[Mne_analysis] ICA failing to exclude bad channels

Tue May 30 13:13:36 EDT 2017

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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-- 

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/>
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