[Mne_analysis] ICA failing to exclude bad channels

Tue Jun 6 14:26:47 EDT 2017

Hi Dennis

This is the text on the MNE API page (v 0.14 stable) that we were working
off of:

No re-referencing:If the EEG data is already using the proper reference, set
 ref_channels=[]. This will prevent MNE-Python from automatically
re-referencing the data to an average reference.Average reference:A new
virtual reference electrode is created by averaging the current EEG signal.
Make sure that all bad EEG channels are properly marked and set
ref_channels=None.
In this case, we wanted to re-calculate the average reference after
removing the noisy channels. From my reading, [] will have no effect on the
reference, whereas "None" (somewhat counterintuitively) specifies that we
want the average reference.

The issue was that I stopped reading there, and didn't see the box below
that says:
*Note*
*In case of average reference (ref_channels=None), the reference is added
as an SSP projector and it is not applied automatically. For it to take
effect, apply with method apply_proj. For custom reference (ref_channel is
not None), this method operates in place.*

I will say this is pretty counterintuitive and inconsistent: depending on
what reference you choose you may or may not have to subsequently run
apply_proj. I agree that a more robust/consistent/intuitive API would be
appreciated and likely help prevent errors in people's workflows.

thanks
Aaron

On Tue, 6 Jun 2017 at 12:48 Denis-Alexander Engemann <
denis.engemann at gmail.com> wrote:

>
> 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> wrote:
>
>> Dear MNE users,
>>
>>
>> Just a reminder - we were using the following lines of code to exclude
>> bad channels:
>>
>>
>> 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 <
>> mne_analysis-bounces at nmr.mgh.harvard.edu> on behalf of Aaron Newman <
>> 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
>>
>>
>> This sender failed our fraud detection checks and may not be who they appear to be. Learn about
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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['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>
>> 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>
>>> > 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['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, 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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>>> > >
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>>
>> --
>>
>> Aaron J Newman, PhD
>>
>> Professor
>>
>> Director, RADIANT CREATE Neurotechnology Innovation Training Program
>>
>> Director, NeuroCognitive Imaging Lab (NCIL)
>>
>> FACULTY OF SCIENCE
>>
>> Department of Psychology & Neuroscience
>>
>> FACULTY OF MEDICINE
>>
>> Departments of Pediatrics, Psychiatry, and Surgery
>>
>> Aaron.Newman at dal.ca
>>
>> +1.902.488.1973 <+1%20902-488-1973> | fax: +1.902.494.6585
>> <+1%20902-494-6585>
>>
>> Box 15000, Life Sciences Centre, Halifax NS B3H 4R2 Canada
>>
>> DALHOUSIE UNIVERSITY
>>
>> NCIL.science <http://ncil.science/>
>> dal.ca/RADIANT <http://www.dal.ca/RADIANT>
>> _______________________________________________
>> Mne_analysis mailing list
>> Mne_analysis at nmr.mgh.harvard.edu
>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>>
>>
>> The information in this e-mail is intended only for the person to whom it
>> is
>> addressed. If you believe this e-mail was sent to you in error and the
>> e-mail
>> contains patient information, please contact the Partners Compliance
>> HelpLine at
>> http://www.partners.org/complianceline . If the e-mail was sent to you
>> in error
>> but does not contain patient information, please contact the sender and
>> properly
>> dispose of the e-mail.
>>
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>
>
> The information in this e-mail is intended only for the person to whom it
> is
> addressed. If you believe this e-mail was sent to you in error and the
> e-mail
> contains patient information, please contact the Partners Compliance
> HelpLine at
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> error
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-- 

Aaron J Newman, PhD

Professor

Director, RADIANT CREATE Neurotechnology Innovation Training Program

Director, NeuroCognitive Imaging Lab (NCIL)

FACULTY OF SCIENCE

Department of Psychology & Neuroscience

FACULTY OF MEDICINE

Departments of Pediatrics, Psychiatry, and Surgery

Aaron.Newman at dal.ca

+1.902.488.1973 | fax: +1.902.494.6585

Box 15000, Life Sciences Centre, Halifax NS B3H 4R2 Canada

DALHOUSIE UNIVERSITY

NCIL.science <http://ncil.science/>
dal.ca/RADIANT <http://www.dal.ca/RADIANT>
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