The event-related potential (ERP) technique in cognitive neuroscience allows scientists to observe human brain activity that reflects specific cognitive processes. In An Introduction to the Event-Related Potential Technique, Steve Luck offers the first comprehensive guide to the practicalities of conducting ERP experiments in cognitive neuroscience and related fields, including affective neuroscience and experimental psychopathology. The book can serve as a guide for the classroom or the laboratory and as a reference for researchers who do not conduct ERP studies themselves but need to understand and evaluate ERP experiments in the literature. It summarizes the accumulated body of ERP theory and practice, providing detailed, practical advice about how to design, conduct, and interpret ERP experiments, and presents the theoretical background needed to understand why an experiment is carried out in a particular way. Luck focuses on the most fundamental techniques, describing them as they are used in many of the world's leading ERP laboratories. These techniques reflect a long history of electrophysiological recordings and provide an excellent foundation for more advanced approaches. The book also provides advice on the key topic of how to design ERP experiments so that they will be useful in answering questions of broad scientific interest. This reflects the increasing proportion of ERP research that focuses on these broader questions rather than the "ERPology" of early studies, which concentrated primarily on ERP components and methods. Topics covered include the neural origins of ERPs, signal averaging, artifact rejection and correction, filtering, measurement and analysis, localization, and the practicalities of setting up the lab.

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An Introduction to the Event-Related Potential Technique

Directional data is ubiquitious in science. Due to its circular nature such data cannot be analyzed with commonly used statistical techniques. Despite the rapid development of specialized methods for directional statistics over the last fifty years, there is only little software available that makes such methods easy to use for practioners. Most importantly, one of the most commonly used programming languages in biosciences, MATLAB, is currently not supporting directional statistics. To remedy this situation, we have implemented the CircStat toolbox for MATLAB which provides methods for the descriptive and inferential statistical analysis of directional data. We cover the statistical background of the available methods and describe how to apply them to data. Finally, we analyze a dataset from neurophysiology to demonstrate the capabilities of the CircStat toolbox.

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CircStat: A Matlab Toolbox for Circular Statistics

https://csea.phhp.ufl.edu/Publications/Old/Cuthbert2000.pdf

Brain potentials in affective picture processing: Covariation with autonomic arousal and affective report

The anterior cingulate cortex (ACC) has been shown to respond to conflict between simultaneously active, incompatible response tendencies. This area is active during high-conflict correct trials and also when participants make errors. Here, we use the temporal resolution of high-density event-related potentials (ERPs) in combination with source localization to investigate the timing of ACC activity during conflict and error detection. We predicted that the same area of the ACC is active prior to high-conflict correct responses and following erroneous responses. Dipole modeling supported this prediction: The frontocentral N2, occurring prior to the response on correct conflict trials, and the ERN, occurring immediately following error responses, could both be modeled as having a generator in the caudal ACC, suggesting the same process to underlie both peaks. Modeling further suggested that the rostral area of the ACC was also active following errors, but later in time, contributing to the error positivity (PE), and peaking at 200-250 msec following the ERN peak. Despite the inherent limitations of source localization, these data may begin to shed light on the timing of action-monitoring processes. First, the time course of caudal ACC activity follows the time course as predicted by the conflict theory of this region. Second, caudal ACC activity might be temporally dissociated from rostral ACC activity during error trials, which possibly reflects a separate, affective component of the evaluative functions of the ACC.

The Timing of Action-Monitoring Processes in the Anterior Cingulate Cortex

Gehring, Goss, Coles, Meyer, and Donchin (1993) have reported electrophysiological evidence for a brain mechanism dedicated to monitoring performance and compensating for errors (see also Falkenstein, Hohnsbein, Hoormann, & Blanke, 1990). They have described a component of the human event-related potential, called the error-related negativity (ERN), which is characterized by a negative peak about 100 ms following the onset of electromyographic (EMG) activity when the subject is in error on that trial. The amplitude of the ERN is larger when the subject strives for response accuracy than when the subject concentrates on speed. It is also correlated with several error-compensation variables. The larger the ERN, the smaller the force with which the erroneous key is pressed, the higher the probability of correcting the error by immediately depressing the other key, and the slower the reaction time on the next trial. These correlations suggest that the neural system whose activity is reflected by the ERN is involved in the active inhibition and correction of an error as soon as it is detected. Gehring et aI. (1993) noted that the data from their study did not allow for the localization of this neural system in the brain. They recorded from five electrode sites and observed only that the ERN was largest over the front and middle of the scalp. They did, however, offer some speculation, based on animal evidence, pointing toward \"a system involving the anterior cingulate cortex and supplementary motor areas\" (p. 389). Our own data allow for a direct confirmation of this localization in humans. We recorded high-density event-related potentials using a 64-channel geodesic electrode net (Tucker, 1993), with an interelectrode spacing of about 4 cm. This methodology enabled us to fully characterize the scalp topography of the ERN in two different experiments. In the first experiment (Dehaene, 1994), 12 subjects were presented on each trial with a single Arabic or spelled-out numeral. They had to press one key with one hand if the target was larger than 5 and another key with the other hand if it was smaller than 5. In the second experiment (Dehaene, unpublished data), 12 other subjects were presented with lists of words, which they had to classify as belonging or not to a target semantic category (e.g., an-

Localization of a Neural System for Error Detection and Compensation

http://www.ccs.fau.edu/%7Ebressler/EEGCCSBS/ferree2001.pdf

Scalp electrode impedance, infection risk, and EEG data quality

Psychological studies with reaction time methodology show that there is meaningful variability in the performance of cognitive operations when responses are measured in milliseconds. Temporal precision is also required to reveal the rapid neurophysiological events in cortical networks. Sampling the brain's electrical activity at the scalp surface characterizes regional brain function with millisecond temporal resolution. The problem with electroencephalographic (EEG) data is localizing the areas of the cortex that generate the observed scalp fields. Although the eventual goal will be to specify the neural generators of the EEG, we propose that an important first step for functional studies is to examine accurate, time-dynamic maps of the brain's electrical fields at the head surface. Given an adequate spatial sampling of the surface potentials, accurate electrical studies require measures that are independent of the location of the reference sensor. The 2D Laplacian of the potential field may be used to define the local features of the scalp current flow. Because the electrical fields are dynamic, brain mapping with electrical data requires animations rather than static images. © 1994 Wiley-Liss, Inc.

Spatiotemporal analysis of brain electrical fields

Geodesic photogrammetry for localizing sensor positions in dense-array EEG

Rao's Spacing Test is a useful and powerful statistic for testing uniformity with circular and periodic data. Previously published tables of critical values are incomplete, giving critical values only out to p=.l and n = 200. We extend those tables to n=1000 and for p-values from .001 out to 0.9. The extension will enable researchers to better evaluate the confidence with which they are accepting the null hypothesis, and to use the test with larger n

An expanded table of probability values for rao's spacing test

A method and apparatus for measuring the location of objects arranged on a convex surface. A plurality of cameras is arranged in a stationary array about the surface, and is used to capture images of the objects. A parameterized model of the surface is developed using optimization techniques, and then the locations of the objects are established by triangulation. The method is applied to location of electrophysiological sensors on the head, for purposes of electroencephalographic source analysis, or for registration with other imaging modalities such as MRI (magnetic resonance imaging).

Gerald S. Russell

Papers

Scalp electrode impedance, infection risk, and EEG data quality

Spatiotemporal analysis of brain electrical fields

Geodesic photogrammetry for localizing sensor positions in dense-array EEG

An expanded table of probability values for rao's spacing test

Measuring the location of objects arranged on a surface, using multi-camera photogrammetry