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A new full closed-loop brain-machine interface approach based on neural activity: A study based on modeling and

Masoud Amiri1, Soheila Nazari2, Amir Homayoun Jafari1

  • 1Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Science (TUMS), Tehran, Iran.

Heliyon
|February 28, 2023
PubMed
Summary

This study introduces a novel bidirectional brain-machine interface (BMI) algorithm. The new BMI algorithm successfully controlled prosthetic limb movement in simulations and animal experiments, outperforming existing methods.

Keywords:
Decoding algorithm (motor interface)Encoding algorithm (sensory interface)Full closed loop brain machine interfaceLFPNeural activity

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Robotics

Background:

  • Bidirectional brain-machine interfaces (BMIs) enable control of external devices by decoding neural signals and encoding device position for neural stimulation.
  • Current BMI research typically involves four key components: neural recording, decoding algorithms, external devices (e.g., robotic limbs), and encoding interfaces.

Purpose of the Study:

  • To develop a novel, closed-loop bidirectional brain-machine interface (BMI) algorithm.
  • To model the topographic mapping between the sensory cortex (S1) and motor cortex (M1) using neural network models.

Main Methods:

  • Proposed a neural network model with topographic mapping between S1 (4-box, 2000 neurons) and M1 (4-box, 2000 neurons) models.
  • Developed a new BMI algorithm based on neural activity to close the loop between the brain and an external mechanical device.
  • Utilized sensory and motor interfaces for encoding artificial limb position into neural stimulation and decoding neural recordings into device movement, respectively.

Main Results:

  • Demonstrated real-time (online) and offline information exchange between the S1-M1 network model and an external device.
  • The proposed BMI algorithm successfully controlled a mechanical arm's movement towards a target in both simulated and experimental data from anesthetized rats.
  • Achieved acceptable Weighted Total Prediction Error (WTPE) and efficient iteration counts for limb movement control.

Conclusions:

  • The developed bidirectional BMI algorithm shows superior performance compared to the "spike train" algorithm in both simulations and offline experimental data.
  • Quantitative and qualitative results confirm the effectiveness and proper performance of the proposed BMI algorithm for prosthetic limb control.