cord injury can disrupt the communication path between the brain and the body. People with severe motor disabilities may lose all voluntary muscle control, including eye movements. These people are forced to accept a reduced quality of life, resulting in dependence on caretakers and escalating social costs . Most of the existing assistive technology devices for these patients are not possible because these devices are dependant on motor activitiesspecific parts of the body. Alternative control paradigms for these individuals are thus desirable.

Brain-computer interface (BCI) has emerged as a new frontier in assistive technology (AT) since it could provide an alternative communication channel between a user’s brain and the outside world . Other terms that are also used in the literature for referring to a BCI system include: brain interface (BI), direct brain interface (DBI), and brain machine interface (BMI).

A BCI system allows individuals with motor disabilities to control objects in their environments (such as a light switch in their room or television, wheelchairs, neural prosthesis and computers) using their brain signals only.This could be accomplished by measuring specific features of the user’s brain activity that relate to his/her intent to perform the control. This specific type of brain activity is termed a “neurological phenomenon”.

BCI, is a communication system in which the brain does not use nerves to give orders to your body or to the world outside. "A BCI provides its user with an alternative method for acting on the world". Brain-computer interface (BCI) is a direct connection between computer(s) and the human brain. Currently research is being conducted the fields of neuroscience and neuroengineering regarding BCI and BMI. Using chips implanted against the brain that have hundreds of pins less than the width of a human hair protruding from them and penetrating the cerebral cortex, scientists are able to read the firings of hundreds of neurons in the brain.

Research on BCIs began in the 1970s, but it wasn't until the mid-1990s that the first working experimental implants in humans appeared. Following years of animal experimentation, early working implants in humans now exist, designed to restore damaged hearing, sight and movement.

Man-Machine interface has been one of the growing fields of research and development in recent years. Most of the effort has been dedicated to the design of user-friendly or ergonomic systems by means of innovative interfaces such as voice recognition, virtual reality. A direct brain-computer interface would add a new dimension to man-machine interaction. Interesting research work in this direction has been already initiated, motivated by the hope to create new communication channels for those with severe motor disabilities.

INTRODUCTION TO HOW THE BCI WORK:

As the power of modern computers grows along side our understanding of the human brain, we move ever closer to making some pretty spectacular science fiction into reality. Imagine transmitting signals directly to someone's brain that would allow them to see, hear or feel specific sensory inputs. Consider the potential to manipulate computers or machinery with nothing more than a thought. It isn't about convenience -- for severely disabled people, development of a brain-computer interface (BCI) could be the most important technological breakthrough in decades.

The electrical brain:
The reason a BCI works at all is because of the way our brains function. Our brains are filled with neurons, individual nerve cells connected to one another by dendrites and axons. Every time we think, move, feel or remember something, our neurons are at work. That work is carried out by small electric signals that zip from neuron to neuron as fast as 250 mph [source: Walker]. The signals are generated by differences in electric potential carried by ions on the membrane of each neuron.

Although the paths the signals take are insulated by something called myelin, some of the electric signal escapes. Scientists can detect those signals, interpret what they mean and use them to direct a device of some kind. For example, researchers could figure out what signals are sent to the brain by the optic nerve when someone sees the color red. They could rig a camera that would send those exact signals into someone's brain whenever the camera saw red, allowing a blind person to "see" without eyes.

BCI Input and Output

One of the biggest challenges facing brain-computer interface researchers today is the basic mechanics of the interface itself. The easiest and least invasive method is a set of electrodes -- a device known as an electroencephalograph (EEG) -- attached to the scalp. The electrodes can read brain signals. However, the skull blocks a lot of the electrical signal, and it distorts what does get through.

To get a higher-resolution signal, scientists can implant electrodes directly into the gray matter of the brain itself, or on the surface of the brain, beneath the skull. This allows for much more direct reception of electric signals and allows electrode placement in the specific area of the brain where the appropriate signals are generated. This approach has many problems, however. It requires invasive surgery to implant the electrodes, and devices left in the brain long-term tend to cause the formation of scar tissue in the gray matter. This scar tissue ultimately blocks signals.

Regardless of the location of the electrodes, the basic mechanism is the same: The electrodes measure minute differences in the voltage between neurons. The signal is then amplified and filtered. In current BCI systems, it is then interpreted by a computer program, although you might be familiar with older analogue encephalographs, which displayed the signals via pens that automatically wrote out the patterns on a continuous sheet of paper.

In the case of a sensory input BCI, the function happens in reverse. A computer converts a signal, such as one from a video camera, into the voltages necessary to trigger neurons. The signals are sent to an implant in the proper area of the brain, and if everything works correctly, the neurons fire and the subject receives a visual image corresponding to what the camera sees.

Another way to measure brain activity is with a Magnetic Resonance Image (MRI). An MRI machine is a massive, complicated device. It produces very high-resolution images of brain activity, but it can't be used as part of a permanent or semipermanent BCI. Researchers use it to get benchmarks for certain brain functions or to map where in the brain electrodes should be placed to measure a specific function.

FUNCTIONAL COMPONENTS OF BCI:

Figure 3 shows a traditional BCI system in which a person controls a device in an operating environment (e.g., a powered wheelchair in a house) through a series of functional components (revised from [FAT06]). In this context, the user’s brain activity is used to generate IC commands that operate the BCI system. The user monitors the state of the device to determine the result of his/her control efforts.

Figure 3 . The functional components of a BCI system

The building components of a BCI system (shown in Figure 3) have the following tasks: the electrodes placed on the head of the user record the brain signal (e.g., electroencephalography (EEG) signals from the scalp, electrocorticography (ECoG) signals from the brain or neuronal activity recorded using microelectrodes implanted in the brain). The ‘artifact processor’ block deals with artifacts in the EEG signals after the signals have been amplified. This block can either remove artifacts from the EEG signals or can simply mark some EEG epochs as artifact-contaminated. The ‘feature generator’ block transforms the resultant signals into feature values that relate to the underlying neurological phenomena employed by the user for control. For example, if the user is using the power of his/her Mu (8-12Hz) rhythm for the purpose of control, the feature generator could continually generate features relating to the power-spectral estimates of the user’s Mu rhythms. The feature generator generally consists of three components: the ‘signal enhancement’, the ‘feature extraction’, and the ‘feature selection’ components, as shown in Figure 3.

In some BCI designs, ‘signal enhancement’ or some of form of ‘pre-processing’ is performed to increase the signal-to-noise ratio of the brain signal(s) prior to extracting the features. To reduce the dimensionality of the problem, it is desired to reduce the number of features and/or the number of EEG channels. ‘Feature selection’ could be performed after or at the feature extraction stage to reduce the number of features and/or EEG channels used. Ideally, the features that are meaningful or useful in the classification stage are identified and chosen, while others are omitted.

The ‘feature translator’ block translates the features into logical control signals, e.g., 0 and 1 where 0 denotes NC and 1 denotes IC. The translation algorithm uses linear classification methods (e.g., linear discriminant analysis) or nonlinear ones (e.g., neural networks). As shown in Figure 3 , a feature translator may consist of two components: ‘feature classification’ and ‘post-processing’. The main aim of the feature classification component is to classify the features into logical control signals. Post-processing methods such as a moving average may be used after feature classification to reduce the number of activations of the system.

The control interface translates the logical control signals from the feature translator into semantic control signals that are appropriate for the particular type of device used. Finally, the device controller translates the semantic control signals into physical control signals that are used by the device.

BCI Applications:

One of the most exciting areas of BCI research is the development of devices that can be controlled by thoughts. Some of the applications of this technology may seem frivolous, such as the ability to control a video game by thought. If you think a remote control is convenient, imagine changing channels with your mind.

However, there's a bigger picture -- devices that would allow severely disabled people to function independently. For a quadriplegic, something as basic as controlling a computer cursor via mental commands would represent a revolutionary improvement in quality of life. But how do we turn those tiny voltage measurements into the movement of a robotic arm?

Early research used monkeys with implanted electrodes. The monkeys used a joystick to control a robotic arm. Scientists measured the signals coming from the electrodes. Eventually, they changed the controls so that the robotic arm was being controlled only by the signals coming form the electrodes, not the joystick.

A more difficult task is interpreting the brain signals for movement in someone who can't physically move their own arm. With a task like that, the subject must "train" to use the device. With an EEG or implant in place, the subject would visualize closinghis or herright hand. After many trials, the software can learn 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.

A similar method is used to manipulate a computer cursor, with the subject thinking about forward, left, right and back movements of the cursor. With enough practice, users can gain enough control over a cursor to draw a circle, access computer programs and control a TV [source: Ars Technica]. It could theoretically be expanded to allow users to "type" with their thoughts.

Once the basic mechanism of converting thoughts to computerized or robotic action is perfected, the potential uses for the technology are almost limitless. Instead of a robotic hand, disabled users could have robotic braces attached to their own limbs, allowing them to move and directly interact with the environment. This could even be accomplished without the "robotic" part of the device. Signals could be sent to the appropriate motor control nerves in the hands, bypassing a damaged section of the spinal cord and allowing actual movement of the subject's own hands.

BCI Drawbacks :

Although we already understand the basic principles behind BCIs, they don't work perfectly. There are several reasons for this.

1.  The brain is incredibly complex. To say that all thoughts or actions are the result of simple electric signals in the brain is a gross understatement. There are about 100 billion neurons in a human brain. Each neuron is constantly sending and receiving signals through a complex web of connections. There are chemical processes involved as well, which EEGs can't pick up on.

2.  The signal is weak and prone to interference. EEGs measure tiny voltage potentials. Something as simple as the blinking eyelids of the subject can generate much stronger signals. Refinements in EEGs and implants will probably overcome this problem to some extent in the future, but for now, reading brain signals is like listening to a bad phone connection. There's lots of static.

3.  The equipment is less than portable. It's far better than it used to be -- early systems were hardwired to massive mainframe computers. But some BCIs still require a wired connection to the equipment, and those that are wireless require the subject to carry a computer that can weigh around 10 pounds. Like all technology, this will surely become lighter and more wireless in the future.

COUNCLUSION:

Invasive BCI research has targeted repairing damaged sight and providing new functionality to paralysed people The prospect of BCIs and brain implants of all kinds have been important themes in science fiction.. The brain-computer interface provides new ways for individuals to interact with their environment. The computer will continue to be a necessary component as long as detecting a brain response reliably remains a complex analytical task. In most cases, the brain response itself is not new, just the means of detecting it and applying it as a control. However, the necessary feedback associated with experimental trials frequently resulted in improved, or at least changed performance. Little is known about the long-term effects of such training either from an individual differences, or from a basic human physiology point of view.

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