EEGLAB signal processing environment is currently the leading open-source software for processing electroencephalographic (EEG) data. The Neuroscience Gateway (NSG, nsgportal.org) is a web and API-based portal allowing users to easily run a variety of neuroscience-related software on high-performance computing (HPC) resources in the U.S. XSEDE network. We have reported recently (Delorme et al., 2019) on the Open EEGLAB Portal expansion of the free NSG services to allow the neuroscience community to build and run MATLAB pipelines using the EEGLAB tool environment. We are now releasing an EEGLAB plug-in, nsgportal, that interfaces EEGLAB with NSG directly from within EEGLAB running on MATLAB on any personal lab computer. The plug-in features a flexible MATLAB graphical user interface (GUI) that allows users to easily submit, interact with, and manage NSG jobs, and to retrieve and examine their results. Command line nsgportal tools supporting these GUI functionalities allow EEGLAB users and plug-in tool developers to build largely automated functions and workflows that include optional NSG job submission and processing. Here we present details on nsgportal implementation and documentation, provide user tutorials on example applications, and show sample test results comparing computation times using HPC versus laptop processing.

Modulation of the amplitude of high-frequency cortical field activity locked to changes in the phase of a slower brain rhythm is known as phase-amplitude coupling (PAC). The study of this phenomenon has been gaining traction in neuroscience because of several reports on its appearance in normal and pathological brain processes in humans as well as across different mammalian species. This has led to the suggestion that PAC may be an intrinsic brain process that facilitates brain inter-area communication across different spatiotemporal scales. Several methods have been proposed to measure the PAC process, but few of these enable detailed study of its time course. It appears that no studies have reported details of PAC dynamics including its possible directional delay characteristic. Here, we study and characterize the use of a novel information theoretic measure that may address this limitation: local transfer entropy. We use both simulated and actual intracranial electroencephalographic data. In both cases, we observe initial indications that local transfer entropy can be used to detect the onset and offset of modulation process periods revealed by mutual information estimated phase-amplitude coupling (MIPAC). We review our results in the context of current theories about PAC in brain electrical activity, and discuss technical issues that must be addressed to see local transfer entropy more widely applied to PAC analysis. The current work sets the foundations for further use of local transfer entropy for estimating PAC process dynamics, and extends and complements our previous work on using local mutual information to compute PAC (MIPAC).