Pre-processing pipeline for low density EEG system

The attached code proposes one prospective preprocessing pipeline that helps handle artifacts in EEG data collected using the Muse system. The text contains some additional preprocessing steps which I typically use when working with denser EEG systems but some of these steps are not applicable to Muse. The code will focus on the sections without an asterisk.

0. Load the required libraries
1. Load the EEG, chanlocs into EEGlab format
   ii) Downsample if needed
   iii) High Pass filter (0.1-2 Hz: Depending on noise level & study)

iv) *** H-infinity adaptive ocular artifact removal *** (Note: not applicable for Muse and not included)

v) Low pass filter (depending on the study)
vi) Remove Bad segments (Visually inspecting)
vii) Remove Bad channels (Visual Inspection with Muse)
Viii) Remove burst artifacts using ASR

ix) *** ICA cleaning *** (Note: not applicable for Muse and not included)
x) *** Interpolating removed channels *** (Note: not applicable for Muse and not included)

Additional Commands
xi) Notch filter (depending on the study)
xii) Common Average Reference (Note: After ICA or add averaged channel if doing before ICA to account for rank deficiency)
xiii) Multiple plots for sanity checks

Pre_processing_tutorial.zip

Start with the attached Pre_processing_tutorial.zip and map the non-asterisk steps—loading EEG and chanlocs, filtering, bad-segment and channel review, ASR, and sanity plots—to the repository’s Python entry points. Done means a runnable Muse-focused preprocessing pipeline that excludes the marked non-applicable steps and covers the listed optional operations.