[FieldTrip] EEG dataset with multiple electrode caps

Tue Jul 9 14:23:02 CEST 2019

Soren.

We have faced this issue with the EGI net going from the "GSN" to the "HydrocelGSN"; both are versions of a 128 channel cap but have different locations.  We also face this with translating the EGI 128 channel caps to 10-10 positions.  Our solution is to have 3d scalp electrode locations for each net and transforming one electrode system into another with spherical spine interpolation. We also use this technique to translate the HGSN net into the 10-10 locations for analysis of ERP data.

E.g.   some studies we did have 128-channel GSN EGI caps for some subjects,  and 128-channel HGSN EGI caps for other subjects.  We know the electrode locations on individuals who had a MRI, had both caps fitted with the EGI GPS system, and thus have the coordinates for the two systems on the same individual.  Or, we can use average electrode locations for the two caps.  Or.  We have the HGSN net, locations measured with the EGI GPS,  and an MRI on an individual, have derived the 10-10 locations from formulas to derive it on the MRI.  The locations on the H-GSN net can then be translated from the H-GSN to the 10-10 sites.  I also think this is preferable to combining electrodes across individuals with the same net since the actual net placement may mean that combining a specific electrode (e.g., #72) from two individuals actually combines the EEG from different places on the scalp due to head size, net placement differences, etc; the 10-10 system provides a common metric across individuals, across net types in your case, and across labs where different net configurations or EEG holder systems are used.

See Gao et al 2019 for an example of the H-GSN to 10-10 use, and Richards-et al 2018, for a MAtLAB program that does the spherical spline interpolation; both on my www site.  See Richards et al 2015 for avg electrode locations for the EGI 128 channel systems and 10-10 systems, though I understand in your case you have 64 channel electrodes.

Re doing both with source analysis.  This would be "ok", only if you knew the positions of the electrodes on the scalp accurately.  Source analysis requires the scalp electrode locations be known in order to create an  lead-field matrix.   If you have accurate scalp locations, then using the interpolation method would give you a common space, either in one of your two electrode systems, or into the 10-10 system.  If you don't know the scalp locations then source analysis would be inaccurate and probably with the same level of inaccuracy of just translating one system to the other system by "eyeball-estimates" of comparable electrodes.  Note that one of the drawbacks of many published source analysis papers is the lack of a measurement of electrode locations on individuals and the use of avg electrode locations even when a participant MRI is available.  

John

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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://jerlab.sc.edu
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Today's Topics:

   1. Re: EEG dataset with multiple electrode caps
      (Schoffelen, J.M. (Jan Mathijs))

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Message: 1
Date: Tue, 9 Jul 2019 09:34:27 +0000
From: "Schoffelen, J.M. (Jan Mathijs)" <jan.schoffelen at donders.ru.nl>
To: FieldTrip discussion list <fieldtrip at science.ru.nl>
Subject: Re: [FieldTrip] EEG dataset with multiple electrode caps
Message-ID: <73683A4A-5EF5-4E6F-90FB-E166B2EA07DE at donders.ru.nl>
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Dear Soren,

Tricky business, I’d say. Let’s assume that you have  a more or less balanced number of subjects that were recorded with each of the caps. This at least would allow you to study the spatio(spectro)temporal response (and derived measures, such as the correlation with a behavioral variable) per subset, as well as to investigate the effect of ‘group membership’ on the result.

I think that each of the options are viable, but I would go for option 2). Your electrode caps seem to be sufficiently spatially dense to allow for spatial interpolation, for instance using ft_channelrepair (treating the target electrodes as the ‘missing’ channels). I would treat both groups of subjects with a spatial interpolation, rather than interpolating one of the groups onto the other one. Also, I think I’d try to avoid a source reconstruction step if spatial interpolation could do the trick.

It is hard to make a definitive statement about the ‘accuracy’ of each of the methods in general, but I’d think of both of them as a mapping procedure that projects the data into a ‘common space’, which subsequently allows for treating the individual data points’ spatial location to be comparable across subjects, which is a prerequisite for the spatial clustering.

Best wishes,
Jan-Mathijs

> On 8 Jul 2019, at 22:36, Soren Emmanuel Wainio-Theberge <swain083 at uottawa.ca> wrote:
> 
> Hello all,
> 
> My name is Soren Wainio-Theberge, and I'm working in the Mind, Brain Imaging and Neuroethics unit in Ottawa, Canada. I have a dataset where we switched electrode caps partway through the experiment in order to obtain fNIRS data on some subjects. There is some limited overlap between the caps (a cluster of four electrodes in the parietal region and two in the frontal), but for the most part the electrode locations are different. I'm now looking to use cluster-based permutation testing with the whole dataset, testing a correlation with a psychological variable. Is this possible? If so, what would be the most sound approach? I can see three options at the moment:
> 
> 1) Convert all subjects with the fNIRS cap to the original cap by interpolating those electrodes. 
> 2) For each cap, interpolate the electrodes of the other cap to get a common space with more electrodes. 
> 3) Do the analysis in source space to avoid the electrode cap issue altogether (though we only have 64 electrodes for each cap and no individual anatomy, so source estimation won't be very accurate). 
> 
> Which is the best option from the perspective of a) the cluster test and multiple comparisons (ie does option 2 cause more issues for the cluster testing than option 1), and b) the accuracy of the method in general ( interpolation vs source space)? Or is there another possibility which I haven't thought of?
> 
> Thanks very much,
> Soren
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