When two concurrent sensorimotor tasks have to be performed at a short time interval, the second response is generally delayed at a central decision stage. However, in patients who have undergone full or partial transection of forebrain fibers connecting the two hemispheres (split-brain), independent structures subserving all processing stages should reside in each disconnected hemisphere, thus predicting parallel processing of dual tasks. Surprisingly, this prediction is usually not verified behaviorally. We reasoned that brain imaging with high-density recordings of event-related potentials (ERPs) could clarify the extent and limits of parallel processing in callosal patients. We studied a patient (AC) with posterior callosal section in a lateralized number-comparison task. Behaviorally, the split-brain patient showed robust dual-task interference, superficially similar to the psychological refractory period (PRP) effect in the control group of 14 healthy subjects, but significantly different in important aspects such as slowing of response times in the first task. Analysis of ERPs revealed that the parietal P3 component became split into distinct contralateral components in the patient, and was dramatically reduced for targets in his left visual field. In contrast to the control group, P3 latencies showed minimal to nonexistent postponement related to dual-task processing in the patient. In summary, our findings suggest that the left and right hemisphere networks normally involved in a single distributed "global neuronal workspace" that underlies the generation of the P3 component and serial processing, became strongly decoupled after a posterior callosal lesion.
Pubmed ID: 22542264 RIS Download
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Software tool for analysis of MEG, EEG, and other electrophysiological data. Used by experimental neuroscientists.
View all literature mentionsInteractive 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
View all literature mentionsA completely automatic algorithm for artifact identification and removal in EEG data. ADJUST is based on Independent Component Analysis (ICA), a successful but unsupervised method for isolating artifacts from EEG recordings. ADJUST identifies artifacted ICA components by combining stereotyped artifact-specific spatial and temporal features. Features are optimised to capture blinks, eye movements and generic discontinuities. Once artifacted IC are identified, they can be simply removed from the data while leaving the activity due to neural sources almost unaffected.
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