Electroencephalogram (EEG) studies the electrical activity of the brain produced by large segments of neurons firing in tandem. EEG is particularly well-suited to studies that examine functional or effective connectivity and research where temporal resolution is important (da Silva, 2013).
Fluctuations in the voltage of ionic currents produced by neurons are recorded through electrodes (St. Louis & Frey, 2016). Electrodes are the physical sensors or transducers that are placed on the scalp to perform the analogue recording. They are connected to amplifiers, which not only amplify, but also filter the EEG activity. EEG data is represented by channels for each electrode, with voltages represented on the x-axis, and time represented on the y-axis. Event codes (which signal at what point periods of interest in an experiment occur) may be found at the bottom of the data graph along the y-axis (Acheson, 2019).
Although EEG has a far higher temporally resolution than fMRI, what it gains in temporal recording, it loses in spatial accuracy. This is because in order to be read on the scalp, the electrical activity must first travel through ‘biological filters’, i.e. the meninges, the skull and skin. This results in spread and reduced amplification (St. Louis & Frey, 2016). Some research has combined EEG and fMRI to great effect, but detailed elaboration on data management for studies combining the two modalities is beyond the scope of this primer. Instead we direct you to the Human Time Data fMRI data handbook for guidance on the management of this type of data. This particular guide will instead focus on data collection and management issues specific to EEG and aims to be particularly useful to the new beginner. For an in-depth overview of how to create a data management plan for your study, please see the Human Time Data guide Data Management: DMPs and Best Practices