Brain Computer Interface (Bci) - Essay Example

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 girlfriend’s 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 machine interaction. A brain-computer interface, sometimes called a direct neural Interface or a brain machine Interface, is a direct communication pathway between a human or animal brain(or brain cell culture) and an external vice.

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In one BPCS, computers either accept commands from the brain or send signals to it but not both. Two way BPCS will allow brains and external devices to exchange information in both directions but have yet to be successfully implanted in animals or humans. Brain-Computer Interface Is a staple of science fiction writing. In Its earliest incarnations no mechanism was thought necessary. As the technology seemed so far fetched that no explanation was likely. As more became known about the brain however, the possibility has become more real and the science fiction more technically sophisticated.

Recently, the cyberpunk movement has adopted the idea of ‘Jacking in’, sliding ‘bistros’ chips into slots implanted in the skull(Gibson, W. 1 984). Although such boosts are still science fiction, there have been several recent steps toward Interfacing the brain and computers. In this definition, the word brain means the brain or nervous system of an organic life form rather than the mind. Computer means any processing or computational device, from simple circuits to silicon chips (including hypothetical future technologies like quantum computing).

Research on BPCS has been going on for more than 30 years UT from the mid sass’s there has been dramatic Increase working experimental Implants. The common thread Department of Computer Science and Engineering, GEE, BBS throughout the research is the remarkable cortical-plasticity of the brain, which often adapts to PC’s treating prostheses controlled by implants and natural limbs. With recent advances in technology and knowledge, pioneering researches could now conceivably attempt to produce PC’s that augment human functions rather than simply restoring them, previously only the realm of science fiction.

Fig. 1 . 1: Schematic diagram off BCC system 2 Pragmatic Ran Patria Chapter 2. Working architecture 2. 1 . Introduction: Before moving to real implications of BCC and its application let us first discuss the three types of BCC. These types are decided on the basis of the technique used for the interface. Each of these techniques has some advantages as well as some disadvantages. The three types of BCC are as follows with there features: 2. 2. Invasive BCC: Invasive BCC are directly implanted into the grey matter of the brain during neurosurgery.

They produce the highest quality signals of BCC devices . Invasive PC’s has targeted repairing damaged sight and providing new functionality o paralyzed people. But these PC’s are prone to building up of scar-tissue which causes the signal to become weaker and even lost as body reacts to a foreign object in the brain. Fig. 2. 2. 1 : Jensen Neumann, a man with acquired blindness, being interviewed about his vision BCC on Cab’s The Early Show In vision science, direct brain implants have been used to treat non-congenital I. E. Interface to restore sight as private researcher, William Double. Double’s first prototype was implanted into Jerry, a man blinded in adulthood, minion. A single-array BCC containing 68 electrodes was implanted onto Jerry visual cortex and succeeded in producing phosphates, the sensation of seeing light. The system included TV cameras mounted on glasses to send signals to the implant. Initially the implant allowed Jerry to see shades of grey in a limited field of vision and at a low frame-rate also requiring him to be hooked up to a two-ton mainframe.

Shrinking electronics and faster computers made his artificial eye more portable and allowed him to perform simple tasks unassisted. In 2002, Jensen Neumann, also blinded in adulthood, became the first in a series of 16 paying patients to receive Double’s second enervation implant, marking one of the earliest commercial uses of BPCS. The second generation device used a more sophisticated implant enabling better mapping of phosphates into coherent vision. Phosphates are spread out across the visual field in what researchers call the starry-night effect.

Immediately after his implant, Jensen was able to use imperfectly restored vision to drive slowly around the parking area of the research institute. PC’s focusing on motor Neuropsychiatry aim to either restore movement in paralyzed individuals or provide devices to assist them, such as interfaces with computers or robot arms. Researchers at Emory University in Atlanta led by Philip Kennedy and Roy Bake were first to install a brain implant in a human that produced signals of high enough quality to stimulate movement.

Their patient, Johnny Ray, suffered from ‘locked-in syndrome’ after suffering a brain-stem stroke. Rays implant was installed in 1998 and he lived long enough to start working with the implant, eventually learning to control a computer cursor. Therapeutic Matt Angle became the first person to control an artificial hand using a BCC in 2005 as part of the nine-month human trail of cyber kinetics Neurophysiology Brainwave chip-implant. Implanted in Angle’s right prenatal gurus(area of the motor cortex for arm movement), the 96 electrode his hand as well as a computer cursor, lights and TV. 2. 3. Partially Invasive BCC: Partially invasive BCC devices are implanted inside the skull but rest outside the brain rather than amidst the grey matter. They produce better resolution signals than noninvasive PC’s where the bone tissue of the cranium deflects and deforms signals and have a lower risk of forming scar-tissue in the brain than fully-invasive BPCS. Electrocardiography(Cog) uses the same technology as non-invasive electroencephalography, but the electrodes are embedded in a thin plastic pad that is placed above the cortex, beneath the durra mater.

Cog technologies were first traded in humans in 2004 by Eric Leathered and Daniel Moran from Washington University in SST Louis. In a later trial, the researchers enabled a teenage boy to play Space Invaders using his Cog implant. This research indicates that it is difficult to produce cinematic BCC devices with more than one dimension of control using Cog Light Reactive Imaging BCC devices are still in the realm of theory. These would involve implanting laser inside the skull. The laser would be trained on a single neuron and the neuron’s reflectance measured by a separate sensor.

When neuron fires, The laser light pattern and wavelengths it reflects would change slightly. This would allow researchers to monitor single neurons but require less contact with tissue and reduce the risk of scarcities build up. 2. 4. Non-lenitive BCC : As well as invasive experiments, there have also been experiments in humans using noninvasive nonrecurring technologies as interfaces. Signals recorded in this way have been used to power muscle implants and restore partial movement in an experimental volunteer.

Although they are easy to wear, non- invasive implants produce poor signal resolution because the skull dampens signals, dispersing and blurring the electromagnetic waves created by the neurons. Although the waves can still be detected it is more difficult to determine the area of the brain that created them or the actions of individual neurons. 5 fig. 2. 4. 1 : Recordings of brainwaves produced by an electroencephalogram Electroencephalography(GEE) is the most studied potential non-invasive interface, mainly due to its fine temporal resolutions, ease of use, portability and low set-up cost.

But as well as the technology’s susceptibility to noise, another substantial barrier to using GEE as a brain-computer interface is the extensive training required before users can work the technology. For example, in experiments beginning in the mid-sass, Nielsen Barbarism of the University of Tubing in Germany used GEE recordings of slow cortical potential to give paralyses patients limited control over a computer cursor. (Barbarism had earlier trained epileptics to prevent impending fits by controlling this low voltage wave. The experiment saw ten patients trained to eve a computer cursor by controlling their brainwaves. The process was slow, requiring more than an hour for patients to write 100 characters with the cursor, while training often took many months. Another research parameter is the type of waves measured. Barbarism’s later research with Jonathan Wallow at New York State University has focused on developing technology that would allow users to choose the brain signals they found easiest to operate a BCC, including mum and beta waves. A further parameter is the method of feedback used and this is shown in studies of IPPP signals.

Patterns of IPPP waves are generated involuntarily (stimulus- feedback) when people see something they recognize and may allow PC’s to decode categories of thoughts without training patients first. By contrast, the biofeedback methods described above require learning to control brainwaves so the resulting brain activity can be detected. In 6 2000, for example, research by Jessica Baileys at the University of Rochester showed that volunteers wearing virtual reality helmets could control elements in a virtual world using their IPPP GEE readings, including turning lights on and off and bringing a mock-up car to a stop.

In 1999, researchers at Case Western Reserve University led by Hunter Paycheck, used electrode GEE skullcap to return limited hand movements to quadriplegic Jim Catch. As Catch concentrated on simple but opposite concepts like up and down, his beta-rhythm GEE output was analyses using software to identify patterns in the noise. A basic pattern was identified and used to control a switch: Above average activity was set to on, below average off.

As well as enabling Catch to control a computer cursor the signals were also used to drive the nerve controllers embedded n his hands, restoring some movement. Electronic neural-networks have been deployed which shift the learning phase from the user to the computer. Experiments by scientists at the Forerunner Society in 2004 using neural networks led to noticeable improvements within 30 minutes of training. Experiments by Eduardo Miranda aim to use GEE recordings of mental activity associated with music to allow the disabled to express themselves musically through an encephalopathy.

Magnetoencephalography (MEG) and functional magnetic resonance imaging (fem.) have both been used successfully as non-invasive BPCS. In a widely reported experiment, fem. allowed two users being scanned to play Pong in real-time by altering their hemorrhagic response or brain blood flow through biofeedback techniques. fem. measurements of hemorrhagic responses in real time have also been used to control robot arms with a seven second delay between thought and movement. 7 2. 5. Animal BCC research: fig. . 5. 1: Rats implanted with PC’s in Theodore Burger’s experiments cortexes in order to operate PC’s to carry out movement. Monkeys have navigated computer cursors on screen and commanded robotic arms to perform simple tasks imply by thinking about the task and without any motor output. Other research on cats has decoded visual signals. 2. 5. 1 . Early work Studies that developed algorithms to reconstruct movements from motor cortex neurons, which control movement, date back to the sass.

Work by groups led by Schmidt, Fete and Baker in the sass established that monkeys could quickly learn to voluntarily control the firing rate of individual neurons in the primary motor cortex after closed-loop operant conditioning, a training method using punishment and rewards. In the sass, Apostles Gorgeously at Johns Hopkins University found a thematic 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. There has been rapid development in PC’s since the mid-sass. Several groups have been able to capture complex brain motor centre signals using recordings from aural 8 ensembles (groups of neurons) and use these to control external devices, including research groups led by Richard Andersen, John Donahue, Phillip Kennedy, Miguel Nicole, and Andrew Schwartz. . 5. 2. Prominent research successes Phillip Kennedy and colleagues built the first anticlerical brain-computer interface by implanting neurotics-cone electrodes into monkeys. Fig. 2. 5. 2: Garrett Stanley recordings of cat vision using a BCC implanted in the lateral genetically nucleus (top row: original image; bottom row: recording) 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 genetically 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. Miguel Nicole has been a prominent proponent of using multiple electrodes bread over a greater area of the brain to obtain neuronal signals to drive a BCC.

Such neural ensembles are said to reduce the variability in output produced by single electrodes, which could make it difficult to operate a BCC. After conducting initial studies in rats during the sass, Nicole and his colleagues developed PC’s 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 9 this kind of work.

By 2000, the group succeeded in building a BCC that reproduced owl monkey movements while the monkey operated a Joystick or reached for food. The BCC 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 SIC. Fig. 2. 5. 3: Diagram of the BCC developed by Miguel Nicole for use on monkeys. Later experiments by Nicole 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 BCC used velocity predictions to control reaching movements and simultaneously predicted hand gripping force. Other labs that develop PC’s and algorithms that decode neuron signals include John Donahue from Brown University, Andrew Schwartz from the University of Pittsburgh ND Richard Andersen from Caltech.

These researchers were able to produce working PC’s even though they recorded signals from far fewer neurons than Nicole (15-30 neurons versus 50-200 neurons). A computer screen with or without assistance of a Joystick (closed-loop Bcc). Schwartz 10 Dungeon’s group reported training rhesus monkeys to use a BCC to track visual targets on group created a BCC for three-dimensional tracking in virtual reality and also reproduced BCC 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 powered by the animal’s own brain signals.

Andersen’s group used recordings of presentment activity from the posterior parietal cortex in their BCC, including signals created when experimental animals anticipated receiving a reward. In addition to predicting cinematic and kinetic parameters of limb movements, PC’s that predict electromyography or electrical activity of muscles are being developed. Such PC’s could be used to restore mobility in paralyses limbs by electrically stimulating muscles. 2. 6. Cell-culture PC’s Researchers have also built devices to interface with neural cells ND entire neural networks in cultures outside animals.

As well as furthering research on animal implantable devices, experiments on cultured neural tissue have focused on building problem-solving networks, constructing basic computers and manipulating robotic devices. Research into techniques for stimulating and recording from individual neurons grown on semiconductor chips is sometimes referred to as fig. 2. 6. 1 : World first: Neuroscience developed by Caltech researchers Jerome Pine and Michael Maier 11 Development of the first working neuroscience as claimed by a Caltech team led by Jerome Pine and Michael Maier in 1997.

The Caltech chip had room for 16 neurons. In 2003, a team led by Theodore Berger at the University of Southern California started work on a neuroscience designed to function as an artificial or prosthetic hippopotamus. The neuroscience was designed to function in rat brains and is intended as a prototype for the eventual development of higher-brain prosthesis. The hippopotamus was chosen because it is thought to be the most ordered and structured part of the brain and is the most studied area. Its function is to encode experiences for storage as long-term memories elsewhere in the brain.