techinfi
Brain-machine interfaces:
It sounds like a dream of the future: being able to control robots and computers with your thoughts. However, the vision of brain-machine interfaces (BMI) is no longer just the stuff of science fiction novels.
More and more scientific publications describe such devices – with different degrees of complexity and possible uses.
A paraplegic patient has had two wireless devices implanted in his brain for two years to record his brain waves. After much training, strapped into an exoskeleton, he can move all four limbs – his arms in four directions and his legs for a few wobbly steps.
This has hardly any advantages for the patient, but it is a step forward for BMI research. So far, BMI has only been used sporadically and for research purposes. It will take some time before they are suitable for everyday use.
What are brain-machine interfaces?
Brain-machine interfaces(BMI) orbrain-computer interfaces(BCI) enable communication between the brain and a connected device or computer.
Connection is made via the peripheral or central nervous system (brain and spinal cord nerves) based on the assumption that just imagining an intended behavior, such as raising an arm, triggers changes in brain activity.
BMI measure these changes and converts them into control signals, such as digital commands to a robotic arm.
Learning process:
The brain and computer have to learn to work together.
The computer program has to adjust to the patterns of the brain to be able to use them for control. This is mostly done with the help of artificial intelligence and machine learning algorithms.
It must learn which signals from which neurons are important for a particular movement. With advances in the AI industry, BMI is also getting better.
Example of neurofeedback training:
Brain activity should be controlled mentally in a targeted manner. Patients must visualize and focus on a movement. In this way, they generate more activity in the associated motor areas of the brain.
At the same time, patients hear their rising and falling brain frequency as a rising and falling tone. The computer will reward them with positive feedback if they control their brain frequency correctly.
Two BMI types:
Unidirectional BMI
The system consists of three components: devices to record neural signals, components to analyze the signals, and devices to provide the commands to operate a machine.
Example of a unidirectional BMI:
Moving the hand leads to activating the corresponding region of the motor cortex. A BMI records these changes in brain activity, uses algorithms to convert the signals into computer commands, and the artificial hand moves.
Bi-directional BMI
Adds two more components that send feedback from the machine to the brain. The feedback is given physiologically (stimulation of certain brain regions for a corresponding sensation) or via active feedback to the patient.
Example of a bidirectional BMI:
Moving the hand leads to activating the corresponding region of the motor cortex. A BMI records these changes in brain activity, turns the signals into computer commands, and the artificial hand moves. The computer reports its movements back to the human brain in the form of signals, in that the BMI generates a perceptual stimulus at a corresponding point in the brain through stimulation, for example, a feeling for how tightly the hand has closed.
Feedback:
Feedback is necessary to be able to interact with the environment.
Research is aimed at a BMI that enables the patient to control the initial behavior of the prosthesis directly and, at the same time, receive relevant sensory information from the device (bidirectional)
This requires restoring the natural control-feedback loop in which the nervous system relays information to the brain via electrical signals, or action potentials, throughout the body.
These signals are the basis for controlled muscle contractions and input/feedback from sensory organs (e.g., texture, temperature, position)
There are few bidirectional BMIs yet; unidirectional ones are predominant. With these, patients only get feedback about their sensory organs, not from the BMI. For example, they see the robot’s hand moving but do not feel it.
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