Electroencephalography (EEG), the first function brain activity imaging modality, has several natural advantages over metabolic brain imaging modalities. EEG is noninvasive, low cost, and lightweight enough to be highly mobile. Two major shifts in scientific perspective on the nature and use of human electrophysiological data are now ongoing. The first is a shift to using EEG data as a source-resolved, relatively high-resolution cortical source imaging modality. The second is a shift from recording electrophysiological data with at best a scant record of behavior (e.g., latencies of occasional button presses) to concurrently collecting and combining EEG data with other data modalities (e.g., body motion capture, eye tracking, audio and video, ECG, EMG, GSR, MEG, fMRI, etc.), paradigms that we term Mobile Brain/Body Imaging (MoBI) to capture brain activities and subject actions during natural, motivated behavior.The EEGLAB signal processing environment, an open source software project of the Swartz Center for Computational Neuroscience (SCCN) of the University of California, San Diego (UCSD), began as a set of EEG data analysis running on Matlab (The Mathworks, Inc.) released by Makeig on the World Wide Web in 1997. EEGLAB was first released from SCCN in 2001. Now nearly twenty years later, the EEGLAB reference paper (Delorme & Makeig, 2004) has over 4,100 citations (now increasing by over 3 per day), the opt-in EEGLAB discussion email list links over 5,500 researchers, the EEGLAB news list over 15,400 researchers, and a survey of 687 research respondents reported EEGLAB to be the software environment most widely used for electrophysiological data analysis in cognitive neuroscience. Currently, at least 52 EEGLAB plug-in tool sets have been released by other researchers from many laboratories. Here we propose, first, to greatly augment the power of the EEGLAB environment by enabling it to perform time series, biophysical, and statistical analyses of multimodal as well as unimodal EEG data. However, ever more precise analyses of large and multimodal data sets and studies require increasing amounts of computational power, more than is readily available in many laboratories. Thus second, in collaboration with the San Diego Supercomputer Center (SDSC) we propose to expand the current Neuroscience Gatew ay (nsgportal.org) services to enable EEGLAB users to freely run EEGLAB processing scripts and pipelines on SDSC supercomputers. The proposed Open EEGLAB Portal will allow researchers to submit any amount of unimodal or multimodal EEG data for parallel processing using standard or custom EEGLAB processing pipelines. We will also develop and release first tools for meta-analysis of source-resolved EEG measures across studies. Multimodal EEG analysis and source-level EEG analysis accelerated by free use of supercomputing resources will give the EEG research community unprecedented abilities to observe and model distributed cortical dynamics supporting h...