[Eeglablist] EEGLAB Preprocessing with High Density electrodes and issues in IC Labelling

[Naviya Lall]

Hello,

My name is Naviya and I am a Junior Research Fellow in Delhi, India.

At our lab we collect EEG data with students from our university. Initially
we used a 32 channel system and EEG preprocessing was convenient and
straightforward, including Independent Components (IC) labelling. However,
we recently started using a 160-channel system, particularly 155
electrodes. Here the IC Label tool/function began to struggle a bit
and a *majority
of the components are labelled as "Other" with very few being labelled as
Eye, Heart, Channel noise or Brain*. I request your feedback and help in
trying to understand why this could be happening and if my methods for
cleaning data have been effective. I have attached a few screenshots of
different plots and steps from when I was working on* Eye closed - Resting
State data, recorded for 5 minutes at 500Hz. All impedance was below 25Hz
when we recorded this. *

I have read Dr. Delorme and Dr. Makeig's documentation for preprocessing
and finalised this pipeline. These are the steps that I followed-

   1. Channel locations already present from BrainVision recorder
   2. Downsampling to 250Hz
   3. Adding a Notch filter at 48 to 52Hz.
   4. Bandpass filter between 0.1 and 50Hz (allowing these frequencies to
   pass)
   5. Epoching to create 2 second long segments of Resting state
   6. Re-referencing the data to Average reference
   7. ICA followed by removing Eye, heart, channel noise components

*A few specific questions-*

   1. With the 32 channel data, we had a task based experiment and I had
   used the same pipeline I have described above, *is there a different
   pipeline for preprocessing Resting State data? *I attempted
   preprocessing continuous data but the ICAs looked similar, if not worse.
   2. One of the graphs that appears on the bottom left when IC label is
   used shows the component across time for each epoch. I had many components
   with a dark line across any one epoch (refer image -
   10_Single_component.png) and this was not uniform in trial/epoch across
   components. I could not figure out what this meant exactly
   3. Since I have 150+ channels, I end up with 150+ components, is that
   the right way of running ICA or *should the number of components be
   less?* And how many of those 155 components actually matter? Do I need
   to go through each and every one in detail? *So far, I have. *

I would be very grateful if anyone can help me figure this out and come up
with the best pipeline for preprocessing. I can also share our RAW resting
state EEG data with you if that would help.

Thank you for your time.

Best regards,
Naviya

--
Naviya Lall
Junior Research Fellow
Cognitive Science Lab
IIIT Delhi
naviyalalluni.wixsite.com <https://urldefense.com/v3/__https://naviyalalluni.wixsite.com/naviyalall__;!!Mih3wA!FT2n5_CMINbUXGerS6qz4y_upfBU-l96eIBOnRDtVe243I5ifTqI3uKWyKBtR0gS3YnNirKKi66z1EJXJejiv5yt$ >