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