[FieldTrip] Precisions on DICS beamforming on EEG data and normalisation

Wed Mar 15 09:44:56 CET 2017

Dear Michel-Pierre,

Allow me to add some additional (unfortunately non-referenced) advice.

1) Is it appropriate to perform DICS beamforming using EEG with 60 channels (standard montage) ? If not, what would be the appropriate number of channels ? Can you suggest a reference discussing this issue ?

First, make sure your data are referenced to the common-average as the forward model assumes this. Then, the appropriate number of channels depends on the required spatial resolution; If you wish to source localize posterior alpha activity 60 channels is fine. If you wish to parcellate your brain into 100 regions and do whole-brain connectivity, 60 channels is not fine and you might want to consider switching to MEG as well.

2) When not using a contrast to perform the beamforming, is the normalisation of the lead field an adequate procedure to correct for the depth/centre bias ? The Fieldtrip tutorial suggest it is but other posts on the mailing list suggest that it is not.

You say that you are looking at a change of alpha in response to a visual stimulus? It seems like you do have a contrast. You can compare to the baseline.

3) How does one choose an optimal time window for DICS beamforming when the duration of the effect is quite long (e.g. several seconds of alpha changes in response to a visual stimulus) ? Is it correct to use a longer time-window (e.g. 2 seconds) that is representative of the duration of the effect ?

Together with the previous point, you can compare your time window of interest to your baseline. Here it is important that you take the same window length from the baseline period as you take during the activation period to prevent a bias towards the window with more data when calculating the common filter.  However, according to http://www.fieldtriptoolbox.org/example/common_filters_in_beamforming it is fine to have an unequal amount of trials in each conditions so if your baseline period is only 1 second, you could cut your 'active' period into 1s segments using ft_redefinetrial so you can still use all of the data.

Best,

Jim

-----Original Message-----
From: fieldtrip-bounces at science.ru.nl [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Marc Lalancette
Sent: Tuesday, March 14, 2017 5:47 PM
To: fieldtrip at science.ru.nl
Cc: mpcoll at mac.com
Subject: Re: [FieldTrip] Precisions on DICS beamforming on EEG data and normalisation

Hi Michel-Pierre,

Regarding question 2, I'm mostly familiar with LCMV, and I can't remember exactly how DICS works, but I would guess normalization approaches have the same properties in both.  (Please someone correct me on this if I'm wrong.)  One great reference for LCMV beamformer in general, and normalization in particular, is the book by Sekihara and Nagarajan.  For a scalar beamformer, yes normalizing the leadfield ("array-gain") will correct depth bias, but I find these absolute values harder to interpret.  Dividing instead by projected noise ("unit-noise-gain") also corrects depth bias, and has better spatial resolution.  For a vector beamformer, things get a bit more complicated as the "array-gain" and "unit-noise-gain" vector formulae in that book are not rotationally invariant and I would not recommend using them.  (See my recent post: https://mailman.science.ru.nl/pipermail/fieldtrip/2017-March/011390.html)  Fieldtrip does not by default use these normalizations, but I also haven'!

t seen an analysis of (or had time to investigate much) how its vector beamformer normalization approach fares in terms of bias and resolution compared to others.  Maybe it exists somewhere?  Sorry if it's not a very practical answer...

Cheers,

Marc Lalancette

Lab Research Project Manager, Research MEG The Hospital for Sick Children, Diagnostic Imaging, Room S742

555 University Avenue, Toronto, ON, M5G 1X8

416-813-7654 x201535

-----Original Message-----

Date: Mon, 13 Mar 2017 12:47:25 +0000

From: MP Coll <mpcoll at mac.com<mailto:mpcoll at mac.com>>

To: fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>

Subject: [FieldTrip] Precisions on DICS beamforming on EEG data and

        using   normalisation

Message-ID: <28ea0ca0-fed2-3cba-aa11-a79cc8c7c1a3 at mac.com<mailto:28ea0ca0-fed2-3cba-aa11-a79cc8c7c1a3 at mac.com>>

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Dear Fieldtrip Community,

My name is Michel-Pierre Coll and I am a postdoctoral researcher at King's college London.

A reviewer recently suggested we perform DICS beamforming to source localise EEG effects in the frequency domain during an action observation/execution paradigm. I was able to perform these analyses using the very good tutorial on the Fiedltrip website. However, I have some questions regarding these analyses. I have searched the literature and the mailing list but somehow I can't find clear answers to these basic questions.

1) Is it appropriate to perform DICS beamforming using EEG with 60 channels (standard montage) ? If not, what would be the appropriate number of channels ? Can you suggest a reference discussing this issue ?

2) When not using a contrast to perform the beamforming, is the normalisation of the lead field an adequate procedure to correct for the depth/centre bias ? The Fieldtrip tutorial suggest it is but other posts on the mailing list suggest that it is not.

3) How does one choose an optimal time window for DICS beamforming when the duration of the effect is quite long (e.g. several seconds of alpha changes in response to a visual stimulus) ? Is it correct to use a longer time-window (e.g. 2 seconds) that is representative of the duration of the effect ?

I would greatly appreciate any hints on these questions or if you could point me towards relevant texts discussing these issues.

Best,

MP Coll

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