[Eeglablist] Poor ICA Decomposition/Strange Scalp Maps

Cedric Cannard ccannard at protonmail.com
Tue Jul 20 08:49:58 PDT 2021


Hi Casey,

> 2.  Re-Reference to Mastoid channels
Average referencing would be much better, especially on 128-channel data.

> 3.  Filter: highpass .03Hz, low pass 40Hz
ICA works much better with highpass ~1 Hz
If you really need to keep low frequencies. You can run ICA and reject your bad components on data highpassed at 1 Hz, and transfer them (EEG.icaweights, EEG.icasphere, EEG.icawinv) to your original data highpassed at 0.03 Hz.

> 5.  Identify channels to interpolate and epochs to reject
Either use the pca option with the reduced rank in input when running ICA (not recommended), or interpolate rejected channels after ICA and rejection of bad ICs.Each interpolated channel reduces data rank, which reduces ICA performance.

Also, you didn't mention data cleaning. ICA performs better after cleaning major artifacts first (e.g., manually or with ASR).

Hope this helps,

Cedric


‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐

On Monday, July 19th, 2021 at 12:32 PM, Nicastri, Casey via eeglablist <eeglablist at sccn.ucsd.edu> wrote:

> Hi all,
>
> I am wondering if anyone can help give me some more information on a possible reason my ICA decomposition is so poor. A picture of the first 35 scalp maps is linked below. This data was collected on a 128 channel system (BioSemi/ActiView System)
>
> Current Pipeline:
>
> 1.  Import, resample at 256Hz (collected at 512)
> 2.  Re-Reference to Mastoid channels
> 3.  Filter: highpass .03Hz, low pass 40Hz
> 4.  Epoch to 1.2s epochs
> 5.  Identify channels to interpolate and epochs to reject
> 6.  Runica, component rejection
>
>     Picture: https://urldefense.proofpoint.com/v2/url?u=https-3A__www.dropbox.com_s_h7y0r2dshcta2cu_Screen-2520Shot-25202021-2D07-2D14-2520at-252011.13.30-2520AM.png-3Fdl-3D0&d=DwIGaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=UKJhquGPgJ-e9o2GfVHIU_WgNx1T9I_aX4zuqcDdQJE&s=2G80tUh3e0z2iW3K4iJipbqd6-0lGV4WYGNBSlG2U6Q&e=
>
>     Are there any large errors here? I’ve never seen components like this. The data is a bit noisy but not worse than other data previously collected on this system. Any ideas?
>
>     Thanks!
>
>     Casey
>
>     --
>
>     Casey Nicastri
>
>     Clinical Research Assistant
>
>     Division of Cognitive and Behavioral Neurology
>
>     Center for Brain/Mind Medicine, Brigham and Women’s Hospital, Harvard Medical School
>
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