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Cited by 79Neuroscience and Neural EngineeringNeural dynamics and brain functionEEG and Brain-Computer Interfaces

Generalized framework for stimulus artifact removal.

Erez, Yaara, Tischler, Hadass, Moran, Anan, Bar-Gad, Izhar·2010·Journal of Neuroscience Methods
DOI 10.1016/j.jneumeth.2010.06.005
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Abstract

Stimulation is extensively used in neuroscience research in diverse fields ranging from cognitive to clinical. Studying the effect of electrical and magnetic stimulation on neuronal activity is complicated by large stimulation-derived artifacts on the recording electrodes, which mask the spiking activity. Multiple studies have suggested a variety of solutions for the removal of artifacts and were typically directed at specific stimulation setups. In this study we introduce a generalized framework for stimulus artifacts removal, the Stimulus Artifact Removal Graphical Environment (SARGE). The framework provides an encapsulated environment for a multi-stage removal process, starting from the stimulus pulse detection, through estimation of the artifacts and their removal, and finally to signal reconstruction and the assessment of removal quality. The framework provides the user with subjective graphical and objective quantitative tools for assessing the resulting signal, and the ability to adjust the process to optimize the results. This extendable publicly available framework supports different types of stimulation, stimulation patterns and shapes, and a variety of artifact estimation methods. We exemplify the removal of artifacts generated by electrical micro- and macro-stimulation and magnetic stimulation and different stimulation protocols. The use of different estimation methods, such as averaging and function fitting is demonstrated, and the differences between them are discussed. Finally, the quality of removal is assessed and validated using quantitative measures and combined experimental-simulation studies. The framework marks a shift from "algorithm" and "data" centric approach to a "workflow" centric approach, thus introducing an innovative concept to the artifact removal process.

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  • The blanking technique and digital signal processing have been used to cancel the artifact.

    “The blanking technique and digital signal processing (Erez et al., 2010) have been used to cancel the artifact (Olsson et al., 2005; Venkatraman et al., 2009; Kent and Grill, 2011; Myers et al., 2011; Zoladz et al., 2012; Wei-Ming et al., 2013; Yi et al., 2013; Bozorgzadeh et al., 2014; Elyahoodayan et al., 2019).”

    From Advances in Neural Recording and Stimulation Integrated Circuits (2021) · cites this paper as Erez et al., 2010

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Authors
Erez, Y., Tischler, H., Moran, A., & Bar-Gad, I.
Affiliations
Bar-Ilan University (IL)
ORCID
Venue
Journal of Neuroscience Methods
Publisher
Elsevier BV
Published
2010-08
Volume
191
Issue
1
Pages
45-59
ISSN
0165-0270 (ISSN-L)
DOI
10.1016/j.jneumeth.2010.06.005
OpenAlex
W2036906125
Cited by
79
References
21
Citations by year
2026: 3 · 2025: 4 · 2024: 9 · 2023: 7 · 2022: 2 · 2021: 8 · 2020: 7 · 2019: 2 · 2018: 5 · 2017: 3 · 2016: 5 · 2015: 3 · 2014: 3 · 2013: 2 · 2012: 9
FWCI
2.32
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Journal of Neuroscience Methods (publishedVersion)
  • PubMed (publishedVersion)
Funders
Israel Science Foundation
Topics
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Keywords
electrical stimulation, magnetic stimulation, signal reconstruction, signal quality assessment, averaging, function fitting, neural recording
MeSH terms
Animals, Computer Simulation, Electric Stimulation, Electrophysiology, Macaca fascicularis, Male, Models, Neurological, Signal Processing, Computer-Assisted, Software, Artifacts, Transcranial Magnetic Stimulation

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