# preprocessing/ICA: movement artifacts vs blinks **URL:** \ **Category:** Mailing List Archive (read-only)\ **Tags:** list-archive\ **Created:** [March 14, 2017, 12:44am UTC](https://mne.discourse.group/t/preprocessing-ica-movement-artifacts-vs-blinks/1193 "2017-03-14T00:44:11Z")\ **Posts on this page:** 6\ **Page:** 1
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [March 14, 2017, 12:44am UTC](https://mne.discourse.group/t/preprocessing-ica-movement-artifacts-vs-blinks/1193/1 "2017-03-14T00:44:11Z")
I have EEG data where the recording was continuous during breaks between blocks, so there are lots of movement artifacts during temporal spans that I ultimately don't care about. If I just epoch the data, those temporal spans go away, but I want to do blink rejection. Doing blink rejection on an Epochs object doesn't seem to work out of the box, and doing it on each epoch individually seems inefficient and possibly dodgy. Question 1: is there a sensible way to run blink rejection on an Epochs object that I haven't thought of? Assuming I need to reject blinks using the Raw object, here are more details: I don't have a separate EOG channel, so I'm using a forehead electrode (Fp1) that seems to reflect the blinks quite well (based on visual inspection of raw.plot()). But the movement artifacts are much bigger than the blinks, so ICA is not working very well at catching the actual blinks; in a ~70 minute recording it is detecting as few as 12 "blinks" for some subjects, and those events are localized around what are clearly movement artifacts or other large between-block drifts. Question 2: Other than manually zeroing out values in raw.\_data, is there a way to mask or delete time spans of the Raw object that you want ICA to ignore? I know about raw.crop() but that only works for beginning/end times. Question 3: is there some other approach I'm not thinking of that might get around this problem? -------------- next part -------------- An HTML attachment was scrubbed... URL: [http://mail.nmr.mgh.harvard.edu/pipermail/mne\_analysis/attachments/20170313/535e5e0a/attachment.html](http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20170313/535e5e0a/attachment.html) ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [March 14, 2017, 1:57am UTC](https://mne.discourse.group/t/preprocessing-ica-movement-artifacts-vs-blinks/1193/2 "2017-03-14T01:57:00Z")
Hi Dan It might be helpful if you were a little more clear/specific about what you're currently doing, so we can help suggest things you haven't thought of 🙂 That said, I'm guessing you're using ica.find\_bads\_eog and ica.plot\_scores? I've been working on adapting my lab's EEGLAB workflows to MNE over the past few months and my experience is that this routine works a lot better for MEG than EEG data. For EEG (using various systems and channel counts), this approach is pretty unreliable. In my experience ica.find\_bads\_eog at the default threshold (3.0) often misses finding any blinks, and if you drop the threshold down you tend to get false positives. I haven't found a threshold that works reliably across subjects/datasets. If anyone has had better success I'd love to hear about it though! My approach is to fit ICA on the epochs as I want to analyze them, and use ica.plot\_properties to inspect every component and manually identify the EOG-related components. This is usually very easy to do based on scalp distribution, variance, and the epochs raster plots (and easy to teach undergrads to reliably identify as well). I've heard many people say they like to fit ICA on longer epochs than they ultimately plan on analyzing. In principle, more data = more opportunities to train ICA well. However, I've never done this as I've always been satisfied with the results I get using a "normal" epoch length and manual inspection. Fitting ICA on raw, continuous data - especially when there are lots of very noisy periods such as breaks - is unlikely to work well because ICA will just be trying to fit all the huge variance in your data and so will - as you seem to be seeing - will tend to miss the blinks. There's also a danger that real (brain) data will be lost. Best regards, Aaron ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [March 14, 2017, 6:25am UTC](https://mne.discourse.group/t/preprocessing-ica-movement-artifacts-vs-blinks/1193/3 "2017-03-14T06:25:12Z")
Hi Dan, in the dev-version of MNE-python it is possible to use annotations \<[https://mne-tools.github.io/dev/generated/mne.Annotations.html](https://mne-tools.github.io/dev/generated/mne.Annotations.html)\> to omit bad segments of data when fitting ICA \<[https://mne-tools.github.io/dev/generated/mne.preprocessing.ICA.html?mne.preprocessing.ICA.fit#mne.preprocessing.ICA.fit](https://mne-tools.github.io/dev/generated/mne.preprocessing.ICA.html?mne.preprocessing.ICA.fit#mne.preprocessing.ICA.fit)\> by using reject\_by\_annotation and annotation descriptions starting with the keyword 'bad'. It is also possible to annotate the data interactively with raw.plot by pressing 'a'. Would be nice to hear if this works for you. -Jaakko ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [March 14, 2017, 2:18pm UTC](https://mne.discourse.group/t/preprocessing-ica-movement-artifacts-vs-blinks/1193/4 "2017-03-14T14:18:15Z")
> My approach is to fit ICA on the epochs as I want to analyze them, and > use ica.plot\_properties to inspect every component and manually identify > the EOG-related components. This is usually very easy to do based on scalp > distribution, variance, and the epochs raster plots (and easy to teach > undergrads to reliably identify as well). Assuming the movement artifacts are much bigger than the blinks, I wonder if this suggestion be adapted to this use case with something like: 1. Use `make_fixed_length_events` to make regularly-spaced events of some duration (not sure if e.g., 1 sec or 10 sec would work better) 2. Create Epochs using a rejection threshold that throws away epochs based on movement artifacts, but not the blink artifacts. 3. Run ICA on the Epochs. Eric -------------- next part -------------- An HTML attachment was scrubbed... URL: [http://mail.nmr.mgh.harvard.edu/pipermail/mne\_analysis/attachments/20170314/f7b3e669/attachment.html](http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20170314/f7b3e669/attachment.html) ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [March 15, 2017, 7:58am UTC](https://mne.discourse.group/t/preprocessing-ica-movement-artifacts-vs-blinks/1193/5 "2017-03-15T07:58:40Z")
one more thing. If the movement artifacts can get mapped to strong peak-to-peak amplitudes the reject param in ICA can certainly help automatically excluding these segments from the ICA fit. Alex ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [March 16, 2017, 1:10am UTC](https://mne.discourse.group/t/preprocessing-ica-movement-artifacts-vs-blinks/1193/6 "2017-03-16T01:10:08Z")
Thanks all, for the suggestions.? Annotations seemed like the right way to go, since I know the block start/end times already.? [Jaakko, I can report that annotations are working for me (both adding programmatically via `raw.annotations = mne.Annotations(...)` and also interactively.]? After annotating the between-block times as "bad-between-blocks", `ica.plot_components` looks good: 3 components that make sense for my experiment, and one on the forehead/face (the blinks).? Unfortunately neither `find_eog_events` nor `create_eog_epochs` seems to respect annotations (yet?).? So although I can ignore the between-blocks times when fitting the ICA, it doesn't seem to help with the next step of `ica.find_bads_eog`.? Please let me know if I'm misunderstanding something about this process; otherwise I'll report back after I've had a chance to try some of the other suggestions.