[Mne_analysis] ICA failing to exclude bad channels

Tue Jun 6 11:54:26 EDT 2017

Hi Denis,

Thanks for your reply. Apologies if I was unclear in my last email - the problem has actually been resolved. I was simply posting this in order to close the thread and provide help for anyone who might have the same problem in the future :)

Regards

Lyam.

---------------------------------------------------------

Lyam Bailey, BSc.

Graduate Student
Department of Psychology & Neuroscience
Dalhousie University

________________________________
From: mne_analysis-bounces at nmr.mgh.harvard.edu <mne_analysis-bounces at nmr.mgh.harvard.edu> on behalf of Denis-Alexander Engemann <denis.engemann at gmail.com>
Sent: Tuesday, June 6, 2017 4:48:10 PM
To: Discussion and support forum for the users of MNE Software
Subject: Re: [Mne_analysis] ICA failing to exclude bad channels

Hi,

Did you try:

```Python
raw, ref_data = set_eeg_reference(raw, ref_channels=[],copy=False)
```

As far as I remember ref_channels=None just does nothing. Passing an empty list should trigger average referencing.

Best,
Denis

On Tue, Jun 6, 2017 at 5:45 PM Lyam Bailey <Lyam.Bailey at dal.ca<mailto:Lyam.Bailey at dal.ca>> wrote:

Dear MNE users,

Just a reminder - we were using the following lines of code to exclude bad channels:

raw.info<http://raw.info>['bads'] = ['CP1',etc...]
raw, ref_data = set_eeg_reference(raw, ref_channels=None,copy=False)

It turns out that set_eeg_reference was not applying an average reference, which caused our problem. This is fixed by adding raw.apply_proj() immediately after the set_eeg_reference line.

Kind regards

Lyam

---------------------------------------------------------

Lyam Bailey, BSc.

Graduate Student
Department of Psychology & Neuroscience
Dalhousie University

________________________________
From: mne_analysis-bounces at nmr.mgh.harvard.edu<mailto:mne_analysis-bounces at nmr.mgh.harvard.edu> <mne_analysis-bounces at nmr.mgh.harvard.edu<mailto:mne_analysis-bounces at nmr.mgh.harvard.edu>> on behalf of Aaron Newman <Aaron.Newman at dal.ca<mailto:Aaron.Newman at dal.ca>>
Sent: Tuesday, May 30, 2017 6:13:36 PM
To: Discussion and support forum for the users of MNE Software
Subject: Re: [Mne_analysis] ICA failing to exclude bad channels

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Hi all

Just to provide a bit more background on behalf of Lyam - we definitely have the noisy channels explicitly listed in raw.info<http://raw.info>['bads'] and told ICA to ignore these. However, the single IC we get weights entirely on the period of the experiment when the bad channels were going crazy (which was one period in the middle of the experiment).

Some additional background that might be helpful: We're using an ANT 72 channel amp (same as the BrainProducts QuickAmp), which records the data relative to the average reference. An unfortunate consequence of that is that if any channels are particularly noisy (as they were here, due to bad electrodes), the noise gets introduced into all the other channels. So, as Lyam said, immediately after import we mark the bad channels as "bads" and then re-reference to the average of the remaining channels. Visually, this is quite effective at removing the artifacts - during the period where the artifacts were present, we can now see the EEG clearly and subjectively, it doesn't look any noisier or otherwise different from other periods of the experiment. I suppose it's possible that ICA is sensitive to residual crap in that section of the EEG that isn't obvious to the naked eye; however based on visual inspection I find it very difficult to believe that 99% of the variance in the entire dataset is attributable to such hypothetical, non-obvious residual noise.

Anyway, Lyam will share the data and code with you, Alex, and hopefully you can help us out! This is not an isolated case - quite a few data sets were acquired in three different experiments before we replaced the bad electrodes, so we are highly motivated to find a solution!

Thanks in advance,
Aaron

On Tue, 30 May 2017 at 06:36 Phillip Alday <Phillip.Alday at unisa.edu.au<mailto:Phillip.Alday at unisa.edu.au>> wrote:
I suspect the problem may be in the definition of 'bads' -- Lyam are
you explicitly marking channels 'bad' or you expecting automatic
detection of bad channels (as e.g. some EEGLAB functions do)?

Phillip

On Tue, 2017-05-30 at 11:33 +0200, Alexandre Gramfort wrote:
> Dear Lyam,
>
> ICA does ignore the bad channels see for example:
>
> https://github.com/mne-tools/mne-python/blob/master/mne/preprocessing
> /ica.py#L408
>
> can you share a full gist of code to replicate the problem?
>
> Alex
>
>
> On Mon, May 29, 2017 at 8:37 PM, Lyam Bailey <Lyam.Bailey at dal.ca<mailto:Lyam.Bailey at dal.ca>>
> wrote:
> >
> > Dear MNE users,
> >
> >
> > I'm trying to analyse some EEG data which contains a few very noisy
> > channels
> > (amplitude is often to the order of 1V). This seems to be causing
> > problems
> > with ICA, even after bad channels are excluded
> >
> >
> > I begin EEG preprocessing by excluding the bad channels, and then
> > re-referencing the data to the average of all remaining channels
> > with:
> >
> >
> > raw.info<http://raw.info>['bads'] = ['CP1',etc...]
> > raw, ref_data = set_eeg_reference(raw, ref_channels=None,
> > copy=False)
> >
> > After filtering and trial-by-trial artifact rejection, I run ICA
> > with:
> >
> > ica = mne.preprocessing.ICA(n_components=.99, method='fastica',
> >                             max_iter=500,
> > random_state=ica_random_state)
> > picks = mne.pick_types(epochs.info<http://epochs.info>, meg=False,
> >                        eeg=True, eog=False, stim=False,
> > exclude='bads')
> > ica.fit(epochs)
> >
> > This usually outputs a single IC component, and does nothing to
> > address
> > blinks/saccades etc that are clearly present in the raw data. My
> > feeling is
> > that ICA is somehow failing to exclude the bad channels, meaning
> > that (in
> > the presence of much higher variance, introduced by the noisy
> > channels) it
> > is relatively blind to 'normal' artifacts in the EEG.
> >
> > Does anyone know why this might be happening? Any advice on the
> > problem
> > would be greatly appreciated!
> >
> > Regards
> > Lyam
> >
> > ---------------------------------------------------------
> >
> > Lyam Bailey, BSc.
> >
> > Graduate Student
> > Department of Psychology & Neuroscience
> > Dalhousie University
> >
> >
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