Project Summary Electrophysiological recordings in humans and animals play an essential role in developing an understanding of the human brain. Signal recording technology spans the entire scale from invasive microelectrode single-unit recordings, through mesoscale macroelectrode measures of local field potentials, to whole-brain monitoring through measurement of scalp potentials (EEG) and extracranial magnetic fields (MEG). Analysis of these data presents a host of challenges, from low level noise removal and artifact rejection to sophisticated spatio-temporal modeling and statistical inference. The multidisciplinary neuroscience research community has an ongoing need for validated and documented open-source software to perform this analysis and to facilitate reproducible and large-scale research involving electrophysiological data. This proposal describes our plans to continue to develop and support Brainstorm, open-source software that meets this need. Brainstorm is a Matlab/Java multi-platform (Linux, MacOS, Windows) software package for analysis and visualization of electrophysiological data. The software is extensively documented through a series of detailed tutorials and actively supported through a user forum and a mailing list. Over the past 8 years we have registered 16,000 distinct users, provided hands on instruction to 1,200 trainees, and the software has been used and cited in ~600 journal papers. Brainstorm includes tools for importing MEG/EEG, intracranial EEG, animal electrophysiology, and near-infrared spectroscopy (NIRS) data from multiple vendors, extensive interactive features for data preprocessing, selection and visualization, coregistration to volume and surface MRIs and atlases, forward and inverse mapping of cortical current density, time-series and connectivity analysis, and a range of statistical tools. Data can be analyzed through a graphical interface or through scripted pipelines. The current proposal represents a plan to extend Brainstorm in a manner that leverages the unique features of our software and addresses important needs for large-scale data analysis. In this project we will continue to extend and support our software through the following three specific aims: (i) we will harness recent developments in distributed and shared data and high performance computing resources, together with standardization of data organization, to facilitate large-scale, reproducible analysis of electrophysiological data. (ii) We will also address the need for improved modeling resulting from the increasing use of both invasive recordings and direct brain stimulation through development of new modeling software for accurate computation of the intracranial electromagnetic fields produced by brain stimulation and neuronal activation. (iii) Finally, we will continue to add new functionality and to support the software through in-person training, online forums, documentation and other resources.