[Eeglablist] Plotting CHannel Measures for STUDY (Brian Harvey)

Brian Harvey brian.harvey at biogen.com
Sat Sep 7 06:59:09 PDT 2019


Hi Nike

I reasoned the same thing... Currently I have been able to cluster ICs and obtain very nice result across subjects whom were recorded on different days, each with a unique set of ICA weights.

Why across session is this not supported in eeglab? I wrote A. Delorme asking this and his response was

"You can try EEGLAB 14 and this type of analysis is possible. But I would not recommend it. If you find a difference between a cluster activity across sessions, you will not know if it is because of ICA or if it is because there is a genuine difference in the cluster activity."

I therefore have move to using channel measures to make this comparison within eeglab 2019 and it is working, however only one channel at a time. I'm thinking I will have to get my fingers dirty and start writing code but hate reinventing wheels if not necessary.

Cheers

Brian Harvey
Biogen
Cambridge, MA
617.679.3195

From: Nike gnanateja
Sent: Saturday, September 7, 6:58 AM
Subject: [Eeglablist] Plotting CHannel Measures for STUDY (Brian Harvey)
To: eeglablist at sccn.ucsd.edu


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Dear Harvey and list,

I don't have a solution to this, but I have a question about this. If the

clustering works across subjects, in spite of more variability across

subjects, then shouldn't it work even better when done in the same subjects

across sessions? There would still be a high correlation of the ICs

within-subjects than across subjects. Am I missing something ?



best,



Nike



On Fri, Sep 6, 2019 at 3:00 PM <eeglablist-request at sccn.ucsd.edu> wrote:



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>    1. Plotting CHannel Measures for STUDY (Brian Harvey)

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> ---------- Forwarded message ----------

> From: Brian Harvey <brian.harvey at biogen.com>

> To: "eeglablist at sccn.ucsd.edu" <eeglablist at sccn.ucsd.edu>

> Cc:

> Bcc:

> Date: Fri, 6 Sep 2019 16:00:31 +0000

> Subject: [Eeglablist] Plotting CHannel Measures for STUDY

> Hi team!

>

> I am new to using this list and as I am using your tools more these days I

> am now thinking to reach out for some assistance, beginning with this...

>

> I am the lead data scientist for an ERP (SSVEP) study at the VA here in

> Boston, MA and currently have recordings from 10 subjects across two repeat

> sessions. Each recording has 4 different visual stimuli randomly presented

> and stored in the eeglab structure as unique events (0Hz, 15Hz, 30Hz, and

> 40Hz)

>

> When working with single session data, all ICA weights are applied within

> subject to all conditions and then I create seperate datasets from each

> event "type" afterwards. Clustering components works quite well to identify

> the steady-state evoked activity across subjects and comparisons across

> stimuli type work well (primarily against 0Hz, no flicker).

>

> The issue comes when incorporating the second session datasets

> (essentially repeats of the experiment within subject on a different day).

> There is interest on the re-test reliability. Obviously ICA weights for

> these repeat recordings are different and thus ICA clustering fails for

> timef or itc measures. Arnaud explained that comparing across sessions

> using ICA clusters is not recommended.

>

> Therefore I moved to channel measures for this... Precompute works great

> (although session 2 timef and erpimage files are written to the session 1

> subject folders and I had to move them manually to the session2 subject

> folders)

>

> Issue is when plotting these measures. When I choose a single channel for

> plooting ex. spectra looking at only session 1 vs session 2 (all events);

> as expected I get three subplots (Session 1/Session2 and t-test results).

> However if I chose multiple channels (all occipital leads) and choose to

> compare the mean of the channels for comparison it returns on one subplot.

>

> This appears to be the case for plotting scalp topography of ITC for a

> specific frequency at a specific time...

>

> Is this normal intended behavior?

>

> Thanks,

> Brian

>

>

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G Nike Gnanateja <https://urldefense.proofpoint.com/v2/url?u=http-3A__goog-5F636235333_&d=DwICAg&c=n7UHtw8cUfEZZQ61ciL2BA&r=TRx_2zPEKjt2eUqOrrQ2qy2yNPA3tLhBWOLba81oTK8&m=3sHfdUFtJ-N94JFhy9L3sSw4LR_xjYMMKQ8xZZouR8I&s=WgvzCZj9EAQn4OkMmUTNQfzrtl1PyKt6_8yR013cbrM&e= >

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