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Understanding the neural underpinning of conscious perception remains one of the primary challenges of cognitive neuroscience. Theories based mostly on studies of the visual system differ according to whether the neural activity giving rise to conscious perception occurs in modality-specific sensory cortex or in associative areas, such as the frontal and parietal cortices. Here, we search for modality-specific conscious processing in the auditory cortex using a bistable stream segregation paradigm that presents a constant stimulus without the confounding influence of physical changes to sound properties. ABA_ triplets (i.e., alternating low, A, and high, B, tones, and _ gap) with a 700 ms silent response period after every third triplet were presented repeatedly, and human participants reported nearly equivalent proportions of 1- and 2-stream percepts. The pattern of behavioral responses was consistent with previous studies of visual and auditory bistable perception. The intermittent response paradigm has the benefit of evoking spontaneous perceptual switches that can be attributed to a well-defined stimulus event, enabling precise identification of the timing of perception-related neural events with event-related potentials (ERPs). Significantly more negative ERPs were observed for 2-streams compared to 1-stream, and for switches compared to non-switches during the sustained potential (500-1000 ms post-stimulus onset). Further analyses revealed that the negativity associated with switching was independent of switch direction, suggesting that spontaneous changes in perception have a unique neural signature separate from the observation that 2-stream percepts evoke more negative ERPs than 1-stream. Source analysis of the sustained potential showed activity associated with these differences originating in anterior superior temporal gyrus, indicating involvement of the ventral auditory pathway that is important for processing auditory objects.
Pubmed ID: 31546046
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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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