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Facial emotions express our internal states and are fundamental in social interactions. Here we explore whether the repetition of unattended facial emotions builds up a predictive representation of frequently encountered emotions in the visual system. Participants (n=24) were presented peripherally with facial stimuli expressing emotions while they performed a visual detection task presented in the center of the visual field. Facial stimuli consisted of four faces of different identity, but expressed the same emotion (happy or fearful). Facial stimuli were presented in blocks of oddball sequence (standard emotion: p=0.9, deviant emotion: p=0.1). Event-related potentials (ERPs) to the same emotions were compared when the emotions were deviant and standard, respectively. We found visual mismatch negativity (vMMN) responses to unattended deviant emotions in the 170-360 ms post-stimulus range over bilateral occipito-temporal sites. Our results demonstrate that information about the emotional content of unattended faces presented at the periphery of the visual field is rapidly processed and stored in a predictive memory representation by the visual system. We also found evidence that differential processing of deviant fearful faces starts already at 70-120 ms after stimulus onset. This finding shows a 'negativity bias' under unattended conditions. Differential processing of fearful deviants were more pronounced in the right hemisphere in the 195-275 ms and 360-390 ms intervals, whereas processing of happy deviants evoked larger differential response in the left hemisphere in the 360-390 ms range, indicating differential hemispheric specialization for automatic processing of positive and negative affect.
Pubmed ID: 22037000
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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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