14-07-2012, 10:51 AM
BRAIN COMPUTER INTRFACE
[attachment=27610]
ABSTRACT
Brain Computer Interfaces (BCI) are systems, which use human thought as a communication channel between Brain and Computer. The BCI system does not depend on the brain’s normal pathways of peripheral nerves and muscles rather transforms specific ‘thoughts’, which is a synonym for these specific states, into control signals. These are then converted into computer readable form so that they can be fed to the computer. This computer now will interpret the instructions it receives from the brain. These instructions are nothing but our thoughts.The immediate requirement of a BCI system to work is to acquire the signals generated by the brain. It is believed that the brain radiates certain electromagnetic signal during thought process. When a person thinks about performing a particular task such as raising a finger, this gives rise to brain activity that can be measured through the scalp via electronic sensors called Electrodes. A BCI relies specifically on the analysis of brain signals created by the different mental tasks. Measured signals do, however, contain background Noise. The challenge is to identify those particular signal characteristics that relate to those particular mental tasks.
The brain’s electrical activity can be measured and monitored by a variety of non-invasive and efficient ways. The non-invasive methods now available include Electroencephalography (EEG), Magneto Encephalography (MEG), Positron Emission Tomography (PET), and Functional Magnetic Resonance Imaging (FMRI). PET, MEG and FMRI are expensive and complex to operate and therefore not practical in most applications. At present, only EEG, which is easily recorded and processed with inexpensive equipment, appears to offer the practical possibility of non-invasive communication channel. Further more, EEG signals are rather well studied and there is evidence that subject can control them to some extent in a voluntary manner. Assessment and interpretation of the Brainwaves, recorded as Electroencephalograph, is a crucial process in a BCI system. The EEG signals recorded are needed to be analyzed and converted to the computer readable form so that the computer can analyze and perform the required task represented by the signal it receives. This involves the EEG signal processing to extract and present the frequency and amplitude information .Different methods of signal processing are used to interpret these signals which include Multi-variety Autoregressive Analysis, Fourier Transformation and Fast Fourier Transformation of the signals.
INTRODUCTION
A brain–computer interface (BCI), often called a mind-machine interface (MMI), or sometimes called a direct neural interface or a brain–machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions.
Research on BCIs began in the 1970s at the University of California Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature.
The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s.
History
The history of brain–computer interfaces (BCIs) starts with Hans Berger's discovery of the electrical activity of the human brain
and the development of electroencephalography (EEG). In 1924 Berger was the first to record human brain activity by means of EEG. By analyzing EEG traces, Berger was able to identify oscillatory activity in the brain, such as the alpha wave (8–12 Hz), also known as Berger's wave.
Berger's first recording device was very rudimentary. He inserted silver wires under the scalps of his patients. These were later replaced by silver foils attached to the patients' head by rubber bandages. Berger connected these sensors to a Lippmann capillary electrometer, with disappointing results. More sophisticated measuring devices, such as the Siemens double-coil recording galvanometer, which displayed electric voltages as small as one ten thousandth of a volt, led to success.
Berger analyzed the interrelation of alternations in his EEG wave diagrams with brain diseases. EEGs permitted completely new possibilities for the research of human brain activities.
BCI versus neuroprosthetics.
Main article: Neuroprosthetics
Neuroprosthetics is an area of neuroscience concerned with neural prostheses. That is, using artificial devices to replace the function of impaired nervous systems and brain related problems, or of sensory organs. The most widely used neuroprosthetic device is the cochlear implant which, as of 2006, had been implanted in approximately 100,000 people worldwide. There are also several neuroprosthetic devices that aim to restore vision, including retinal implants.
The difference between BCIs and neuroprosthetics is mostly in how the terms are used: neuroprosthetics typically connect the nervous system to a device, whereas BCIs usually connect the brain (or nervous system) with a computer system. Practical neuroprosthetics can be linked to any part of the nervous system—for example, peripheral nerves—while the term "BCI" usually designates a narrower class of systems which interface with the central nervous system.
The terms are sometimes, however, used interchangeably. Neuroprosthetics and BCIs seek to achieve the same aims, such as restoring sight, hearing, movement, ability to communicate, and even cognitive function. Both use similar experimental methods and surgical techniques.
Animal BCI research
A rat implanted with a BCI as part of Theodore Berger's experiments
Several laboratories have managed to record signals from monkey and rat cerebral cortices to operate BCIs to produce movement. Monkeys have navigated computer cursors on screen and commanded robotic arms to perform simple tasks simply by thinking about the task and seeing the visual feedback, but without any motor output. In May 2008 photographs that showed a monkey, at the University of Pittsburgh Medical Center, operating a robotic arm by thinking, were published in a number of well known science journals and magazines. Other research, on cats, has decoded their neaural visual signals.
Early work
Monkey operating a robotic arm with brain–computer interfacing (Schwartz lab, University of Pittsburgh)
In 1969 the operant conditioning studies of Fetz and colleagues, at the Regional Primate Research Center and Department of Physiology and Biophysics, University of Washington School of Medicine in Seattle, showed for the first time that monkeys could learn to control the deflection of a biofeedback meter arm with neural activity. Similar work in the 1970s established that monkeys could quickly learn to voluntarily control the firing rates of individual and multiple neurons in the primary motor cortex if they were rewarded for generating appropriate patterns of neural activity.
Studies that developed algorithms to reconstruct movements from motor cortex neurons, which control movement, date back to the 1970s. In the 1980s, Apostolos Georgopoulos at Johns Hopkins University found a mathematical relationship between the electrical responses of single motor cortex neurons in rhesus macaque monkeys and the direction in which they moved their arms (based on a cosine function). He also found that dispersed groups of neurons, in different areas of the monkey's brains, collectively controlled motor commands. But he was able to record the firings of neurons in only one area at a time, because of the technical limitations imposed by his equipment.
There has been rapid development in BCIs since the mid-1990s. Several groups have been able to capture complex brain motor cortex signals by recording from neural ensembles (groups of neurons) and using these to control external devices. Notable research groups have been led by Richard Andersen, John Donoghue, Phillip Kennedy, Miguel Nicolelis and Andrew Schwartz.
[attachment=27610]
ABSTRACT
Brain Computer Interfaces (BCI) are systems, which use human thought as a communication channel between Brain and Computer. The BCI system does not depend on the brain’s normal pathways of peripheral nerves and muscles rather transforms specific ‘thoughts’, which is a synonym for these specific states, into control signals. These are then converted into computer readable form so that they can be fed to the computer. This computer now will interpret the instructions it receives from the brain. These instructions are nothing but our thoughts.The immediate requirement of a BCI system to work is to acquire the signals generated by the brain. It is believed that the brain radiates certain electromagnetic signal during thought process. When a person thinks about performing a particular task such as raising a finger, this gives rise to brain activity that can be measured through the scalp via electronic sensors called Electrodes. A BCI relies specifically on the analysis of brain signals created by the different mental tasks. Measured signals do, however, contain background Noise. The challenge is to identify those particular signal characteristics that relate to those particular mental tasks.
The brain’s electrical activity can be measured and monitored by a variety of non-invasive and efficient ways. The non-invasive methods now available include Electroencephalography (EEG), Magneto Encephalography (MEG), Positron Emission Tomography (PET), and Functional Magnetic Resonance Imaging (FMRI). PET, MEG and FMRI are expensive and complex to operate and therefore not practical in most applications. At present, only EEG, which is easily recorded and processed with inexpensive equipment, appears to offer the practical possibility of non-invasive communication channel. Further more, EEG signals are rather well studied and there is evidence that subject can control them to some extent in a voluntary manner. Assessment and interpretation of the Brainwaves, recorded as Electroencephalograph, is a crucial process in a BCI system. The EEG signals recorded are needed to be analyzed and converted to the computer readable form so that the computer can analyze and perform the required task represented by the signal it receives. This involves the EEG signal processing to extract and present the frequency and amplitude information .Different methods of signal processing are used to interpret these signals which include Multi-variety Autoregressive Analysis, Fourier Transformation and Fast Fourier Transformation of the signals.
INTRODUCTION
A brain–computer interface (BCI), often called a mind-machine interface (MMI), or sometimes called a direct neural interface or a brain–machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions.
Research on BCIs began in the 1970s at the University of California Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature.
The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s.
History
The history of brain–computer interfaces (BCIs) starts with Hans Berger's discovery of the electrical activity of the human brain
and the development of electroencephalography (EEG). In 1924 Berger was the first to record human brain activity by means of EEG. By analyzing EEG traces, Berger was able to identify oscillatory activity in the brain, such as the alpha wave (8–12 Hz), also known as Berger's wave.
Berger's first recording device was very rudimentary. He inserted silver wires under the scalps of his patients. These were later replaced by silver foils attached to the patients' head by rubber bandages. Berger connected these sensors to a Lippmann capillary electrometer, with disappointing results. More sophisticated measuring devices, such as the Siemens double-coil recording galvanometer, which displayed electric voltages as small as one ten thousandth of a volt, led to success.
Berger analyzed the interrelation of alternations in his EEG wave diagrams with brain diseases. EEGs permitted completely new possibilities for the research of human brain activities.
BCI versus neuroprosthetics.
Main article: Neuroprosthetics
Neuroprosthetics is an area of neuroscience concerned with neural prostheses. That is, using artificial devices to replace the function of impaired nervous systems and brain related problems, or of sensory organs. The most widely used neuroprosthetic device is the cochlear implant which, as of 2006, had been implanted in approximately 100,000 people worldwide. There are also several neuroprosthetic devices that aim to restore vision, including retinal implants.
The difference between BCIs and neuroprosthetics is mostly in how the terms are used: neuroprosthetics typically connect the nervous system to a device, whereas BCIs usually connect the brain (or nervous system) with a computer system. Practical neuroprosthetics can be linked to any part of the nervous system—for example, peripheral nerves—while the term "BCI" usually designates a narrower class of systems which interface with the central nervous system.
The terms are sometimes, however, used interchangeably. Neuroprosthetics and BCIs seek to achieve the same aims, such as restoring sight, hearing, movement, ability to communicate, and even cognitive function. Both use similar experimental methods and surgical techniques.
Animal BCI research
A rat implanted with a BCI as part of Theodore Berger's experiments
Several laboratories have managed to record signals from monkey and rat cerebral cortices to operate BCIs to produce movement. Monkeys have navigated computer cursors on screen and commanded robotic arms to perform simple tasks simply by thinking about the task and seeing the visual feedback, but without any motor output. In May 2008 photographs that showed a monkey, at the University of Pittsburgh Medical Center, operating a robotic arm by thinking, were published in a number of well known science journals and magazines. Other research, on cats, has decoded their neaural visual signals.
Early work
Monkey operating a robotic arm with brain–computer interfacing (Schwartz lab, University of Pittsburgh)
In 1969 the operant conditioning studies of Fetz and colleagues, at the Regional Primate Research Center and Department of Physiology and Biophysics, University of Washington School of Medicine in Seattle, showed for the first time that monkeys could learn to control the deflection of a biofeedback meter arm with neural activity. Similar work in the 1970s established that monkeys could quickly learn to voluntarily control the firing rates of individual and multiple neurons in the primary motor cortex if they were rewarded for generating appropriate patterns of neural activity.
Studies that developed algorithms to reconstruct movements from motor cortex neurons, which control movement, date back to the 1970s. In the 1980s, Apostolos Georgopoulos at Johns Hopkins University found a mathematical relationship between the electrical responses of single motor cortex neurons in rhesus macaque monkeys and the direction in which they moved their arms (based on a cosine function). He also found that dispersed groups of neurons, in different areas of the monkey's brains, collectively controlled motor commands. But he was able to record the firings of neurons in only one area at a time, because of the technical limitations imposed by his equipment.
There has been rapid development in BCIs since the mid-1990s. Several groups have been able to capture complex brain motor cortex signals by recording from neural ensembles (groups of neurons) and using these to control external devices. Notable research groups have been led by Richard Andersen, John Donoghue, Phillip Kennedy, Miguel Nicolelis and Andrew Schwartz.