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Cognitive science has regarded an individual's face as a form of representative stimuli to engage self-representation. The domain-generality of self-representation has been assumed in several reports, but was recently refuted in a functional magnetic resonance imaging study (Sugiura et al., 2008). The general validity of this study's criticism should be tested by other measures to compensate for the limitation of the time resolution of the blood-oxygen-level-dependent (BOLD) signal. In this article, we report an EEG study on the domain-generality of visual self-representation. Domain-general self-representation was operationally defined as the self-relevance common to one's own Face and Cup; three levels of familiarity, Self, Familiar, and Unfamiliar, were prepared for each. There was another condition, Visual Field, that manipulated visual hemifield during stimulus presentation, but it was collapsed because it produced no interaction with stimulus familiarity. Our results confirmed comparable phase resetting in both domains in response to familiarity manipulation, which occurred within the medial frontal area during 270-390 ms poststimulus and in the theta band. However, self-specific dissociation was observed only for Face. The results here support the conclusion that visual self-representation is domain-specific and that the oscillatory responses observed suggest evidence of face-specific visual self-representation. Results also revealed an inter-trial phase coherency decrease specifically for Self-Face within the right fusiform area during 170-290 ms poststimulus and in the alpha and theta band, suggesting reduced functional demand for Self-Face represented by sharpened networks.
Pubmed ID: 20079852
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Interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. First developed on Matlab 5.3 under Linux, EEGLAB runs on Matlab v5 and higher under Linux, Unix, Windows, and Mac OS X (Matlab 7+ recommended). EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users'' transition from GUI-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics, both at the level of individual EEGLAB ''datasets'' and/or across a collection of datasets brought together in an EEGLAB ''studyset.'' For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG data. For creative research programmers and methods developers, EEGLAB offers an extensible, open-source platform through which they can share new methods with the world research community by publishing EEGLAB ''plug-in'' functions that appear automatically in the EEGLAB menu of users who download them. For example, novel EEGLAB plug-ins might be built and released to ''pick peaks'' in ERP or time/frequency results, or to perform specialized import/export, data visualization, or inverse source modeling of EEG, MEG, and/or ECOG data. EEGLAB Features * Graphic user interface * Multiformat data importing * High-density data scrolling * Defined EEG data structure * Open source plug-in facility * Interactive plotting functions * Semi-automated artifact removal * ICA & time/frequency transforms * Many advanced plug-in toolboxes * Event & channel location handling * Forward/inverse head/source modeling
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