ReviewJournal of neural engineering2025
Medtronic Percept™ recorded LFP pre-processing to remove noise and cardiac signals from neural recordings.
Review in Journal of neural engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Gamma suppression correlates with thalamic stimulation therapeutic response in intractable epilepsy.Epilepsia · 2026Article
- Deep Brain Stimulation after Subthalamic Focused Ultrasound for Parkinson's Disease.Movement disorders clinical practice · 2026Article
- A novel methodological approach to understanding the cortical and subcortical effects of aerobic exercise in Parkinson's disease.Frontiers in human neuroscience · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Chronic brain sensing devices, such as the Medtronic Percept™ or Neuropace RNS system, record local field potentials (LFPs) that may be vulnerable to interference and noise due to hardware limitations, environmental factors, movement, stimulation, cardiac signals, and analytical procedures. Although onboard hardware filters can attenuate some unwanted signals, additional processing is often required. Here we demonstrate that cardiac artifacts significantly alter the power spectral density (PSD) of neural activity within the theta (4-8 Hz), alpha (8-12 Hz), and beta (12-30 Hz) bands. We introduce a time-domain template subtraction method specifically designed to remove QRS complex cardiac artifacts. Separately, we describe techniques for transforming time domain data to the frequency domain and mitigating transient artifacts by estimating background neural activity-either through window rejection based on PSD characteristics or via principal component analysis. Finally, we present an approach to isolate oscillatory neural activity by subtracting the aperiodic 1/
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