[Eeglablist] Comments on EEG and ERP reference

Sat Jan 3 21:53:51 PST 2026

There are two features of the Laplacian that prevent it from being recommended as the best montage. It completely fails to detect dipoles oriented parallel to the scalp surface. Even if we assume that dipoles are always perpendicular to the cortex, the presence of cortical folds can cause a dipole to be parallel to the scalp surface. And the Laplacian greatly attenuates the signal from deep sources.

> Hi Cedric -
> 
> Given the amount of energy that Ive put into the surface Laplacian, an even
> more pointed question might be - How come I dont use it that much?
> 
> (1) Surface Laplacian eliminates reference issue
> (2) Surface Laplacian isolates the high spatial frequency part of the data
> for which localization works well. The low spatial frequency part is
> always going to be hard to understand, because it reflects both deep
> sources and spatial distributed correlated sources.
> 
> So,it seems like a great idea as long as you have 64 or more electrodes.
> But,ou are not just filtering out volume conduction, you are also filtering
> out spatially coherent brain networks. these are often the parts of the
> data that have the strongest brain-behavior relationships. So, your
> correlation to behavior usually gets worse.
> 
> This latter point is why outside of domains like motor BCI or SSVEPs. where
> a well defined spatially localized source region is of interest, surface
> Laplacians have not been widely adopted.
> 
> Ramesh Srinivasan
> Professor
> Department of Cognitive Sciences
> Department of Biomedical Engineering
> 
> On Fri, Jan 2, 2026 at 6:36 AM Cedric Cannard via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
> 
>> My personal experience and the extensive literature (see bottom of email)
>> suggest REST is better than average reference (AR). And Mastoid references
>> can lead to serious signal cancellations due to polarity inversions, for
>> example, where phase can be opposite between hemispheres.
>>
>> When individual MRIs are available, one can use DIPFIT to compute the lead
>> field matrix from those MRIs, as Arno pointed out. This lead field matrix
>> can then be input into any of EEGLAB’s REST plugins (Yao’s original with
>> graphical user interface, my REST_cmd replica for batch/command line use,
>> and Arno’s FieldTrip-based implementation) for optimal results.
>>
>> Without individual MRIs, Yao recommends using a three-sphere or averaged
>> realistic head model. About head model accuracy, as Yao explained, the
>> nonuniqueness of the EEG inverse problem means head model errors can be
>> partially compensated by adjusting the equivalent source configuration.
>> Simulation studies consistently show that while head model errors reduce
>> REST performance, it typically remains better than AR. The key difference
>> is that REST utilizes 3D physics (the nonuniqueness of the EEG inverse in
>> 3D space), while AR uses 2D physics (surface potential integration being
>> zero).
>>
>> I’m surprised the surface Laplacian (CSD transform) hasn’t made it to
>> this thread yet. Should it be the default method when individual MRIs
>> aren’t available? The Laplacian estimates the second spatial derivative of
>> scalp potential, emphasizing local sources while attenuating distant
>> sources and volume conduction effects. It only requires accurate electrode
>> positions rather than a full volume conduction model. The trade-offs are
>> amplified high-frequency noise, required good electrode coverage, and
>> incompatibility with standard ERP literature comparisons that used other
>> referencing methods. But if the goal is to better reflect EEG signals not
>> contaminated by volume conduction (which I think should be everyone’s
>> goal), why don’t we use this method more as default?
>> Ramesh, I’m curious what your thoughts are around this with all the work
>> you’ve done in his area?
>>
>> For those looking for more literature on this topic, it is extensive:
>>
>> Nunez (2010). REST: A Good Idea but Not the Gold Standard. Clin
>> Neurophysio.
>>
>> Chella et al. (2016). Impact of the reference choice on scalp EEG
>> connectivity estimation. Journal of Neural Engineering.
>>
>> Chella et al. (2017). Non-linear Analysis of Scalp EEG by Using Bispectra:
>> The Effect of the Reference Choice. Frontiers in Human Neuroscience.
>>
>> Dong et al. (2017). MATLAB Toolboxes for Reference Electrode
>> Standardization Technique (REST) of Scalp EEG. Frontiers in Neuroscience.
>>
>> Lei et al. (2017). Understanding the Influences of EEG Reference: A
>> Large-Scale Brain Network Perspective. Frontiers in Neuroscience.
>>
>> Qin et al. (2017). A Comparative Study on the Dynamic EEG Center of Mass
>> with Different References. Frontiers in Neuroscience.
>>
>> Yang et al. (2017). A Comparative Study of Average, Linked Mastoid, and
>> REST References for ERP Components Acquired during fMRI. Frontiers in
>> Neuroscience.
>>
>> Hu et al. (2018). How do reference montage and electrode setup affect the
>> measured scalp EEG potentials? Journal of Neural Engineering.
>>
>> Zheng et al (2018). A Comparative Study of Standardized Infinity Reference
>> and Average Reference for EEG of Three Typical Brain States. Frontiers in
>> Neuroscience.
>>
>> Yao et al. (2019). Which Reference Should We Use for EEG and ERP
>> practice?. Brain Topography.
>>
>> Hu et al. (2019). The Statistics of EEG Unipolar References: Derivations
>> and Properties.
>>
>> Dong et al. (2023). Rereferencing of clinical EEGs with nonunipolar
>> mastoid reference to infinity reference by REST. Clinical Neurophysiology.
>>
>> Cedric Cannard
>>
>> Sent from Proton Mail for iOS.
>>
>> -------- Original Message --------
>> On Tuesday, 12/30/25 at 13:28 Joseph Dien via eeglablist <
>> eeglablist at sccn.ucsd.edu> wrote:
>> It's good that this discussion is happening. From such exchanges come
>> scientific consensus. I've been noticing REST come up from time to time
>> in the literature, but haven't invested the time to investigate it.
>> This seems like a good moment to seek further information by asking what
>> is the benefit of adopting the complexity of REST that you have just
>> described to us? The simplest method, of course, is just the
>> conventional reference, like the widely used mean mastoids. While the
>> average reference (AR) is modestly more complex, the arguments for the
>> average reference are not just biophysical accuracy (Bertrand et al.,
>> 1985), but also more practical reasons. Some reasons include:
>>
>> 1) Researcher intuitions can be misled when depiction of the scalp
>> voltage field is grossly distorted by the requirement of conventional
>> reference that the zero isopotential line runs through the location of a
>> single reference site (or the mean of two such sites as in the mean
>> mastoid). Examples are noted in Dien (1998).
>>
>> 2) Since typical ERP ANOVAs are in effect contrasts with the reference
>> site (see Dien, 1998), ERP components that have a peak channel located
>> near the reference site(s) suffer loss in statistical power compared to
>> those that are relatively distant, which has likely biased the
>> literature towards vertically oriented ERP components like the P300 and
>> the N400. The average reference is relatively even-handed since it by
>> definition locates the zero isopotential line between the positive and
>> negative peaks of a scalp topography (with the zero line corresponding
>> to the source generator, all things being equal, with many assumptions).
>>
>> 3) Just as averaging trials together improves the signal-to-noise ratio,
>> so too does averaging channels together in a regional channel (all
>> things being equal). This principle applies to the reference channel as
>> well as the recording channel. Thus there are benefits for the average
>> reference (which averages all the channels) compared to methods that
>> average only one or two channels (Dien, 2017). While the fact that the
>> average reference uses all the channels means that it is vulnerable to
>> bad channels, one can use artifact correction methods prior to
>> rereferencing, allowing the effects of such preprocessing to be fully
>> transparent.
>>
>> 4) At least some methods of source analysis, such as the dipole
>> equivalent procedure implemented by BESA, automatically rereference data
>> to average reference to better approximate the biophysics. Using
>> average reference in the waveforms thus keeps things more consistent,
>> facilitating sanity checks.
>>
>> With respect to algorithms like REST, a concern then would be that it
>> may introduce additional complexities without corresponding benefits.
>> For example, one can freely mathematically rereference between
>> conventional reference schemes like mean mastoids and average
>> reference. Can one do so with REST? I see the potential (sic) for the
>> output of REST to depend on the version and/or implementation of the
>> REST algorithm. We've already seen some issues like this with the
>> average reference when it is implemented incorrectly (Kim et al.,
>> 2023). But at least here, there is only one correct way to implement an
>> average reference, so there is no ambiguity introduced by algorithm
>> choices. This kind of concern has also made me hesitant to adopt the
>> PARE version of the average reference (Junghöfer et al., 1999), although
>> it does directly address the undersampling of the underside of the head,
>> and so I have implemented a version of it in my EP Toolkit (Dien, 2010).
>>
>> With regards to my four points above:
>>
>> 1) Even if REST leads to a more accurate rendition of the scalp voltage
>> fields than the average reference, will the difference be meaningful
>> with respect to effects on researcher intuition? I expect there would
>> be the same caveats regarding sensor coverage as there is for source
>> analyses in general? AR is also affected by sensor coverage, but the
>> practical motivations still apply.
>>
>> 2) I'm not sure how REST and AR would compare on this.
>>
>> 3) I'm not sure how REST would do here.
>>
>> 4) Since REST does its own head modeling, I worry about how one could
>> relate its output to that of source analysis programs.
>>
>> So in all, I guess I'm asking the infamous so what question (and with
>> full earnestness). I'd be very interested in empirical comparisons (I'm
>> always looking for better ways of doing things).
>>
>> Anyway, these are my two bits on the topic.
>>
>> Cheers!
>>
>> Joe
>>
>> Bertrand, O., Perrin, F., & Pernier, J. (1985). A theoretical
>> justification of the average reference in topographic evoked potential
>> studies. Electroencephalography and Clinical Neurophysiology, 62,
>> 462–464.
>> https://urldefense.com/v3/__https://doi.org/10.1016/0168-5597(85)90058-9__;!!Mih3wA!B9OtMgGLbtXZro6SwPMz9dhCrLPHGKViEiFL9D-9JpnAuEOc0vBDPtjv1aYvpFEYJ59jhNUx1w3Dn-UxHGo$
>>
>> Dien, J. (1998). Issues in the application of the average reference:
>> Review, critiques, and recommendations. Behavior Research Methods,
>> Instruments, & Computers, 30(1), 34–43.
>> https://urldefense.com/v3/__https://doi.org/10.3758/BF03209414__;!!Mih3wA!B9OtMgGLbtXZro6SwPMz9dhCrLPHGKViEiFL9D-9JpnAuEOc0vBDPtjv1aYvpFEYJ59jhNUx1w3DxPf3q0I$
>>
>> Dien, J. (2010). The ERP PCA Toolkit: An Open Source Program For
>> Advanced Statistical Analysis of Event Related Potential Data. Journal
>> of Neuroscience Methods, 187(1), 138–145.
>>
>> https://urldefense.com/v3/__https://doi.org/10.1016/j.jneumeth.2009.12.009__;!!Mih3wA!B9OtMgGLbtXZro6SwPMz9dhCrLPHGKViEiFL9D-9JpnAuEOc0vBDPtjv1aYvpFEYJ59jhNUx1w3DtuQc0Eg$
>>
>> Dien, J. (2017). Best practices for repeated measures ANOVAs of ERP
>> data: Reference, regional channels, and robust ANOVAs. International
>> Journal of Psychophysiology, 111(1), 42–56.
>>
>> https://urldefense.com/v3/__https://doi.org/10.1016/j.ijpsycho.2016.09.006__;!!Mih3wA!B9OtMgGLbtXZro6SwPMz9dhCrLPHGKViEiFL9D-9JpnAuEOc0vBDPtjv1aYvpFEYJ59jhNUx1w3DOdQOa8M$
>>
>> Junghöfer, M., Elbert, T., Tucker, D. M., & Braun, C. (1999). The polar
>> average reference effect: A bias in estimating the head surface integral
>> in EEG recording. Clinical Neurophysiology, 110(6), 1149–1155.
>>
>> Kim, H., Luo, J., Chu, S., Cannard, C., Hoffmann, S., & Miyakoshi, M.
>> (2023). ICA’s bug: How ghost ICs emerge from effective rank deficiency
>> caused by EEG electrode interpolation and incorrect re-referencing.
>> Frontiers in Signal Processing, 3.
>>
>> https://urldefense.com/v3/__https://doi.org/10.3389/frsip.2023.1064138__;!!Mih3wA!B9OtMgGLbtXZro6SwPMz9dhCrLPHGKViEiFL9D-9JpnAuEOc0vBDPtjv1aYvpFEYJ59jhNUx1w3DNGXfPEk$
>>
>> --
>>
>> --------------------------------------------------------------------------------
>>
>> Joseph Dien, PhD
>> Senior Research Scientist
>> Department of Human Development and Quantitative Methodology
>> University of Maryland, College Park
>>
>> https://urldefense.com/v3/__http://joedien.com__;!!Mih3wA!B9OtMgGLbtXZro6SwPMz9dhCrLPHGKViEiFL9D-9JpnAuEOc0vBDPtjv1aYvpFEYJ59jhNUx1w3DGQ8CVPI$
>>
>> On 12/29/25 09:17, dyao--- via eeglablist wrote:
>>> Dear Ramesh,
>>> Thanks a lot for your comments.
>>>
>>> About the head model effect on REST, the following message may be
>> helpgful
>>> (mainly from my book: Yao D. The Physics and Mathematics of
>> Electroencephalogram. CRC, 2024 )
>>>
>>>
>>> (1) V0=AX=A'X'=A''X''
>>> where, X are the true sources, X' are the equivalent sources, A /A' is
>> the lead array of the true head model corresponding to sources X/X'
>> respectively (sources positions diferent).
>>> If we have an approximate head model, then we have the approximate
>> lead array A'', and the corresonding X''.
>>> In (1), the reference is zero (at infinity).
>>> As each of X, X'(many forms), X''(many forms) may generate the same
>> true scalp recordings V0, we say that the EEG inverse is nonuniqueness. And
>> it is this nonuniqueness that provides the theoretical base of the
>> "equivalent sources principle" in electromagnetism, and give us chance to
>> develope REST.
>>>
>>> (2) Vr=ArX=Ar'X'=Ar''X''
>>> it is a modified version of equation(1) with a non-zero reference r,
>> which may be any a concrete reference,such AR, linked mastoids. Ar/Ar'
>> still is the lead array of the true head model, Ar'' is the approximate
>> lead array (the approximate head model), and the corresponding equivalent
>> sources X'' which working together with Ar'' can (approximately) generating
>> the actual recordings Vr.
>>> Equation（2） means that, if we have the correct model-Ar', we definitely
>> can have the equivalent sources X' (it may be dipole or charge or their
>> combination, etc ). If we do not have the correct model-Ar', or say if we
>> only have an approximate head model-Ar'', then we still may have an
>> approximate X'', which, with Ar'', may (approximately) generate the
>> actual recordings Vr, too. It means the error of the head model (Ar''- Ar')
>> may be partially (or numerically) compensated by an approximated equivalent
>> sources.
>>> Whether these guesses are true? or how about the final performance of
>> REST with approximate head model? there are many simulation studies
>> concerning this issues, and the results are positive, in a word, the head
>> model error may reduce the REST performance, but it is still usaully better
>> than AR in general.
>>>
>>>
>>> from equation (2),we have
>>> (3) X'=Ar'(+)Vr
>>> X''=Ar''(+)Vr
>>> here we see ,if the head model is approximate （Ar''- Ar'）, the
>> equivalent sources may be a little different for the same scalp potential
>> recordings Vr （ X'-X"）.
>>>
>>> from equation (3),we have
>>> (4) V0=AX=A'X'=A'Ar'(+)Vr=T'Vr
>>> or V0=AX=A'X'=A''X''=A''Ar''(+)Vr=T''Vr
>>>
>>> here we see, the equivalent sources only act as a "bridge", we do not
>> really need to know it. we only use T' and T'' which depends on the head
>> model and the correspondings equivalent sources positions (A, A', A''). In
>> REST practice, we may assume that the equivalent sources are located on a
>> closed surfce encovering the all actual sources (or a 3D distribution
>> overlaying the all actual sources), then we have A'/A'' and their
>> generalized inverse A'(+)/A''(+） .
>>>
>>> In REST practice, if you have individual head model, then you may take
>> V0=T'Vr, with T'=A'Ar'(+)
>>> for general case, without personal true head model, we recommend to use
>> the three-spheres head model or the averged realistic head model, then we
>> have V0=T''Vr, with T''=A''Ar''(+), here the T'' is provded by the REST
>> software, Vr is your eeg data, and V0 is the final with zero reference.
>>>
>>> In general, both REST and AR are based on "physical fact", the
>> difference is :
>>> REST utilizes the "nonuniqueness' of EEG inverse in the EEG related
>> 3D space, 3D physics involved.
>>> while, AR based on the scalp surface potential integral being zero (
>> assume the surface recordings are over a whole spherical head surface), 2D
>> physics involved.
>>> That's why these two references are usually much better than
>> Linked-ears or other unpolar references, and in general, REST is better
>> than AR, as it utilize more "physics" to constrain the estimate.
>>>
>>>
>>>
>>>
>>> best wishes
>>>
>>>
>>>> -----原始邮件-----
>>>> 发件人: "Richards, John"<RICHARDS at mailbox.sc.edu>
>>>> 发送时间:2025-12-29 07:12:35 (星期一)
>>>> 收件人: "Ramesh Srinivasan"<srinivar at uci.edu>,"dyao at uestc.edu.cn" <
>> dyao at uestc.edu.cn>
>>>> 抄送: eeglablist<eeglablist at sccn.ucsd.edu>
>>>> 主题: RE: [Eeglablist] Comments on EEG and ERP reference
>>>>
>>>> I have wondered about the head model used in the REST reference. In
>> addition to any other issue, if the head model is "incorrect" then are the
>> calculations of the reference also incorrect? Using an average template
>> for a head model has some issues in source reconstruction, and might have
>> some here also. Using a participant-defined, individual-based, realistic
>> head model, is better than an average MRI template head model in several
>> ways in source modeling. Might be so here also?
>>>>
>>>> John
>>>>
>>>> ***********************************************
>>>> John E. Richards
>>>> Carolina Distinguished Professor
>>>> Department of Psychology
>>>> University of South Carolina
>>>> Columbia, SC 29208
>>>> Dept Phone: 803 777 2079
>>>> Fax: 803 777 9558
>>>> Email:richards-john at sc.edu
>>>>
>> https://urldefense.com/v3/__https://jerlab.sc.edu/__;!!Mih3wA!B_y6AojvL8UK_viRzlNr2q7Xqoiu0WDu3k3h_W18Ou0WCZt0Lyr5WvORKF3yOahNTzBBsuIZRyMhupHFOEwQbw$
>>>> ***********************************************
>>>>
>>>> -----Original Message-----
>>>> From: eeglablist<eeglablist-bounces at sccn.ucsd.edu> On Behalf Of Ramesh
>> Srinivasan via eeglablist
>>>> Sent: Sunday, December 28, 2025 1:25 AM
>>>> To:dyao at uestc.edu.cn
>>>> Cc: eeglablist<eeglablist at sccn.ucsd.edu>
>>>> Subject: Re: [Eeglablist] Comments on EEG and ERP reference
>>>>
>>>> Hi Dezhong,
>>>>
>>>> Id like to comment first on your quote from Luck's book and then about
>> your method REST.
>>>>
>>>> First, Luck is apparently unaware of the argument for the average
>> reference, or of the physical principles underlying them and is
>> intellectually lazy by focusing on whether the models used to test these
>> ideas are spherical or realistic. That has nothing to do with it.
>>>>
>>>> The critical issue is whether the head is a closed object, not whether
>> it
>>>> is a sphere. In other words, is there evidence that current flows out
>> of
>>>> head into neck and vice versa. The preponderance of evidence suggests
>> that very little current generated by sources in the brain travels down the
>> neck into the body. How do we know this? Well, the simplest evidence is
>> that we can record EEG at all. If there was a significant current path
>> through the neck, EEG recordings would be overwhelmed by EKG which is
>> orders of magnitude larger signal. Hence it is reasonable to assume that
>> the neck is like a giant resistor limiting current flow in both directions.
>>>> From the point of view of current flow from brain sources the head is
>> mostly a closed object. Indeed your REST method hinges on this being true
>> because you dont use a volume conduction model of the whole body, just the
>>>> head. So, this quotation from Luck is just wrong, and is inconsistent
>>>> with your views and methods.
>>>>
>>>> If current is contained with the head, then the potentials on the head
>> should sum to zero. But we can never measure the underside of the head, so
>> the average of the potentials we measure indeed must have some error.
>>>> Thus, your papers make a good argument for REST. I would only caution
>> that the head model is approximate and not exact and is thus a potential
>> source of error as well.
>>>>
>>>> Ramesh Srinivasan
>>>> Professor
>>>> Department of Cognitive Sciences
>>>> Department of Biomedical Engineering
>>>>
>>>>
>>>>
>>>> On Sat, Dec 27, 2025 at 7:55 PM dyao--- via eeglablist <
>> eeglablist at sccn.ucsd.edu> wrote:
>>>>
>>>>> Hello everyone,
>>>>> Since the discover of EEG in 1924, EEG reference is continuously a
>>>>> debate issue, which one is the best?
>>>>> 1. Prof Arnaud Delorme, the first author of the EEGLAb, has a
>>>>> video on "What is the best EEG reference?"
>>>>>
>> https://urldefense.com/v3/__https://urld/__;!!Mih3wA!B_y6AojvL8UK_viRzlNr2q7Xqoiu0WDu3k3h_W18Ou0WCZt0Lyr5WvORKF3yOahNTzBBsuIZRyMhupG00uO9bA$
>>>>> efense.com%2Fv3%2F__https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3DioIETU
>>>>> X4G4k__%3B!!Mih3wA!H4eUzNmw2tOSr4Yu5olqnIPXoeADBK0vmXkMOIBvk_d6LqSb0l_
>>>>> YqNxv43Ug-3I3FuirE3dNqJiL7VkJeJYlMQ%24&data=05%7C02%7CRICHARDS%40mailb
>>>>> ox.sc.edu%7Cd8b0ceab7e034c20d0c108de464f8a78%7C4b2a4b19d135420e8bb2b1c
>>>>> d238998cc%7C0%7C0%7C639025504614617191%7CUnknown%7CTWFpbGZsb3d8eyJFbXB
>>>>> 0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsI
>>>>> ldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=uhG13aGkyG7yndRcQFYbgzvTMyeqjWqd9%2
>>>>> FlGO2UPFJg%3D&reserved=0 , the answer is REST 2. Prof Steven Luck,
>>>>> in his new textbook(Applied Event-Related Potential Data
>>>>> Analysis,2022), in section 6.1 "I’d like to point out
>>>>> using the average across sites as the reference in order to
>>>>> approximate the absolute voltage assumes that the surface of the head
>>>>> sums to zero, but this is only true for spheres. I have yet to meet
>>>>> someone with a spherical head. And no neck. Fortunately, there is a
>>>>> way to estimate the true zero, called the Reference Electrode
>>>>> Standardization Technique (REST), and there is an EEGLAB plugin that
>>>>> implements it (Dong et al., 2017). I haven’t tried it myself or looked
>>>>> at the math, so I don’t have an opinion about whether it’s useful and
>>>>> robust. But if you really want to get an estimate of the absolute
>> voltage, REST seems like the best current approach"
>>>>> 3. in 2014, Lepage etal annouanced that they designed an updated
>>>>> average reference (robust common average reference(rCAR))being better
>>>>> than REST, and since then rCAR were adopted by a few following work.
>>>>> however, a recent comment on rCAR,attached here, confirmed that rCAR
>>>>> is not an EEG reference method but an "noise removement method on
>>>>> various artificial data" as the all illustrative examples adopted in
>>>>> rCAR paper were not EEG data (assumed, not generated by sources inside
>>>>> a brain). In this comment paper, on Data generated by sources inside a
>>>>> head model, REST is much better than rCAR. (Lepage, K.Q., Kramer,
>>>>> M.A., Chu, C.J., 2014. A statistically robust EEG re-referencing
>>>>> procedure to mitigate reference effect. J. Neurosci. Methods 235,
>> 101–116).
>>>>> In general, REST and average reference(AR) are the best two as
>>>>> both of them are based on EEG physics. REST is based on the equivalent
>>>>> distributed-sources principle of the scalp potential, it depends on
>>>>> the 'equivalence between the unknown neural sources in the brain and
>>>>> the reconstructed equivalent sources in the brain', various
>>>>> simulations confirmed the equivalence depends on the cover range and
>>>>> density of the scalp electrode array. AR is based that the whole
>>>>> surface potential integral is zero if the head a sphere, apparently,
>> the weakness is that "
>>>>> our head is not a sphere, and the measurment is usually limited to the
>>>>> uper surface, not and impossible being the whole surface as we have
>>>>> the neck", various simulations confirmed AR depends on the cover range
>>>>> and density of the scalp electrode array,too. The conducted
>>>>> comparative studies showed that REST is usually better than AR
>> especially when electrode number>20.
>>>>> For both methods, the most important factor is the cover range, then
>>>>> is the density of the electrode density ,or say, the number and
>>>>> distribution of electrodes.
>>>>> wish the above message is meaningful for your work in EEG and ERP.
>>>>> Best wishes
>>>>> -----------------------------
>>>>> Dezhong Yao, PhD, CheungKong Professor AIMBE Fellow，Cuba Academico
>>>>> Correspondiente，CSBME Fellow Director, Brain-Apparatus Communication
>>>>> Institute
>>>>> Editor-in-Chief，Brain-Apparatus Communication, Taylor & Francis Group
>>>>> University of Electronic Science and technology of China, 611731,
>>>>> Chengdu, China _______________________________________________
>>>>> To unsubscribe, send an empty email to
>>>>> eeglablist-unsubscribe at sccn.ucsd.edu or visit
>>>>>
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>>>>> .
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>>>
>>> ------------------------------
>>> Dezhong Yao, PhD, CheungKong Professor
>>> AIMBE Fellow，Cuba Academico Correspondiente，CSBME Fellow
>>> Director, Brain-Apparatus Communication Institute
>>> Editor-in-Chief，Brain-Apparatus Communication, Taylor & Francis Group
>>> University of Electronic Science and technology of China, 611731,
>> Chengdu, China
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