Literature DB >> 30144660

Removing deep brain stimulation artifacts from the electroencephalogram: Issues, recommendations and an open-source toolbox.

Guillaume Lio1, Stéphane Thobois2, Bénédicte Ballanger3, Brian Lau4, Philippe Boulinguez5.   

Abstract

A major question for deep brain stimulation (DBS) research is understanding how DBS of one target area modulates activity in different parts of the brain. EEG gives privileged access to brain dynamics, but its use with implanted patients is limited since DBS adds significant high-amplitude electrical artifacts that can completely obscure neural activity measured using EEG. Here, we systematically review and discuss the methods available for removing DBS artifacts. These include simple techniques such as oversampling, antialiasing analog filtering and digital low-pass filtering, which are necessary but typically not sufficient to fully remove DBS artifacts when each is used in isolation. We also cover more advanced methods, including techniques tracking outliers in the frequency-domain, which can be effective, but are rarely used. The reason for that is twofold: First, it requires advanced skills in signal processing since no user friendly tool for removing DBS artifacts is currently available. Second, it involves fine-tuning to avoid over-aggressive filtering. We highlight an open-source toolbox incorporating most artifact removal methods, allowing users to combine different strategies.
Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Antialiasing; Artifacts; Deep brain stimulation; EEG; Hampel; ICA; Low-pass filtering; MEG; Matched filters; Oversampling; Template subtraction

Mesh:

Year:  2018        PMID: 30144660     DOI: 10.1016/j.clinph.2018.07.023

Source DB:  PubMed          Journal:  Clin Neurophysiol        ISSN: 1388-2457            Impact factor:   3.708


  11 in total

1.  Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning.

Authors:  D J Caldwell; J A Cronin; R P N Rao; K L Collins; K E Weaver; A L Ko; J G Ojemann; J N Kutz; B W Brunton
Journal:  J Neural Eng       Date:  2020-04-09       Impact factor: 5.379

2.  Frequency following responses and rate change complexes in cochlear implant users.

Authors:  Robin Gransier; Franҫois Guérit; Robert P Carlyon; Jan Wouters
Journal:  Hear Res       Date:  2021-02-11       Impact factor: 3.208

3.  Dataset of concurrent EEG, ECG, and behavior with multiple doses of transcranial electrical stimulation.

Authors:  Nigel Gebodh; Zeinab Esmaeilpour; Abhishek Datta; Marom Bikson
Journal:  Sci Data       Date:  2021-10-27       Impact factor: 6.444

4.  Time-frequency signatures evoked by single-pulse deep brain stimulation to the subcallosal cingulate.

Authors:  Ezra E Smith; Ki Sueng Choi; Ashan Veerakumar; Mosadoluwa Obatusin; Bryan Howell; Andrew H Smith; Vineet Tiruvadi; Andrea L Crowell; Patricio Riva-Posse; Sankaraleengam Alagapan; Christopher J Rozell; Helen S Mayberg; Allison C Waters
Journal:  Front Hum Neurosci       Date:  2022-08-18       Impact factor: 3.473

5.  Quantitative EEG and Verbal Fluency in DBS Patients: Comparison of Stimulator-On and -Off Conditions.

Authors:  Florian Hatz; Antonia Meyer; Anne Roesch; Ethan Taub; Ute Gschwandtner; Peter Fuhr
Journal:  Front Neurol       Date:  2019-01-09       Impact factor: 4.003

6.  Electrophysiological assessment of temporal envelope processing in cochlear implant users.

Authors:  Robin Gransier; Robert P Carlyon; Jan Wouters
Journal:  Sci Rep       Date:  2020-09-21       Impact factor: 4.379

7.  The comparative performance of DBS artefact rejection methods for MEG recordings.

Authors:  Ahmet Levent Kandemir; Vladimir Litvak; Esther Florin
Journal:  Neuroimage       Date:  2020-06-12       Impact factor: 6.556

8.  Artefact-free recording of local field potentials with simultaneous stimulation for closed-loop Deep-Brain Stimulation.

Authors:  Jean Debarros; Lea Gaignon; Shenghong He; Alek Pogosyan; Moaad Benjaber; Timothy Denison; Peter Brown; Huiling Tan
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2020-07

9.  Identification of biomarkers that predict response to subthalamic nucleus deep brain stimulation in resistant obsessive-compulsive disorder: protocol for an open-label follow-up study.

Authors:  Shyam Sundar Arumugham; Dwarakanath Srinivas; Janardhanan C Narayanaswamy; T S Jaisoorya; Himani Kashyap; Philippe Domenech; Stéphane Palfi; Luc Mallet; Ganesan Venkatasubramanian; Yc Janardhan Reddy
Journal:  BMJ Open       Date:  2021-06-22       Impact factor: 2.692

10.  Proactive inhibition is not modified by deep brain stimulation for Parkinson's disease: An electrical neuroimaging study.

Authors:  Michael De Pretto; Michael Mouthon; Ines Debove; Claudio Pollo; Michael Schüpbach; Lucas Spierer; Ettore A Accolla
Journal:  Hum Brain Mapp       Date:  2021-06-10       Impact factor: 5.038

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