期刊
CLINICAL NEUROPHYSIOLOGY
卷 129, 期 10, 页码 2170-2185出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.clinph.2018.07.023
关键词
Deep brain stimulation; EEG; MEG; Artifacts; Antialiasing; Oversampling; Low-pass filtering; Matched filters; Hampel; Template subtraction; ICA
资金
- grant AO-HCL [D50786-UF81431]
- grant ANR [MNPS-039-01, ANR-16-CE37-0007-03]
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. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
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