Learning Objectives

This chapter covers electroencephalography (EEG), a technique for non-invasively measuring electrical activity from electrodes placed on the scalp.

By the end of this chapter, you should be able to:

• Explain the neural origins of EEG data

• Explain, in basic terms, how EEG is recorded

• Explain the difference between time- and frequency-domain treatments of EEG data

• Explain the rationale for fundamental EEG data preprocessing operations, including:

  • filtering

  • event code processing

  • segmentation (epoching)

  • artifact removal

  • averaging

• Describe the purpose of the MNE-Python software package

• Use MNE-Python to perform the above EEG preprocessing steps

• Use MNE-Python to visualize EEG data in the time and frequency domains, including

  • waveform plots of one or more electrodes

  • scalp topography plots

  • frequency spectra

  • time-frequency plots

• Convert processed EEG data from MNE-Python to a pandas DataFrame

• Plot averaged EEG data using Seaborn