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      Which Reference Should We Use for EEG and ERP practice?

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          Abstract

          Which reference is appropriate for the scalp ERP and EEG studies? This unsettled problem still inspires unceasing debate. The ideal reference should be the one with zero or constant potential but unfortunately it is well known that no point on the body fulfills this condition. Consequently, more than ten references are used in the present EEG-ERP studies. This diversity seriously undermines the reproducibility and comparability of results across laboratories. A comprehensive review accompanied by a brief communication with rigorous derivations and notable properties (Hu et al. Brain Topogr, 2019. 10.1007/s10548-019-00706-y) is thus necessary to provide application-oriented principled recommendations. In this paper current popular references are classified into two categories: (1) unipolar references that construct a neutral reference, including both online unipolar references and offline re-references. Examples of unipolar references are the reference electrode standardization technique (REST), average reference (AR), and linked-mastoids/ears reference (LM); (2) non-unipolar references that include the bipolar reference and the Laplacian reference. We show that each reference is derived with a different assumption and serves different aims. We also note from (Hu et al. 2019) that there is a general form for the reference problem, the ‘no memory’ property of the unipolar references, and a unified estimator for the potentials at infinity termed as the regularized REST (rREST) which has more advantageous statistical evidence than AR. A thorough discussion of the advantages and limitations of references is provided with recommendations in the hope to clarify the role of each reference in the ERP and EEG practice.

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          Über das Elektrenkephalogramm des Menschen

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            Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain.

            This paper presents a new method for localizing the electric activity in the brain based on multichannel surface EEG recordings. In contrast to the models presented up to now the new method does not assume a limited number of dipolar point sources nor a distribution on a given known surface, but directly computes a current distribution throughout the full brain volume. In order to find a unique solution for the 3-dimensional distribution among the infinite set of different possible solutions, the method assumes that neighboring neurons are simultaneously and synchronously activated. The basic assumption rests on evidence from single cell recordings in the brain that demonstrates strong synchronization of adjacent neurons. In view of this physiological consideration the computational task is to select the smoothest of all possible 3-dimensional current distributions, a task that is a common procedure in generalized signal processing. The result is a true 3-dimensional tomography with the characteristic that localization is preserved with a certain amount of dispersion, i.e., it has a relatively low spatial resolution. The new method, which we call Low Resolution Electromagnetic Tomography (LORETA) is illustrated with two different sets of evoked potential data, the first showing the tomography of the P100 component to checkerboard stimulation of the left, right, upper and lower hemiretina, and the second showing the results for the auditory N100 component and the two cognitive components CNV and P300. A direct comparison of the tomography results with those obtained from fitting one and two dipoles illustrates that the new method provides physiologically meaningful results while dipolar solutions fail in many situations. In the case of the cognitive components, the method offers new hypotheses on the location of higher cognitive functions in the brain.
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              A method to standardize a reference of scalp EEG recordings to a point at infinity.

              D. Yao (2001)
              The effect of an active reference in EEG recording is one of the oldest technical problems in EEG practice. In this paper, a method is proposed to approximately standardize the reference of scalp EEG recordings to a point at infinity. This method is based on the fact that the use of scalp potentials to determine the neural electrical activities or their equivalent sources does not depend on the reference, so we may approximately reconstruct the equivalent sources from scalp EEG recordings with a scalp point or average reference. Then the potentials referenced at infinity are approximately reconstructed from the equivalent sources. As a point at infinity is far from all the possible neural sources, this method may be considered as a reference electrode standardization technique (REST). The simulation studies performed with assumed neural sources included effects of electrode number, volume conductor model and noise on the performance of REST, and the significance of REST in EEG temporal analysis. The results showed that REST is potentially very effective for the most important superficial cortical region and the standardization could be especially important in recovering the temporal information of EEG recordings.
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                Author and article information

                Contributors
                86-28-61830654 , dyao@uestc.edu.cn
                qyuner@163.com
                andy@neuroinformatics-collaboratory.org
                pedro.valdes@neuroinformatics-collaboratory.org
                Journal
                Brain Topogr
                Brain Topogr
                Brain Topography
                Springer US (New York )
                0896-0267
                1573-6792
                29 April 2019
                29 April 2019
                2019
                : 32
                : 4
                : 530-549
                Affiliations
                [1 ]ISNI 0000 0004 0369 4060, GRID grid.54549.39, The 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
                [2 ]ISNI 0000 0004 0369 4060, GRID grid.54549.39, School of Life Science and Technology, Center for Information in Medicine, , University of Electronic Science and Technology of China, ; Chengdu, China
                [3 ]Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
                Author notes

                Handling Editor: Christoph M. Michel.

                Author information
                http://orcid.org/0000-0002-8106-6137
                http://orcid.org/0000-0003-1670-7417
                Article
                707
                10.1007/s10548-019-00707-x
                6592976
                31037477
                7a8ed9f5-5043-42fc-a570-0890b919046b
                © The Author(s) 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 10 July 2018
                : 2 April 2019
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 81861128001
                Award Recipient :
                Funded by: National Natural Science Foundation of China
                Award ID: 61871105
                Award Recipient :
                Funded by: 111 project
                Award ID: B12027
                Award Recipient :
                Categories
                Review
                Custom metadata
                © Springer Science+Business Media, LLC, part of Springer Nature 2019

                Neurology
                rest reference,average reference,linked-mastoids reference,laplacian,bipolar reference
                Neurology
                rest reference, average reference, linked-mastoids reference, laplacian, bipolar reference

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