Arnaud Delorme1, Makoto Miyakoshi2, Tzyy-Ping Jung2, Scott Makeig2. 1. 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. 2. Swartz Center of Computational Neuroscience, Institute of Neural Computation, University of California San Diego, La Jolla, San Diego, CA 92093-0559, USA.
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.
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.
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