Grand average ERP-image plotting and statistics: A method for comparing variability in event-related single-trial EEG activities across subjects and conditions

J Neurosci Methods. 2015 Jul 30;250:3-6. doi: 10.1016/j.jneumeth.2014.10.003. Epub 2014 Oct 22.

Abstract

Backgroud: With the advent of modern computing methods, modeling trial-to-trial variability in biophysical recordings including electroencephalography (EEG) has become of increasingly interest. Yet no widely used method exists for comparing variability in ordered collections of single-trial data epochs across conditions and subjects.

New method: We have developed a method based on an ERP-image visualization tool in which potential, spectral power, or some other measure at each time point in a set of event-related single-trial data epochs are represented as color coded horizontal lines that are then stacked to form a 2-D colored image. Moving-window smoothing across trial epochs can make otherwise hidden event-related features in the data more perceptible. Stacking trials in different orders, for example ordered by subject reaction time, by context-related information such as inter-stimulus interval, or some other characteristic of the data (e.g., latency-window mean power or phase of some EEG source) can reveal aspects of the multifold complexities of trial-to-trial EEG data variability.

Results: This study demonstrates new methods for computing and visualizing 'grand' ERP-image plots across subjects and for performing robust statistical testing on the resulting images. These methods have been implemented and made freely available in the EEGLAB signal-processing environment that we maintain and distribute.

Keywords: EEG; ERP; ERP-image; Event related potential; Single-trial.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Attention / physiology
  • Brain / physiology*
  • Data Interpretation, Statistical
  • Electroencephalography / methods*
  • Evoked Potentials*
  • Humans
  • Middle Aged
  • Neuropsychological Tests
  • Signal Processing, Computer-Assisted
  • Visual Perception / physiology