27-11-2012, 05:04 PM
A Brain-Machine Interface
[attachment=41366]
ABSTRACT
A brain-machine interface is a communication system that does not depend on the brains normal output pathways of peripheral nerves and muscles. It is a new communication link between a functioning human brain and the outside world. These are electronic interfaces with the brain, which has the ability to send and receive signals from the brain. BMI uses brain activity to command, control, actuate and communicate with the world directly through brain integration with peripheral devices and systems. The signals from the brain are taken to the computer via the implants for data entry without any direct brain intervention. BMI transforms mental decisions and/or reactions into control signals by analyzing the bioelectrical brain activity.
While linking the brain directly with machines was once considered science fiction, advances over the past few years have made it increasingly viable. It is an area of intense research with almost limitless possibilities. The human brain is the most complex physical system we know of, and we would have to understand its operation in great detail to build such a device. 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, paralyzed persons will be able to control a motorized wheel chair, computer painter, or robotic arm by thought alone. In this era where drastic diseases are getting common it is a boon if we can develop it to its full potential. Recent technical and theoretical advances, have demonstrated the ultimate feasibility of this concept for a wide range of space-based applications. Besides the clinical purposes such an interface would find immediate applications in various technology products also.
INTRODUCTION
Picture a time when humans see in the UV and IR portions of the electromagnetic spectrum, or hear speech on the noisy flight deck of an aircraft carrier; or when soldiers communicate by thought alone. Imagine a time when the human brain has its own wireless modem so that instead of acting on thoughts, war fighters have thoughts that act. Imagine that one day we will be able to download vast amounts of knowledge directly to our brain! So as to cut the lengthy processes of learning everything from scratch. Instead of paying to go to university we could pay to get a "knowledge implant" and perhaps be able to obtain many lifetimes worth of knowledge and expertise in various fields at a young age.
When we talk about high end computing and intelligent interfaces, we just cannot ignore robotics and artificial intelligence. In the near future, most devices would be remote/logically controlled. Researchers are close to breakthroughs in neural interfaces, meaning we could soon mesh our minds with machines. This technology has the capability to impact our lives in ways that have been previously thought possible in only sci-fi movies.
Brain-Machine Interface (BMI) is a communication system, which enables the user to control special computer applications by using only his or her thoughts. It will allow human brain to accept and control a mechanical device as a part of the body. Data can flow from brain to the outside machinery, or to brain from the outside machinery. Different research groups have examined and used different methods to achieve this. Almost all of them are based on electroencephalography (EEG) recorded from the scalp. Our major goal of such research is to create a system that allows patients who have damaged their sensory/motor nerves severely to activate outside mechanisms by using brain signals.
BRAIN MACHINE INTERFACE
A brain-machine interface (BMI) is an attempt to mesh our minds with machines. It is a communication channel from a human's brain to a computer, which does not resort to the usual human output pathways as muscles. It is about giving machine-like capabilities to intelligence, asking the brain to accommodate synthetic devices, and learning how to control those devices much the way we control our arms and legs today. These experiments lend hope that people with spinal injuries will be able to someday use their brain to control a prosthetic limb, or even their own arm. A BMI could, e.g., allow a paralyzed patient to convey her/his intentions to a computer program. But also applications in which healthy users can benefit from the direct brain computer communication are conceivable, e.g., to speed up reaction times. Initially theses interactions are with peripheral devices, but ultimately it may be interaction with another brain. The first peripheral devices were robotic arms. Our approach bases on an artificial neural network that recognizes and classifies different brain activation patterns associated with carefully selected mental tasks. Using BMI artificial electrical signal can stimulate the brain tissue in order to transmit some particular sensory information.
THE HUMAN BRAIN
All of it happens in the brain. The brain is undoubtly the most complex organ found among the carbon-based life forms. So complex it is that we have only vague information about how it works. The average human brain weights around 1400 grams. The most relevant part of brain concerning BMI’s is the cerebral cortex. The cerebral cortex can be divided into two hemispheres. The hemispheres are connected with each other via corpus callosum. Each hemisphere can be divided into four lobes. They are called frontal, parietal, occipital and temporal lobes. Cerebral cortex is responsible for many higher order functions like problem solving, language comprehension and processing of complex visual information. The cerebral cortex can be divided into several areas, which are responsible of different functions. This kind of knowledge has been used when with BCI’s based on the pattern recognition approach. The mental tasks are chosen in such a way that they activate different parts of the cerebral cortex.
MAIN PRINCIPLE
Main principle behind this interface is the bioelectrical activity of nerves and muscles. It is now well established that the human body, which is composed of living tissues, can be considered as a power station generating multiple electrical signals with two internal sources, namely muscles and nerves.
We know that brain is the most important part of human body. It controls all the emotions and functions of the human body. The brain is composed of millions of neurons. These neurons work together in complex logic and produce thought and signals that control our bodies. When the neuron fires, or activates, there is a voltage change across the cell, (~100mv) which can be read through a variety of devices. When we want to make a voluntary action, the command generates from the frontal lobe. Signals are generated on the surface of the brain. These electric signals are different in magnitude and frequency.
By monitoring and analyzing these signals we can understand the working of brain. When we imagine ourselves doing something, small signals generate from different areas of the brain. These signals are not large enough to travel down the spine and cause actual movement. These small signals are, however, measurable. A neuron depolarizes to generate an impulse; this action causes small changes in the electric field around the neuron. These changes are measured as 0 (no impulse) or 1 (impulse generated) by the electrodes. We can control the brain functions by artificially producing these signals and sending them to respective parts. This is through stimulation of that part of the brain, which is responsible for a particular function using implanted electrodes.
ELECTROENCEPHALOGRAPHY
Electroencephalography (EEG) is a method used in measuring the electrical activity of the brain. The brain generates rhythmical potentials which originate in the individual neurons of the brain. These potentials get summated as millions of cell discharge synchronously and appear as a surface waveform, the recording of which is known as the electroencephalogram.
The neurons, like other cells of the body, are electrically polarized at rest. The interior of the neuron is at a potential of about –70mV relative to the exterior. When a neuron is exposed to a stimulus above a certain threshold, a nerve impulse, seen as a change in membrane potential, is generated which spreads in the cell resulting in the depolarization of the cell. Shortly afterwards, repolarization occurs.
The EEG signal can be picked up with electrodes either from scalp or directly from the cerebral cortex. As 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. The EEG is measured from the surface of the scalp by measuring potential difference between the actual measuring electrode and a reference electrode.
BMI APPROACHES
What are the thoughts the user thinks in order to control a BMI? An ideal BMI could detect the user’s wishes and commands directly. However, this is not possible with today’s technology. Therefore, BMI researches have used the knowledge they have had of the human brain and the EEG in order to design a BMI. There are basically two different approaches that have been used. The first one called a pattern recognition approach is based on cognitive mental tasks. The second one called an operant conditioning approach is based on the self-regulation of the EEG response.
In the first approach the subject concentrates on a few mental tasks. Concentration on these mental tasks produces different EEG patterns. The BCI (or the classifier in particular) can then be trained to classify these patterns.
BLOCK DESCRIPTION
The BMI consists of several components: 1.the implant device, or chronic multi-electrode array, 2.the signal recording and processing section, 3.an external device the subject uses to produce and control motion and 4.a feedback section to the subject. The first component is an implanted array of microelectrodes into the frontal and parietal lobes—areas of the brain involved in producing multiple output commands to control complex muscle movements. This device record action potentials of individual neurons and then represent the neural signal using a rate code .The second component consists of spike detection algorithms, neural encoding and decoding systems, data acquisition and real time processing systems etc .A high performance dsp architecture is used for this purpose. The external device that the subject uses may be a robotic arm, a wheel chair etc. depending upon the application. Feedback is an important factor in BCI’s. In the BCI’s based on the operant conditioning approach, feedback training is essential for the user to acquire the control of his or her EEG response. However, feedback can speed up the learning process and improve performance.
BMI COMPONENTS
A brain-machine interface (BMI) in its scientific interpretation is a combination of several hardware and software components trying to enable its user to communicate with a computer by intentionally altering his or her brain waves. The task of the hardware part is to record the brainwaves– in the form of the EEG signal – of a human subject, and the software has to analyze that data. In other words, the hardware consists of an EEG machine and a number of electrodes scattered over the subject’s skull. The EEG machine, which is connected to the electrodes via thin wires, records the brain-electrical activity of the subject, yielding a multi-dimensional (analog or digital) output. The values in each dimension (also called channel) represent the relative differences in the voltage potential measured at two electrode sites.
EXTERNAL DEVICE
The classifier’s output is the input for the device control. The device control simply transforms the classification to a particular action. The action can be, e.g., an up or down movement of a cursor on the feedback screen or a selection of a letter in a writing application. However, if the classification was “nothing” or “reject”, no action is performed, although the user may be informed about the rejection. It is the device that subject produce and control motion. Examples are robotic arm, thought controlled wheel chair etc
FEDBACK
Real-time feedback can dramatically improve the performance of a brain–machine interface. Feedback is needed for learning and for control. Real-time feedback can dramatically improve the performance of a brain–machine interface. In the brain, feedback normally allows for two corrective mechanisms. One is the ‘online’ control and correction of errors during the execution of a movement. The other is learning: the gradual adaptation of motor commands, which takes place after the execution of one or more movements.
In the BMIs based on the operant conditioning approach, feedback training is essential for the user to acquire the control of his or her EEG response. The BMIs based on the pattern recognition approach and using mental tasks do not definitely require feedback training. However, feedback can speed up the learning process and improve performance. Cursor control has been the most popular type of feedback in BMIs. Feedback can have many different effects, some of them beneficial and some harmful. Feedback used in BMIs has similarities with biofeedback, especially EEG biofeedback.
[attachment=41366]
ABSTRACT
A brain-machine interface is a communication system that does not depend on the brains normal output pathways of peripheral nerves and muscles. It is a new communication link between a functioning human brain and the outside world. These are electronic interfaces with the brain, which has the ability to send and receive signals from the brain. BMI uses brain activity to command, control, actuate and communicate with the world directly through brain integration with peripheral devices and systems. The signals from the brain are taken to the computer via the implants for data entry without any direct brain intervention. BMI transforms mental decisions and/or reactions into control signals by analyzing the bioelectrical brain activity.
While linking the brain directly with machines was once considered science fiction, advances over the past few years have made it increasingly viable. It is an area of intense research with almost limitless possibilities. The human brain is the most complex physical system we know of, and we would have to understand its operation in great detail to build such a device. 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, paralyzed persons will be able to control a motorized wheel chair, computer painter, or robotic arm by thought alone. In this era where drastic diseases are getting common it is a boon if we can develop it to its full potential. Recent technical and theoretical advances, have demonstrated the ultimate feasibility of this concept for a wide range of space-based applications. Besides the clinical purposes such an interface would find immediate applications in various technology products also.
INTRODUCTION
Picture a time when humans see in the UV and IR portions of the electromagnetic spectrum, or hear speech on the noisy flight deck of an aircraft carrier; or when soldiers communicate by thought alone. Imagine a time when the human brain has its own wireless modem so that instead of acting on thoughts, war fighters have thoughts that act. Imagine that one day we will be able to download vast amounts of knowledge directly to our brain! So as to cut the lengthy processes of learning everything from scratch. Instead of paying to go to university we could pay to get a "knowledge implant" and perhaps be able to obtain many lifetimes worth of knowledge and expertise in various fields at a young age.
When we talk about high end computing and intelligent interfaces, we just cannot ignore robotics and artificial intelligence. In the near future, most devices would be remote/logically controlled. Researchers are close to breakthroughs in neural interfaces, meaning we could soon mesh our minds with machines. This technology has the capability to impact our lives in ways that have been previously thought possible in only sci-fi movies.
Brain-Machine Interface (BMI) is a communication system, which enables the user to control special computer applications by using only his or her thoughts. It will allow human brain to accept and control a mechanical device as a part of the body. Data can flow from brain to the outside machinery, or to brain from the outside machinery. Different research groups have examined and used different methods to achieve this. Almost all of them are based on electroencephalography (EEG) recorded from the scalp. Our major goal of such research is to create a system that allows patients who have damaged their sensory/motor nerves severely to activate outside mechanisms by using brain signals.
BRAIN MACHINE INTERFACE
A brain-machine interface (BMI) is an attempt to mesh our minds with machines. It is a communication channel from a human's brain to a computer, which does not resort to the usual human output pathways as muscles. It is about giving machine-like capabilities to intelligence, asking the brain to accommodate synthetic devices, and learning how to control those devices much the way we control our arms and legs today. These experiments lend hope that people with spinal injuries will be able to someday use their brain to control a prosthetic limb, or even their own arm. A BMI could, e.g., allow a paralyzed patient to convey her/his intentions to a computer program. But also applications in which healthy users can benefit from the direct brain computer communication are conceivable, e.g., to speed up reaction times. Initially theses interactions are with peripheral devices, but ultimately it may be interaction with another brain. The first peripheral devices were robotic arms. Our approach bases on an artificial neural network that recognizes and classifies different brain activation patterns associated with carefully selected mental tasks. Using BMI artificial electrical signal can stimulate the brain tissue in order to transmit some particular sensory information.
THE HUMAN BRAIN
All of it happens in the brain. The brain is undoubtly the most complex organ found among the carbon-based life forms. So complex it is that we have only vague information about how it works. The average human brain weights around 1400 grams. The most relevant part of brain concerning BMI’s is the cerebral cortex. The cerebral cortex can be divided into two hemispheres. The hemispheres are connected with each other via corpus callosum. Each hemisphere can be divided into four lobes. They are called frontal, parietal, occipital and temporal lobes. Cerebral cortex is responsible for many higher order functions like problem solving, language comprehension and processing of complex visual information. The cerebral cortex can be divided into several areas, which are responsible of different functions. This kind of knowledge has been used when with BCI’s based on the pattern recognition approach. The mental tasks are chosen in such a way that they activate different parts of the cerebral cortex.
MAIN PRINCIPLE
Main principle behind this interface is the bioelectrical activity of nerves and muscles. It is now well established that the human body, which is composed of living tissues, can be considered as a power station generating multiple electrical signals with two internal sources, namely muscles and nerves.
We know that brain is the most important part of human body. It controls all the emotions and functions of the human body. The brain is composed of millions of neurons. These neurons work together in complex logic and produce thought and signals that control our bodies. When the neuron fires, or activates, there is a voltage change across the cell, (~100mv) which can be read through a variety of devices. When we want to make a voluntary action, the command generates from the frontal lobe. Signals are generated on the surface of the brain. These electric signals are different in magnitude and frequency.
By monitoring and analyzing these signals we can understand the working of brain. When we imagine ourselves doing something, small signals generate from different areas of the brain. These signals are not large enough to travel down the spine and cause actual movement. These small signals are, however, measurable. A neuron depolarizes to generate an impulse; this action causes small changes in the electric field around the neuron. These changes are measured as 0 (no impulse) or 1 (impulse generated) by the electrodes. We can control the brain functions by artificially producing these signals and sending them to respective parts. This is through stimulation of that part of the brain, which is responsible for a particular function using implanted electrodes.
ELECTROENCEPHALOGRAPHY
Electroencephalography (EEG) is a method used in measuring the electrical activity of the brain. The brain generates rhythmical potentials which originate in the individual neurons of the brain. These potentials get summated as millions of cell discharge synchronously and appear as a surface waveform, the recording of which is known as the electroencephalogram.
The neurons, like other cells of the body, are electrically polarized at rest. The interior of the neuron is at a potential of about –70mV relative to the exterior. When a neuron is exposed to a stimulus above a certain threshold, a nerve impulse, seen as a change in membrane potential, is generated which spreads in the cell resulting in the depolarization of the cell. Shortly afterwards, repolarization occurs.
The EEG signal can be picked up with electrodes either from scalp or directly from the cerebral cortex. As 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. The EEG is measured from the surface of the scalp by measuring potential difference between the actual measuring electrode and a reference electrode.
BMI APPROACHES
What are the thoughts the user thinks in order to control a BMI? An ideal BMI could detect the user’s wishes and commands directly. However, this is not possible with today’s technology. Therefore, BMI researches have used the knowledge they have had of the human brain and the EEG in order to design a BMI. There are basically two different approaches that have been used. The first one called a pattern recognition approach is based on cognitive mental tasks. The second one called an operant conditioning approach is based on the self-regulation of the EEG response.
In the first approach the subject concentrates on a few mental tasks. Concentration on these mental tasks produces different EEG patterns. The BCI (or the classifier in particular) can then be trained to classify these patterns.
BLOCK DESCRIPTION
The BMI consists of several components: 1.the implant device, or chronic multi-electrode array, 2.the signal recording and processing section, 3.an external device the subject uses to produce and control motion and 4.a feedback section to the subject. The first component is an implanted array of microelectrodes into the frontal and parietal lobes—areas of the brain involved in producing multiple output commands to control complex muscle movements. This device record action potentials of individual neurons and then represent the neural signal using a rate code .The second component consists of spike detection algorithms, neural encoding and decoding systems, data acquisition and real time processing systems etc .A high performance dsp architecture is used for this purpose. The external device that the subject uses may be a robotic arm, a wheel chair etc. depending upon the application. Feedback is an important factor in BCI’s. In the BCI’s based on the operant conditioning approach, feedback training is essential for the user to acquire the control of his or her EEG response. However, feedback can speed up the learning process and improve performance.
BMI COMPONENTS
A brain-machine interface (BMI) in its scientific interpretation is a combination of several hardware and software components trying to enable its user to communicate with a computer by intentionally altering his or her brain waves. The task of the hardware part is to record the brainwaves– in the form of the EEG signal – of a human subject, and the software has to analyze that data. In other words, the hardware consists of an EEG machine and a number of electrodes scattered over the subject’s skull. The EEG machine, which is connected to the electrodes via thin wires, records the brain-electrical activity of the subject, yielding a multi-dimensional (analog or digital) output. The values in each dimension (also called channel) represent the relative differences in the voltage potential measured at two electrode sites.
EXTERNAL DEVICE
The classifier’s output is the input for the device control. The device control simply transforms the classification to a particular action. The action can be, e.g., an up or down movement of a cursor on the feedback screen or a selection of a letter in a writing application. However, if the classification was “nothing” or “reject”, no action is performed, although the user may be informed about the rejection. It is the device that subject produce and control motion. Examples are robotic arm, thought controlled wheel chair etc
FEDBACK
Real-time feedback can dramatically improve the performance of a brain–machine interface. Feedback is needed for learning and for control. Real-time feedback can dramatically improve the performance of a brain–machine interface. In the brain, feedback normally allows for two corrective mechanisms. One is the ‘online’ control and correction of errors during the execution of a movement. The other is learning: the gradual adaptation of motor commands, which takes place after the execution of one or more movements.
In the BMIs based on the operant conditioning approach, feedback training is essential for the user to acquire the control of his or her EEG response. The BMIs based on the pattern recognition approach and using mental tasks do not definitely require feedback training. However, feedback can speed up the learning process and improve performance. Cursor control has been the most popular type of feedback in BMIs. Feedback can have many different effects, some of them beneficial and some harmful. Feedback used in BMIs has similarities with biofeedback, especially EEG biofeedback.