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Adaptive common average filtering for myocontrol applications.

Hubertus Rehbaum1, Dario Farina

  • 1Department of Neurorehabilitation Engineering, University Medical Center Göttingen, Georg-August-University Göttingen, Göttingen, Germany.

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|November 13, 2014
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A new adaptive common average reference (ACAR) filter enhances electromyography (EMG) signal quality for prosthetic control. This adaptive filter improves noise reduction and selectivity, boosting machine learning performance in myocontrol applications.

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Area of Science:

  • Biomedical Engineering
  • Neurorehabilitation Engineering
  • Signal Processing

Background:

  • Electromyography (EMG) is crucial for upper-limb prosthesis control.
  • Machine learning has advanced myocontrol, but signal quality remains a challenge.
  • Existing filters like Common Average Reference (CAR) are often stationary.

Purpose of the Study:

  • To introduce a novel adaptive common average reference (ACAR) filter for EMG preprocessing.
  • To improve signal-to-noise ratio (SNR) and selectivity of EMG recordings.
  • To enhance the performance of myoelectric control systems.

Main Methods:

  • Developed an adaptive common average reference (ACAR) filter, a signal-dependent evolution of CAR.
  • Evaluated the ACAR filter's effectiveness in noise reduction and signal selectivity.
  • Assessed the impact of ACAR preprocessing on pattern recognition and regression-based myoelectric control.

Main Results:

  • The ACAR filter demonstrated significant noise reduction capabilities.
  • Improved selectivity of EMG signals was observed with the ACAR filter.
  • Application of the ACAR filter led to enhanced performance in both pattern recognition and regression methods for myoelectric control.

Conclusions:

  • The proposed ACAR filter is an effective tool for EMG signal conditioning.
  • ACAR preprocessing optimizes information extraction for improved myocontrol.
  • This novel filter represents a valuable advancement in neurorehabilitation engineering for prosthetic applications.