title: Grand average ERP-image plotting and statistics: A method for comparing variability in event-related single-trial EEG activities across subjects and conditions.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
authors.affiliation: Swartz Center of Computational Neuroscience, Institute of Neural Computation, University of California San Diego, La Jolla, San Diego, CA 92093-0559, USA; Institute of Noetic Sciences, Petaluma, CA, USA; CerCo, CNRS UMR5549, Toulouse, France; Centre de Recherche Cerveau et Cognition, Université de Toulouse III, UPS, Toulouse, France. Electronic address: arno@cerco.ups-tlse.fr.
authors.forename: Arnaud
authors.initials: A
authors.lastname: Delorme
authors.affiliation: Swartz Center of Computational Neuroscience, Institute of Neural Computation, University of California San Diego, La Jolla, San Diego, CA 92093-0559, USA.
authors.forename: Makoto
authors.initials: M
authors.lastname: Miyakoshi
authors.affiliation: Swartz Center of Computational Neuroscience, Institute of Neural Computation, University of California San Diego, La Jolla, San Diego, CA 92093-0559, USA.
authors.forename: Tzyy-Ping
authors.initials: TP
authors.lastname: Jung
authors.affiliation: Swartz Center of Computational Neuroscience, Institute of Neural Computation, University of California San Diego, La Jolla, San Diego, CA 92093-0559, USA.
authors.forename: Scott
authors.initials: S
authors.lastname: Makeig
mesh_terms: D000328:Adult; D001288:Attention / Q000502:physiology; D001921:Brain / Q000502:physiology*; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials; D006801:Humans; D008875:Middle Aged; D009483:Neuropsychological Tests; D012815:Signal Processing, Computer-Assisted; D014796:Visual Perception / Q000502:physiology
keywords: EEG; ERP; ERP-image; Event related potential; Single-trial