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Title
Abstract
Key Takeaways
Introduction
Materials and Methods
Results and Discussion
Conclusion
References

NEURONAL PATHWAY AND SIGNAL MODULATION FOR MOTOR COMMUNICATION

Profile image of Alexander N PisarchikAlexander N Pisarchik

2018, CYBERNETICS AND PHYSICS, VOL. 8, NO. 3, 2019, 106–113

https://doi.org/10.35470/2226-4116-2019-8-3-106-113
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Abstract

The knowledge of the mechanisms of motor imagery (MI) is very important for the development of brain-computer interfaces. Depending on neurophysiologi-cal cortical activity, MI can be divided into two categories: visual imagery (VI) and kinesthetic imagery (KI). Our magnetoencephalography (MEG) experiments with ten untrained subjects provided evidences that in-hibitory control plays a dominant role in KI. We found that communication between inferior parietal cortex and frontal cortex is realised in the mu-frequency range. We also pinpointed three gamma frequencies to be used for motor command communication. The use of artificial intelligence allowed us to classify MI of left and right hands with maximal classification accuracy using the brain activity encoded in the identified gamma frequencies which were then proposed to be used for communication of specifics. Mu-activity was identified as the carrier of gamma-activity between these areas by means of phase-amplitude coupling similar to the modern day radio wave transmission.

Key takeaways
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AI

  1. Inhibitory control is crucial for kinesthetic imagery (KI) in motor command communication.
  2. Magnetoencephalography (MEG) reveals communication between inferior parietal cortex and frontal cortex in the mu-frequency range.
  3. Gamma frequencies (32, 45, 48 Hz) encode specifics of motor commands, while mu-activity serves as a carrier wave.
  4. Artificial neural networks achieved 85% classification accuracy for left/right hand imagery based on MEG signals.
  5. The study enhances understanding of motor imagery mechanisms, aiding brain-computer interface development.

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