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Signal processing methods for reducing artifacts in microelectrode brain recordings caused by functional electrical

D Young1,2, F Willett1,2,3, W D Memberg1,2

  • 1Case Western Reserve University, Cleveland, OH, United States of America.

Journal of Neural Engineering
|December 5, 2017
PubMed
Summary
This summary is machine-generated.

Functional electrical stimulation (FES) can restore movement, but causes artifacts in brain-computer interfaces. Linear regression reference (LRR) effectively reduces these artifacts, restoring decoding performance for paralyzed limb control.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Functional electrical stimulation (FES) aims to restore movement in paralyzed individuals.
  • Intracortical brain-computer interfaces (iBCIs) offer intuitive control for FES neuroprostheses.
  • Electrical stimulation in FES generates artifacts that can impair iBCI performance.

Purpose of the Study:

  • To investigate methods for reducing cortically recorded artifacts caused by peripheral electrical stimulation.
  • To evaluate the effectiveness of artifact reduction techniques in FES+iBCI systems.

Main Methods:

  • Characterized electrical artifacts from intramuscular and surface FES in a participant with intracortical microelectrode arrays.
  • Compared artifact reduction performance of blanking, common average reference (CAR), and linear regression reference (LRR).
  • LRR creates channel-specific reference signals using weighted sums of other channels.

Main Results:

  • Surface FES artifacts were significantly larger (175x) than intramuscular FES artifacts (4x) compared to neural recordings.
  • LRR reduced artifact magnitudes to below 10 µV, largely preserving neural feature values for decoding.
  • LRR outperformed CAR and blanking, nearly fully restoring iBCI decoding performance (over 90% recovery).

Conclusions:

  • Linear regression reference (LRR) effectively mitigates FES-induced electrical artifacts in iBCI systems.
  • LRR significantly improves decoding performance, enabling more reliable control of FES neuroprostheses.
  • The LRR method shows promise for noise reduction in other applications beyond FES+iBCI.