[Mne_analysis] ICA failing to exclude bad channels

Alexandre Gramfort alexandre.gramfort at telecom-paristech.fr
Tue May 30 05:33:28 EDT 2017
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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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