Hi Vladimir,

Thank you so much for the references. I've followed them, and I seem to be close on getting it to work--just one problem has arisen.

I've got the montage variable copied from ~line 150 saved. S.montage contains the montage structure which includes labelorg, labelnew, tra, and keepunused ('yes') fields.

When I attempt to execute
S.D = epochedFilename; %filename of the data
S.mode = 'write';
S.montage = montage;
D = spm_eeg_montage(S);

the montage fails giving me the following error:
Error using scalingfactor (line 168)
cannot convert T to unknown

Error in scalingfactor (line 87)
  factor = cellfun(@scalingfactor, old(:), new(:));

Error in ft_apply_montage (line 293)
  scale = scalingfactor(input.chanunitnew, montage.chanunitorg);

Error in spm_eeg_montage (line 366)
                            balance = ft_apply_montage(getfield(sens.balance, sens.balance.current), sensmontage,
                            'keepunused', keepunused);

Upon perusing this, it looks like the issue is because I don't supply the type of the channel unit for the "new". How can I go about solving this problem?

Best,
Anne

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From: Vladimir Litvak [[log in to unmask]]
Sent: Monday, April 27, 2015 6:53 AM
To: Anne Keller
Cc: [log in to unmask]
Subject: Re: [SPM] ICA before Beamformer

Dear Anne,

First, you should apply your ICA correction in the proper way (as a montage in SPM). See https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=SPM;ba5c9963.1312 and the links therein for some discussions about this.

Secondly, the answer to your question depends on what kind of SPM beamforming you are using. If you are using the Bayesian beamformer in the MSP framework (EBB) I don't think you should do anything special because that framework includes automatic dimensionality reduction prior to inverse computation. If you are using the DAiSS toolbox for SPM12 you could do an explicit dimensionality reduction there and make sure your dimensionality is smaller than the minimal number of data components you might have. If you use the old SPM8 beamforming tools, there the only option is to regularise with some high enough coefficient to make sure there is no problem. I can't say what is 'high enough' you need to play with it.

Best,

Vladimir

On Tue, Apr 21, 2015 at 6:07 PM, Anne Keller <[log in to unmask]> wrote:

Dear SPM Community,

I'm working with MEG data collected from children, and I am hoping to do source localization via beamformer. Our kids have lots of eye movement and blinks, so I was hoping to perform ICA prior to beamformer. How can I address the matrix regularization aspect after I remove components? Is there a SPM function that corrects this? Due to lab requirements, I will be using SPM's beamformer (but Fieldtrip's ICA and reject components functions).

Thanks so much in advance!

Best,
Anne

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