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The three phases of the applied acquired equivalence learning test, i.e. acquisition, retrieval and generalization, investigate the capabilities of humans in associative learning, working memory load and rule-transfer, respectively. Earlier findings denoted the role of different subcortical structures and cortical regions in the visual test. However, there is a lack of information about how multimodal cues modify the EEG-patterns during acquired equivalence learning. To test this we have recorded EEG from 18 healthy volunteers and analyzed the power spectra and the strength of cross-frequency coupling, comparing a unimodal visual-guided and a bimodal, audio-visual-guided paradigm. We found that the changes in the power of the different frequency band oscillations were more critical during the visual paradigm and they showed less synchronized activation compared to the audio-visual paradigm. These findings indicate that multimodal cues require less prominent, but more synchronized cortical contribution, which might be a possible biomarker of forming multimodal associations.
Pubmed ID: 31263168
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Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
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 mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 1,2023. Analytics platform with various sub platforms, each with specific performance capabilities for tasks such as data analysis, data management, data visualization, and data mining procedures.
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