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Which Reference Should We Use for EEG and ERP practice?

Dezhong Yao1,2,3, Yun Qin4,5, Shiang Hu4,6

  • 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave., West Hi-Tech Zone, Chengdu, 611731, China. dyao@uestc.edu.cn.

Brain Topography
|May 1, 2019
PubMed
Summary
This summary is machine-generated.

Choosing the right reference electrode is crucial for reproducible electroencephalography (EEG) and event-related potential (ERP) studies. This review classifies common references, discusses their pros and cons, and offers recommendations for practice.

Keywords:
Average referenceBipolar referenceLaplacianLinked-mastoids referenceREST reference

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • The selection of an appropriate reference electrode for scalp electroencephalography (EEG) and event-related potential (ERP) studies remains a contentious issue.
  • The absence of a true zero or constant potential reference point on the human body leads to the use of over ten different reference methods.
  • This variability in reference electrode selection significantly hinders the reproducibility and comparability of EEG/ERP findings across different research laboratories.

Purpose of the Study:

  • To critically review and classify commonly used reference electrode techniques in EEG and ERP research.
  • To analyze the underlying assumptions, advantages, and limitations of each reference type.
  • To provide principled, application-oriented recommendations for selecting appropriate references in EEG/ERP studies.

Main Methods:

  • Classification of current popular references into unipolar (e.g., Reference Electrode Standardization Technique (REST), Average Reference (AR), Linked Mastoids/Ears (LM)) and non-unipolar (bipolar, Laplacian) categories.
  • Review of derivations and properties of different reference techniques, including the 'no memory' property of unipolar references.
  • Discussion of a unified estimator, regularized REST (rREST), highlighting its statistical advantages over AR.

Main Results:

  • Each reference type is derived based on distinct assumptions and serves specific analytical goals.
  • Unipolar references exhibit a 'no memory' property, and rREST offers a statistically robust alternative to AR.
  • A comprehensive discussion of the strengths and weaknesses of various references is presented.

Conclusions:

  • The choice of reference electrode significantly impacts EEG and ERP data interpretation.
  • Understanding the principles behind each reference is essential for researchers.
  • The study aims to guide the selection of optimal references to enhance the reliability and comparability of EEG/ERP research.