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An adaptive decoder design based on the receding horizon optimization in BMI system.

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  • 11College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, 710054 China.

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Summary

This study introduces an adaptive decoder for motor brain-machine interfaces (BMI) to improve arm movement recovery. By updating decoder weights in real-time, it enhances electroencephalogram signal processing for better joint motor function restoration.

Keywords:
Adaptive decoderBrain-machine interfaceReceding horizon optimizationWiener filter

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Signal Processing

Background:

  • Motor brain-machine interface (BMI) systems rely on electroencephalogram (EEG) signals for decoding movement.
  • Static decoders struggle with dynamic EEG changes during arm movement, leading to inaccurate signal translation and impaired motor function recovery.

Purpose of the Study:

  • To develop an adaptive decoder that dynamically updates its weights to improve EEG signal processing in motor BMI.
  • To enhance the accuracy of joint motor function recovery in BMI systems.

Main Methods:

  • A receding horizon optimization strategy was employed to implement an adaptive Wiener-filter-based decoder.
  • Decoder weights were updated online by minimizing a cost function based on squared position errors within a defined time horizon.

Main Results:

  • Simulations demonstrated superior performance of the adaptive decoder compared to fixed-weight decoders.
  • The adaptive approach led to better recovery of joint motor function and improved neuron activity representation.

Conclusions:

  • An adaptive Wiener-filter-based decoder significantly improves motor BMI performance.
  • Online weight updates are crucial for accurately decoding dynamic EEG signals and restoring motor function.