[Eeglablist] Slow cortical potential analysis-preprocessing pipeline

[Rafał Jończyk]

Dear eeglab(list) users,

I’m currently analysing a 64-channel dataset looking at a slow cortical
potential – Stimulus Preceding Negativity (each epoch lasts 3800 ms). I'm
using eeglab and erplab. I’ve been thinking which processing steps would be
best to get the a reliable dataset, i.e., representative of what’s really
going on without unintentionally getting an effect due to incorrect
preprocessing analysis.

Below I present the pipeline I’ve been experimenting with so far. Steps
marked with „*” are done in erplab. I would really appreciate your
feedback, because I’m still relatively new to EEG analysis. Also, I shortly
discuss the issue of channel interpolation/ICA below the pipeline.

1. Re-sample from 1000Hz to 500Hz

2. Edit channel locations + append Cz + re-reference to Cz

3. Filter: lowpass, cut-off at 20Hz, order 2 (12 dB/Octave)*

4. Epoch [-0.105 3.805]

5. Automatic Epoch Rejection (based on probability) + rejection based on
visual inspection

6. ICA (runica)

7. Removing ICA components: only components related to eye movements +
visual inspection

8. Convert epochs to a continuous dataset*

9. Creating event lists & assigning bins*

10. Epoching: [-100 3800]*

11. Baseline removal [-100 0]*

12. Artefact rejection (peak-to-peak moving window)*

13. Averaging + visual inspection*

14. Global average*

##Comment concerning Step 2: I assume it would be better to remove bad
channels after re-sampling, then re-reference to Cz with bad channels
removed, and then interpolate bad channels from previous dataset (one with
bad channels included) so as to avoid puting noise into the average. This
will be followed by filtering in step 3. The problem I have, however, is
that ICA is not advised to be run on interpolated channels. Is there a
possible compromise/solution to this issue?

I would be very eager to follow the preprocessing and data cleaning advice
suggested in Mokoto’s pipeline, PREP pipeline, or CleanLine plugin more
closely, but their approach is largely based on high-pass filtering which
would eliminate the slow drifts I’m looking at.

Thank you for your insights!
Best,
Rafal

-- 
Rafał Jończyk
Assistant Professor
Faculty of English
Adam Mickiewicz University, Poznań | Poland
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