Cargando…
Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering
Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience, wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulatio...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10295557/ https://www.ncbi.nlm.nih.gov/pubmed/37370650 http://dx.doi.org/10.3390/bioengineering10060719 |
_version_ | 1785063449421676544 |
---|---|
author | Bahador, Nooshin Saha, Josh Rezaei, Mohammad R. Utpal, Saha Ghahremani, Ayda Chen, Robert Lankarany, Milad |
author_facet | Bahador, Nooshin Saha, Josh Rezaei, Mohammad R. Utpal, Saha Ghahremani, Ayda Chen, Robert Lankarany, Milad |
author_sort | Bahador, Nooshin |
collection | PubMed |
description | Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience, wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on the ways in which a burst of DBS pulses, usually delivered at a clinical frequency, i.e., 130 Hz, perturb participants’ choices. It was observed that neural activities recorded during DBS were contaminated with large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal (~1–2 Hz) that can persist for hundreds of milliseconds. While the focus of most of methods for removing DBS artifacts was on the former, the artifact removal capabilities of the slow signal have not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials, recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) normalized least mean square adaptive filtering, (2) optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) moving average. The algorithm’s capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications. |
format | Online Article Text |
id | pubmed-10295557 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102955572023-06-28 Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering Bahador, Nooshin Saha, Josh Rezaei, Mohammad R. Utpal, Saha Ghahremani, Ayda Chen, Robert Lankarany, Milad Bioengineering (Basel) Article Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience, wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on the ways in which a burst of DBS pulses, usually delivered at a clinical frequency, i.e., 130 Hz, perturb participants’ choices. It was observed that neural activities recorded during DBS were contaminated with large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal (~1–2 Hz) that can persist for hundreds of milliseconds. While the focus of most of methods for removing DBS artifacts was on the former, the artifact removal capabilities of the slow signal have not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials, recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) normalized least mean square adaptive filtering, (2) optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) moving average. The algorithm’s capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications. MDPI 2023-06-14 /pmc/articles/PMC10295557/ /pubmed/37370650 http://dx.doi.org/10.3390/bioengineering10060719 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bahador, Nooshin Saha, Josh Rezaei, Mohammad R. Utpal, Saha Ghahremani, Ayda Chen, Robert Lankarany, Milad Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering |
title | Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering |
title_full | Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering |
title_fullStr | Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering |
title_full_unstemmed | Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering |
title_short | Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering |
title_sort | robust removal of slow artifactual dynamics induced by deep brain stimulation in local field potential recordings using svd-based adaptive filtering |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10295557/ https://www.ncbi.nlm.nih.gov/pubmed/37370650 http://dx.doi.org/10.3390/bioengineering10060719 |
work_keys_str_mv | AT bahadornooshin robustremovalofslowartifactualdynamicsinducedbydeepbrainstimulationinlocalfieldpotentialrecordingsusingsvdbasedadaptivefiltering AT sahajosh robustremovalofslowartifactualdynamicsinducedbydeepbrainstimulationinlocalfieldpotentialrecordingsusingsvdbasedadaptivefiltering AT rezaeimohammadr robustremovalofslowartifactualdynamicsinducedbydeepbrainstimulationinlocalfieldpotentialrecordingsusingsvdbasedadaptivefiltering AT utpalsaha robustremovalofslowartifactualdynamicsinducedbydeepbrainstimulationinlocalfieldpotentialrecordingsusingsvdbasedadaptivefiltering AT ghahremaniayda robustremovalofslowartifactualdynamicsinducedbydeepbrainstimulationinlocalfieldpotentialrecordingsusingsvdbasedadaptivefiltering AT chenrobert robustremovalofslowartifactualdynamicsinducedbydeepbrainstimulationinlocalfieldpotentialrecordingsusingsvdbasedadaptivefiltering AT lankaranymilad robustremovalofslowartifactualdynamicsinducedbydeepbrainstimulationinlocalfieldpotentialrecordingsusingsvdbasedadaptivefiltering |