Medtronic Percept™ recorded LFP pre-processing to remove noise and cardiac signals from neural recordings

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/ f component from the power spectrum by fitting the fitting oscillations and one over F logarithmic function. While filter selection must be tailored to the specific device and participant environment to avoid over-filtering, these interference and noise mitigation strategies are crucial for ensuring the integrity of LFP recordings.