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Auditory deprivation is known to be accompanied by alterations in visual processing. Yet not much is known about tactile processing and the interplay of the intact sensory modalities in the deaf. We presented visual, tactile, and visuo-tactile stimuli to congenitally deaf and hearing individuals in a speeded detection task. Analyses of multisensory responses showed a redundant signals effect that was attributable to a coactivation mechanism in both groups, although the redundancy gain was less in the deaf. In line with these behavioral results, on a neural level, there were multisensory interactions in both groups that were again weaker in the deaf. In hearing but not deaf participants, somatosensory event-related potential N200 latencies were modulated by simultaneous visual stimulation. A comparison of unisensory responses between groups revealed larger N200 amplitudes for visual and shorter N200 latencies for tactile stimuli in the deaf. Furthermore, P300 amplitudes were also larger in the deaf. This group difference was significant for tactile and approached significance for visual targets. The differences in visual and tactile processing between deaf and hearing participants, however, were not reflected in behavior. Both the behavioral and electroencephalography (EEG) results suggest more pronounced multisensory interaction in hearing than in deaf individuals. Visuo-tactile enhancements could not be explained by perceptual deficiency, but could be partly attributable to inverse effectiveness.
Pubmed ID: 25653602
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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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