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

• Published in: Journal of neuroscience methods Vol. 250; pp. 3 - 6
• Main Authors: Delorme, Arnaud, Miyakoshi, Makoto, Jung, Tzyy-Ping, Makeig, Scott
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier 30.07.2015
• Subjects: Adult; Attention - physiology; Brain - physiology; Cognitive science; Data Interpretation, Statistical; Electroencephalography - methods; Evoked Potentials; Humans; Middle Aged; Neuropsychological Tests; Neuroscience; Signal Processing, Computer-Assisted; Visual Perception - physiology; ERP-image; ERP; Single-trial; Event related potential; EEG
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2014.10.003

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. 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. 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.