“No technology is superior if it tends to overrule human faculty. In fact, it should be other way around”
Imagine that you are somewhere else and you have to control a machine which is in a remote area, where human can’t withstand for a long time. In such a condition we can move to this BRAIN -MACHINE INTERFACE. It is similar to robotics but it is not exactly a robot. In the robot the interface has a sensor with controller but here the interface with human and machine. In the present wheel chair movements are done according to the patient by controlling the joystick with only up, reverse, left and right movements are possible. But if the patient is a paralyzed person, then it is a critical for the patient to take movements. Such a condition can be recovered by this approach.
The main objective of this paper is to interface the human and machine, by doing this several objects can be controlled. This paper enumerates how Human and Machine can be interfaced and researches undergone on recovery of paralyzed person in their mind.
Scientists have used the brain signals from a monkey to drive a robotic arm. As the animal stuck out its hand to pick up some food off a tray, an artificial neural system linked into the animal's head mimicked activity in the mechanical limb.
It was an amazing sight to see the robot in my lab move, knowing that it was being driven by signals from a monkey brain. It was as if the monkey had a 600-mile- (950-km-) long virtual arm. The rhesus monkeys consciously controls the movement of a robot arm in real time, using only signals from their brains and visual feedback on a video screen. It is said that the animals appeared to operate the robot arm as if it were their own limb. The technologies achievement represents an important step toward technology that could enable paralyzed people to control "neuroprosthetic" limbs, and even free-roaming "neurorobots" using brain signals. Importantly, the technology that developed for analyzing brain signals from behaving animals could also greatly improve rehabilitation of people with brain and spinal cord damage from stroke, disease or trauma.
By understanding the biological factors that control the brain's adaptability.
The clinicians could develop improved drugs and rehabilitation methods for people with such damage. The latest work is the first to demonstrate that monkeys can learn to use only visual feedback and brain signals, without resort to any muscle movement, to control a mechanical robot arm including both reaching and grasping movements.
The frontal and parietal areas of the brain are chosen because they are known to be involved in producing multiple output commands to control complex muscle movement.
The faint signals from the electrode arrays were detected and analyzed by the computer system and developed to recognize patterns of signals that represented particular movements by an animal's arm.
Imagine that you are somewhere else and you have to control a machine which is in a remote area, where human can’t withstand for a long time. In such a condition we can move to this BRAIN -MACHINE INTERFACE. It is similar to robotics but it is not exactly a robot. In the robot the interface has a sensor with controller but here the interface with human and machine. In the present wheel chair movements are done according to the patient by controlling the joystick with only up, reverse, left and right movements are possible. But if the patient is a paralyzed person, then it is a critical for the patient to take movements. Such a condition can be recovered by this approach.
The main objective of this paper is to interface the human and machine, by doing this several objects can be controlled. This paper enumerates how Human and Machine can be interfaced and researches undergone on recovery of paralyzed person in their mind.
Introduction:
The core of this paper is that to operate machines from a remote area . In the given BMI DEVELOPMENT SYSTEMS the brain is connected to client interface node through a neural interface nodes . The client interface node connected to a BMI SERVER which controls remote ROBOTS through a host control.Brain Study:
In the previous research, it has been shown that a rat wired into an artificial neural system can make a robotic water feeder move just by willing it. But the latest work sets new benchmarks because it shows how to process more neural information at a faster speed to produce more sophisticated robotic movements. That the system can be made to work using a primate is also an important proof of principle.Scientists have used the brain signals from a monkey to drive a robotic arm. As the animal stuck out its hand to pick up some food off a tray, an artificial neural system linked into the animal's head mimicked activity in the mechanical limb.
It was an amazing sight to see the robot in my lab move, knowing that it was being driven by signals from a monkey brain. It was as if the monkey had a 600-mile- (950-km-) long virtual arm. The rhesus monkeys consciously controls the movement of a robot arm in real time, using only signals from their brains and visual feedback on a video screen. It is said that the animals appeared to operate the robot arm as if it were their own limb. The technologies achievement represents an important step toward technology that could enable paralyzed people to control "neuroprosthetic" limbs, and even free-roaming "neurorobots" using brain signals. Importantly, the technology that developed for analyzing brain signals from behaving animals could also greatly improve rehabilitation of people with brain and spinal cord damage from stroke, disease or trauma.
By understanding the biological factors that control the brain's adaptability.
The clinicians could develop improved drugs and rehabilitation methods for people with such damage. The latest work is the first to demonstrate that monkeys can learn to use only visual feedback and brain signals, without resort to any muscle movement, to control a mechanical robot arm including both reaching and grasping movements.
Signal Analysis using Electrodes:
A brain-signal recording and analysis system that enabled to decipher brain signals from monkeys in order to control the movement of a robot arm .In the xperiments, an array of microelectrodes each smaller than the diameter of a human hair into the frontal and parietal lobes of the brains of wo female rhesus macaque monkeys. They implanted 96 electrodes in one animal and 320 in the other. The researchers reported their technology of implanting arrays of hundreds of electrodes and recording from them over long periods.The frontal and parietal areas of the brain are chosen because they are known to be involved in producing multiple output commands to control complex muscle movement.
The faint signals from the electrode arrays were detected and analyzed by the computer system and developed to recognize patterns of signals that represented particular movements by an animal's arm.
Experiments:
The experiments conducted for Brain-Machine Interface are:Monkey Experiment:
The goal of the project is to control a hexapod robot (RHEX) using neural signals from monkeys at remote location. To explore the optimal mapping of cortical signals to Rhex’s movement parameters, a model of Rhex’s movements has been generated and human arm control is used to approximate cortical control. In preliminary investigations, the objective was to explore different possible mappings or control strategies for Rhex. Both kinematic (position, velocity) and dynamic (force, torque) mappings from hand space were explored and optimal control strategies were determined. These mappings will be tested in the next phases of the experiment to ascertain the maximal control capabilities of prefrontal and parietal cortices.
In the initial, output signals from the monkeys' brains were analyzed and recorded as the animals were taught to use a joystick to both position a cursor over a target on a video screen and to grasp the joystick with a specified force. After the animal’s initial training, however the cursor was made a simple display – now incorporating into its movement the dynamics, such as inertia and momentum, of a robot arm functioning in another room. While the animal’s performance initially declined when the robot arm was included in the feedback loop, they quickly learned to allow for these dynamics and became proficient in manipulating the robot-reflecting cursor The joystick was then removed, after which the monkeys continued to move their arms in mid-air to manipulate and "grab" the cursor, thus controlling the robot arm.
After a series of psychometric tests on human volunteers, the strategy of controlling a model of Rhex depicted above using the human hand was determined to be the easiest to use and fastest to learn. The flexion/extension of the wrist is mapped to angular velocity and the linear translation of the hand is mapped to linear (fore/aft) velocity. The monkeys are being trained to use this technique to control a virtual model of Rhex
The most amazing result, though, was that after only a few days of playing with the robot in this way, the monkey suddenly realized that it didn't need to move her arm at all. "The arm muscles went completely quiet, it kept the arm at side and controlled the robot arm using only its brain and visual feedback.
Our analyses of the brain signals showed that the animal learned to assimilate the robot arm into her brain as if it was her own arm." Importantly the experiments included both reaching and grasping movements, but derived from the same sets of electrodes.
The neurons from which we were recording could encode different kinds of information. It was surprised to see that the animal can learn to time the activity of the neurons to basically control different types of parameters sequentially. For example, after using a group of neurons to move the robot to a certain point, these same cells would then produce the force output that the animals need to hold an object.
Analysis of the signals from the animal’s brain as they learned revealed that the brain circuitry was actively reorganizing itself to adapt.
Analysis of Outputs:
It was extraordinary to see that when we switched the animal from joystick control to brain control, the physiological properties of the brain cells changed immediately. And when we switched the animal back to joystick control the very next day, the properties changed again.
Such findings tell us that the brain is so amazingly adaptable that it can incorporate an external device into its own 'neuronal space' as a natural extension of the body , actually, we see this every day, when we use any tool, from a pencil to a car. As a part of that we incorporate the properties of that tool into our brain, which makes us proficient in using it, such findings of brain plasticity in mature animals and humans are in sharp contrast to traditional views that only in childhood is the brain plastic enough to allow for such adaptation.
The finding that their brain-machine interface system can work in animals will have direct application to clinical development of neuroprosthetic devices for paralyzed people.
There is certainly a great deal of science and engineering to be done to develop this technology and to create systems that can be used safely in humans. However, the results so far lead us to believe that these brain-machine interfaces hold enormous promise for restoring function to paralyzed people.
The researchers are already conducting preliminary studies of human subjects, in which they are performing analysis of brain signals to determine whether those signals correlate with those seen in the animal models. They are also exploring techniques to increase the longevity of the electrodes beyond the two years they have currently achieved in animal studies. To miniaturize the components, to create wireless interfaces and to develop different grippers, wrists and other mechanical components of a neuroprosthetic device.
And in their animal studies, proceeding to add an additional source of feedback to the system in the form of a small vibrating device placed on the animal's side that will tell the animal about another property of the robot. Beyond the promise of neuroprosthetic devices, the technology for recording and analyzing signals from large electrode arrays in the brain will offer an unprecedented insight into brain function and plasticity.
We have learned in our studies that this approach will offer important insights into how the large-scale circuitry of the brain works .Since we have total control of the system, for example, we can change the properties of the robot arm and watch in real time how the brain adapts.
Brain Machine Interface in Human beings:
The approach of this paper is to control the operations of a robot by means of an human brain without any links .
The brain signals are taken by electrodes from the frontal and parietal lobes .The signals are conveyed with means of electrodes and processed by the unit .The unit has a BMI development system . The brain is connected to (i.e. the microelectrodes are connected to the frontal and parietal lobes) client interface through neural interface nodes which in turn is linked with BMI server which controls the host device .
In the present wheel chair, movements are done according to the patient by controlling the joystick with only up, reverse, left and right movements which are only possible. But if the patient is a paralyzed person, then it is a critical for the patient to take movements because he is unable to control the wheel-chair. So this technology is a marvelous gift to help them.
Conclusion:
Thus this technology is a boon to this world. By this adaptation many Bio-medical difficulties can be overtaken and many of our dreams will come true .
References:
Bio-medical Engineering by Dr. Dan Koditschek. Neural Engineering by Karen Coulter and Rahul Bagdia Neural Networks by Patrick Davalo and Erick Naim.
Technical Paper on Brain Machine Interface
4/
5
Oleh
Siva
