Lab 02

PSYC480

Harriet Grace

University of Canterbury

2024-03-26

Pipeline

EEGLab Preprocessing - Delorme

Recap

  • Referencing and filtering
  • Removing unwanted channels
  • Removing artefacts

Independent Component Analysis (ICA)

  • Why we need ICA?
  • ICA is a signal processing method to separate independent sources linearly mixed in several sensors (ICA for Dummies - By Delorme)
  • Let’s watch the video together: ICA by Delorme
  • Remove artefacts, make sure you have enough data (20 * square of number of channels), high pass filter at 0.5, remove bad channels.

Running ICA

  • Use the the Lab 2 demo dataset on Learn.
  • Use Tools -> Decompose data by ICA. Select Infomax runica.m (default), extended 1 (helps to detect line noise).
  • You can also plot all or individual component maps from the Plot menu.
  • Use Tools -> Inspect/label components by map.
  • Use Tools -> Remove components from data.

Continued

  • Or, do this automatically: Tools -> Classify components using ICLabel, then flag components as artefacts.
  • Important: DON’T remove/reject components if you are conducting group level analysis (we will learn about group level analysis later).

Exercise

To submit

  • Use the the Lab 2 exercise dataset on Learn.
  • Conduct pipeline processing as per usual (using last week’s processing too).
  • Run ICA, share a screenshot.
  • Remove components, share a screenshot.
  • Compare pre- and post-analysis attributes.
  • Compare pre- and post-analysis Channel data (scroll). What do you conclude? Any problems/issues? Why or why not? (consists 25% of today’s mark).

The END