09-03-2012, 04:44 PM
BRAIN COMPUTER INTERFACE
INTRODUCTION:
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.
BCI History
The History of brain–computer interfaces (BCIs) starts with Hans Berger's discovery of the electrical activity of human brain and the development of Electroencephalography (EEG). In 1924 Berger was the first one who recorded an EEG from a human brain. By analyzing EEGs, Berger was able to identify different waves or rhythms which are present in a brain, 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 scalp of his patients. Those were replaced by silver foils which were attached to the patients head by rubber bandages later on.
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:
Neuroprosthetics is an area of neuroscience concerned with neural prostheses—using artificial devices to replace the function of impaired nervous systems and brain related problems or sensory organs. The most widely used neuroprosthetic device is the cochlear implant, which, as of 2006, has been implanted in approximately 100,000 people worldwide. There are also several neuroprosthetic devices that aim to restore vision, including retinal implants.
The differences between BCIs and neuroprosthetics are mostly in the ways 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 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
Rats implanted with BCIs in Theodore Berger's experiments
Several laboratories have managed to record signals from monkey and rat cerebral cortices to operate BCIs to carry out movement. Monkeys have navigated computer cursors on screen and commanded robotic arms to perform simple tasks simply by thinking about the task and without any motor output. In May 2008 photographs that showed a monkey operating a robotic arm with its mind at the Pittsburgh University Medical Center were published in a number of well known science journals and magazines. Other research on cats has decoded visual signals.
Early work
Monkey operating a robotic arm with brain–computer interfacing
The operant conditioning studies of Fetz and colleagues first showed that monkeys could learn to control the deflection of a biofeedback meter arm with neural activity.[7] Such 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.[8]
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 that monkeys moved their arms (based on a cosine function). He also found that dispersed groups of neurons in different areas of the brain collectively controlled motor commands but was only able to record the firings of neurons in one area at a time because of technical limitations imposed by his equipment.[9]
There has been rapid development in BCIs since the mid-1990s.[10] Several groups have been able to capture complex brain motor centre signals using recordings from neural ensembles (groups of neurons) and use these to control external devices, including research groups led by Richard Andersen, John Donoghue, Phillip Kennedy, Miguel Nicolelis, and Andrew Schwartz.
Prominent research successes
Phillip Kennedy and colleagues built the first intracortical brain–computer interface by implanting neurotrophic-cone electrodes into monkeys.
Yang Dan and colleagues' recordings of cat vision using a BCI implanted in the lateral geniculate nucleus (top row: original image; bottom row: recording)
In 1999, researchers led by Yang Dan at University of California, Berkeley decoded neuronal firings to reproduce images seen by cats. The team used an array of electrodes embedded in the thalamus (which integrates all of the brain’s sensory input) of sharp-eyed cats. Researchers targeted 177 brain cells in the thalamus lateral geniculate nucleus area, which decodes signals from the retina. The cats were shown eight short movies, and their neuron firings were recorded. Using mathematical filters, the researchers decoded the signals to generate movies of what the cats saw and were able to reconstruct recognizable scenes and moving objects.[11] Similar results in humans have since been achieved by researchers in Japan
Miguel Nicolelis has been a prominent proponent of using multiple electrodes spread over a greater area of the brain to obtain neuronal signals to drive a BCI. Such neural ensembles are said to reduce the variability in output produced by single electrodes, which could make it difficult to operate a BCI.
After conducting initial studies in rats during the 1990s, Nicolelis and his colleagues developed BCIs that decoded brain activity in owl monkeys and used the devices to reproduce monkey movements in robotic arms. Monkeys have advanced reaching and grasping abilities and good hand manipulation skills, making them ideal test subjects for this kind of work.
By 2000, the group succeeded in building a BCI that reproduced owl monkey movements while the monkey operated a joystick or reached for food. The BCI operated in real time and could also control a separate robot remotely over Internet protocol. But the monkeys could not see the arm moving and did not receive any feedback, a so-called open-loop BCI.
Diagram of the BCI developed by Miguel Nicolelis and colleagues for use on Rhesus monkeys
Later experiments by Nicolelis using rhesus monkeys succeeded in closing the feedback loop and reproduced monkey reaching and grasping movements in a robot arm. With their deeply cleft and furrowed brains, rhesus monkeys are considered to be better models for human neurophysiology than owl monkeys. The monkeys were trained to reach and grasp objects on a computer screen by manipulating a joystick while corresponding movements by a robot arm were hidden. The monkeys were later shown the robot directly and learned to control it by viewing its movements. The BCI used velocity predictions to control reaching movements and simultaneously predicted hand gripping force.
Other labs that develop BCIs and algorithms that decode neuron signals include John Donoghue from Brown University, Andrew Schwartz from the University of Pittsburgh and Richard Andersen from Caltech. These researchers were able to produce working BCIs even though they recorded signals from far fewer neurons than Nicolelis (15–30 neurons versus 50–200 neurons).
Donoghue's group reported training rhesus monkeys to use a BCI to track visual targets on a computer screen with or without assistance of a joystick (closed-loop BCI) Schwartz's group created a BCI for three-dimensional tracking in virtual reality and also reproduced BCI control in a robotic arm. The group created headlines when they demonstrated that a monkey could feed itself pieces of zucchini using a robotic arm controlled by the animal's own brain signals.
Andersen's group used recordings of premovement activity from the posterior parietal cortex in their BCI, including signals created when experimental animals anticipated receiving a reward.
In addition to predicting kinematic and kinetic parameters of limb movements, BCIs that predict electromyographic or electrical activity of muscles are being developed. Such BCIs could be used to restore mobility in paralyzed limbs by electrically stimulating muscles.
Miguel Nicolelis et al. showed that activity of large neural ensembles can predict arm position. This work made possible creation of brain–machine interfaces—electronic devices that read arm movement intentions and translate them into movements of artificial actuators. Carmena et al. programmed the neural coding in a brain–machine interface allowed a monkey to control reaching and grasping movements by a robotic arm, and Lebedev et al. argued that brain networks reorganize to create a new representation of the robotic appendage in addition to the representation of the animal's own limbs.
The biggest impediment of BCI technology at present is the lack of a sensor modality that provides safe, accurate, and robust access to brain signals. It is conceivable or even likely that such a sensor will be developed within the next twenty years. The use of such a sensor should greatly expand the range of communication functions that can be provided using a BCI.
Development and implementation of a Brain–Computer Interface (BCI) system is complex and time consuming. In response to this problem, Dr. Gerwin Schalk has been developing a general-purpose system for BCI research, called BCI2000. BCI2000 has been in development since 2000 in a project led by the Brain–Computer Interface R&D Program at the Wadsworth Center of the New York State Department of Health in Albany, New York, USA.
A new 'wireless' approach uses light-gated ion channels such as Channelrhodopsin to control the activity of genetically defined subsets of neurons in vivo. In the context of a simple learning task, illumination of transfected cells in the somatosensory cortex influenced the decision making process of freely moving mice.
The Annual BCI Award, endowed with 3,000 USD, is an accolade to recognize outstanding and innovative research done in the field of Brain-Computer Interfaces. Each year, a renowned research laboratory is asked to judge the submitted projects and to award the prize. The jury consists of world-leading BCI experts recruited by the awarding laboratory. Cuntai Guan, Kai Keng Ang, Karen Sui Geok Chua, Beng Ti Ang from A*STAR in Singapore with the project "Motor imagery-based Brain-Computer Interface robotic rehabilitation for stroke" won the BCI Award 2010.
