[Mne_analysis] Mne_analysis Digest, Vol 142, Issue 20
Britta Westner
britta.wstnr at gmail.com
Fri Nov 8 15:09:44 EST 2019
External Email - Use Caution
Hi Jeff,
maybe I can chime in here.
Yes, normalizing the lead field is also known as the array-gain beamformer
(you basically normalize the lead field instead of the beamformer weights
as another way to control the center-of-head bias). This is implemented in
the DICS as the parameter "normalize_fwd".
Since you asked about "beamformer approaches", note that in the LCMV code
it is the "depth" parameter that controls this - and this will be unified
among beamformers with the effort Marijn described in his email earlier.
Hope this helps,
Britta
On Fri, Nov 8, 2019 at 2:24 PM Jeff Hanna <jeff.hanna at gmail.com> wrote:
> External Email - Use Caution
>
>
> Hello Marijn,
>
> Thank you; that was helpful. One quick follow-up: In reference [5],
> weight_norm is set to None, but before the beamformer filters are
> calculated, the leadfield matrix column vectors are normalised to unity. If
> I understand correctly, this is equivalent to array-gain weight
> normalisation (which is as far as I know not directly implemented in
> MNE-Python beamformer approaches). Is that correct?
>
> Best,
> Jeff
>
> On Fri, 8 Nov 2019 at 12:14, <mne_analysis-request at nmr.mgh.harvard.edu>
> wrote:
>
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>> Today's Topics:
>>
>> 1. Re: differing weight_norm defaults for make_lcmv and
>> make_dics (Marijn van Vliet)
>> 2. Re: [FORUM] Re: How to edit time attribute in a raw object
>> (Fabrice DUPRAT)
>>
>>
>> ----------------------------------------------------------------------
>>
>> Message: 1
>> Date: Thu, 7 Nov 2019 23:46:58 +0200
>> From: Marijn van Vliet <w.m.vanvliet at gmail.com>
>> Subject: Re: [Mne_analysis] differing weight_norm defaults for
>> make_lcmv and make_dics
>> To: Discussion and support Software <mne_analysis at nmr.mgh.harvard.edu>
>> Message-ID: <C30CB78B-6660-4682-9E85-CBAAE8A6EB8B at gmail.com>
>> Content-Type: text/plain; charset="us-ascii"
>>
>> External Email - Use Caution
>>
>> Hello Jeff,
>>
>> there is currently no consensus for the best choice of parameters for
>> beamformers. It seems that every work of literature on the topic has its
>> own way of doing things, hence the enormous number of parameters. The LCMV
>> beamformer is developed by Britta Westner and reflects the practices of the
>> Center of Functionally Integrative Neuroscience, Aarhus University. The
>> DICS beamformer is developed by me and reflects the practices of the
>> Imaging Language Group, Aalto University. We are together with Christian
>> Kiefer in the process of performing an extensive comparison study to map
>> out the effects of all the parameters in practise.
>>
>> Here is what the docstring of make_dics has to say about it:
>>
>> The DICS beamformer is very similar to the LCMV (make_lcmv()) beamformer
>> and many of the parameters are shared. However, make_dics() and make_lcmv()
>> currently have different defaults for these parameters, which were settled
>> on separately through extensive practical use case testing (but not
>> necessarily exhaustive parameter space searching), and it remains to be
>> seen how functionally interchangeable they could be.
>>
>> The default setting reproduce the DICS beamformer as described in [5]:
>>
>> inversion='single', weight_norm=None, normalize_fwd=True
>> To use the make_lcmv() defaults, use:
>>
>> inversion='matrix', weight_norm='unit-gain', normalize_fwd=False
>>
>> References:
>> [5] van Vliet, et al. (2018) Analysis of functional connectivity and
>> oscillatory power using DICS: from raw MEG data to group-level statistics
>> in Python. bioRxiv, 245530. https://doi.org/10.1101/245530
>>
>> best,
>> Marijn.
>>
>> --
>> Marijn van Vliet
>> Postdoctoral Researcher
>> Department of Neuroscience and Biomedical Engineering
>> Aalto University
>>
>> > On 7 Nov 2019, at 16:55, Jeff Hanna <jeff.hanna at gmail.com> wrote:
>> >
>> > External Email - Use Caution
>> >
>> >
>> > Hello,
>> >
>> > The default value for weight_norm in mne.beamformer.make_lcmv is
>> "unit-noise-gain," but is None in .make_dics. Is there a reason behind
>> this, i.e. are there potential issues with the use of unit-noise-gain in
>> DICS that aren't a problem with LCMV?
>> >
>> > Best,
>> > Jeff Hanna
>> > _______________________________________________
>> > Mne_analysis mailing list
>> > Mne_analysis at nmr.mgh.harvard.edu
>> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>>
>>
>>
>>
>>
>>
>> ------------------------------
>>
>> Message: 2
>> Date: Fri, 8 Nov 2019 12:13:33 +0100
>> From: "Fabrice DUPRAT" <duprat at ipmc.cnrs.fr>
>> Subject: Re: [Mne_analysis] [FORUM] Re: How to edit time attribute in
>> a raw object
>> To: "'Discussion and support forum for the users of MNE Software'"
>> <mne_analysis at nmr.mgh.harvard.edu>
>> Message-ID: <00ae01d59625$8f5f4700$ae1dd500$@ipmc.cnrs.fr>
>> Content-Type: text/plain; charset="utf-8"
>>
>> External Email - Use Caution
>>
>> Eric
>>
>>
>>
>> Can we directly modify the ` <http://raw.info> raw.info['meas_date']`
>> value without creating an annotation ?
>>
>>
>>
>> How ?
>>
>>
>>
>> Thank you in advance
>>
>>
>>
>>
>>
>>
>>
>> De : mne_analysis-bounces at nmr.mgh.harvard.edu [mailto:
>> mne_analysis-bounces at nmr.mgh.harvard.edu] De la part de Eric Larson
>> Envoy? : lundi 4 novembre 2019 15:22
>> ? : Discussion and support forum for the users of MNE Software <
>> mne_analysis at nmr.mgh.harvard.edu>
>> Objet : [FORUM] Re: [Mne_analysis] How to edit time attribute in a raw
>> object
>>
>>
>>
>> External Email - Use Caution
>>
>> If the time of recording start is properly encoded in the file and read
>> by MNE (which is hopefully the case), then you should be able to use it.
>> You can manually convert from relative (starting from zero) to absolute
>> time by looking at `raw.info <http://raw.info> ['meas_date']` and
>> `raw.first_samp`. But the eaiser thing to do would be to construct an
>> Annotations <https://mne.tools/stable/generated/mne.Annotations.html>
>> class with your behavioral/pupil events (in whatever starting time frame
>> you prefer), then set `raw.annotations = my_annotations`, which should
>> convert it to the raw object's time reference in `raw.info <
>> http://raw.info> ['meas_date']`. Check out the Annotations tutorials --
>> such as this one <
>> https://mne.tools/dev/auto_tutorials/raw/plot_30_annotate_raw.html#creating-annotations-programmatically>
>> -- and if it's not clear from them how to do it, we should improve the
>> documentation.
>>
>>
>>
>> We do not allow modifying the `raw.times` attribute intentionally. A lot
>> of downstream code (e.g., epoching) would break, loudly or (worse)
>> silently, in unexpected and unpredictable if users could do this.
>>
>>
>>
>> Eric
>>
>>
>>
>>
>>
>> On Mon, Nov 4, 2019 at 6:03 AM vahid.bokharaie at tuebingen.mpg.de <mailto:
>> vahid.bokharaie at tuebingen.mpg.de> <vahid.bokharaie at tuebingen.mpg.de
>> <mailto:vahid.bokharaie at tuebingen.mpg.de> > wrote:
>>
>> External Email - Use Caution
>>
>> Hi Philip
>> Thanks for your sharp response. It calarified the situation for me.
>> And to answer to your question, for rat EEG, we record behavioural data
>> in a go/no-go task, pupil diameters and EEG. I define Events based on
>> behavioural and pupil size, and then should convert them into proper format
>> to be read in mne. So it is important that time stamps match. Of course
>> this is something that can be handled easily, but I could not believe such
>> a simple feature is not incorporated into mne. Hence spent too much time
>> trying to find the right function.
>> Regards,
>> Vahid
>>
>>
>>
>> -------- Original Message --------
>> Subject: Re: [Mne_analysis] How to edit time attribute in a raw object
>> From: Phillip Alday
>> To: Discussion and support forum for the users of MNE Software ,Vahid
>> Bokharaie
>> CC:
>>
>>
>>
>>
>> The time in the raw recordings (the Raw object) are assumed to be
>> arbitrary and just a measure of time elapsed since the beginning of the
>> recording, so there's not really a way to set or manipulate them. Why do
>> you need absolute time here? Are you concatenating Raw objects?
>>
>> Best,
>> Phillip
>>
>> On 4/11/19 10:55 am, Vahid Bokharaie wrote:
>> > External Email - Use Caution
>> >
>> > Hi all
>> > I posted this question in stackoverflow but seems MNE community is not
>> > active there, hence I searched for MNE mailing list and joined it.
>> > I use MNE for analysis of both rat and human EEG recordings. The system
>> > we have for rat EEG is from a company called NeuroNexus, and they have
>> > their own Matlab script to convert the EEG recordings and the time
>> > stamps into mat files. And then I import them in python and use MNE to
>> > analyse and visualise them. This means I have to establish my EEG raw
>> > objects from the scratch and save them to file, which I can do. But a
>> > seemingly trivial thing that I have not managed to find out how to do
>> is
>> > to incorporate the time array into the EEG raw object. When I create
>> and
>> > save the EEG raw object, it automatically set the initial time to 0,
>> but
>> > my initial recording time is not 0 and I need to offset that. It is
>> > important to correct this, because the time stamps of EEG and
>> > behavioural data should match.
>> > Simply typing |raw.times = new_array| leads to |AttributeError: can't
>> > set attribute. ||This should be a very simple thing to do, but I have
>> > not been able to find it although I have looked into the documentation.
>> > So, I thought to ask it in here and if it is not really possible, then
>> > dig into the source code to fix it myself. Thanks in advance.
>> > Regards,
>> > Vahid
>> > |
>> >
>> > --
>> >
>> ---------------------------------------------------------------------------------------
>> > Vahid S. Bokharaie, PhD
>> > Senior Research Scientist
>> > Max Planck Institute for Biological Cybernetics
>> > Department of Physiology of Cognitive Processes
>> > Max Planck Ring 8, 72076 Tuebingen, Germany
>> > +49 (0)7071 - 601-1644
>> > vahid.bokharaie at tuebingen.mpg.de <mailto:
>> vahid.bokharaie at tuebingen.mpg.de>
>> > https://www.kyb.tuebingen.mpg.de/person/59086/84548
>> >
>> --------------------------------------------------------------------------------------
>> >
>> >
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