Brain Machine Interface for Avatar Control and Estimation for Educational Purposes Based on Neural AI Plugs: Theoretical and Methodological Aspects

Brain Machine Interface for Avatar Control and Estimation for Educational Purposes Based on Neural AI Plugs: Theoretical and Methodological Aspects

DOI: 10.4018/978-1-7998-1581-5.ch016
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Abstract

In the future education process where Avatar will be used, it is critically important to have a layer which is responsible for transferring the knowledge from a Student's Avatar to Student. In this research, authors show the method and high-level architecture of how it could be done. And although the suggested approach works, the current level of technology does not allow creating a mobile set which could implement this approach. The general high-level schema of the Avatar methodology used in the education process is enclosed in the following. There are 4 layers which interact with each other. On the first layer there is a professor, who is an expert in some domain. The professor transfers their knowledge to the second layer which is a computer program, performing the role of the Professor's Avatar. The knowledge gets transferred to a Student's Avatar, which is the 3rd layer, and eventually the Student's Avatar transfers the newly received knowledge to the Student, the 4th layer in this schema. So, the most difficult part here is to transfer the knowledge from machine to human.
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Introduction

“I know Kung Fu”, Neo.

“I know Kung Fu” is a famous phrase known to everyone. Many people dreamt of technology which could allow them to receive the new knowledge without actual learning. This research describes the high-level approach of this.

The general high-level schema of the Avatar methodology used in the education process is enclosed in the following: There are 4 layers which interact with each other. On the first layer we have a professor, who is an expert in some domain. He transfers his knowledge to the second layer which is a computer program, performing the role of the Professor’s Avatar. After that the knowledge gets transferred to a Student’s Avatar, which is the 3rd layer. And eventually the Student’s Avatar transfers the newly received knowledge to the Student, who is the 4th layer in this schema. So the most difficult part here is to transfer the knowledge from machine to human (Mkrttchian, et. all, 2016, 2017, 2018, 2019a, 2019b).

Who Will Need the System and How it could be Applied

The system can be used in lots of spheres, such as but not limited to:

  • Lazy students

  • Necessity to possess some knowledge during extremely short interval of time

  • People with Parkinson's/dementia/sclerosis disease

  • Etc.

The Principal of Memory Work

In order to better understand how the memory works it’s necessary to consider several mechanisms, such as Summation, Long Lasting Potentiation, Papez circle.

Summation, Long Lasting Potentiation, Papez Circle

One of the neural processes which describes the mechanism of memory’s work is called summation. It was described in the details in the research of Rinat Galiautdinov (Rinat Galiautdinov, “Brain machine interface - the accurate interpretation of neurotransmitters' signals targeting the muscles”). The major key of the process is enclosed in the fact that weak but frequent enough signals can eventually make the signal “to jump” to another neuron in the neural circuit and the mechanism of this process is based on collection of calcium ions. The memory based on this mechanism does not last long and the information received through the usage of this mechanism disappears as soon as calcium ions “get disappeared”.

Another very important neural process which is related to the memory mechanism is called Long Lasting Potentiation. The mechanism gets started in the case if there was a strong enough signal (~ -30mV) which makes the receptors “to spits out” the ion of Mg2+.

Another example of how the memory works is Papez circle, which represents some kind of connected route of neural circuit in the brain and runs the signal in the circle.

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