EEGLab: Software Analysis of Human Brain Dynamics

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 used for recording in lifelike situations. A major shift in scientific perspective on the nature and use of human electrophysiological data is now ongoing, a shift to using EEG data as a source-resolved, relatively high-resolution 3D cortical source imaging modality. The EEGLAB signal processing environment is a readily extensible open-source software project of the Swartz Center for Computational Neuroscience (SCCN) of the University of California, San Diego (UCSD). EEGLAB 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 21 years later, its reference paper {Delorme, 2004 #1} has over 18,450 citations (increasing by 6.8 per day), its opt-in EEGLAB discussion email list links over 6,000 researchers, its EEGLAB news reaches over 15,000, and an independent 2011 survey of 687 research respondents reported EEGLAB to be the software environment most widely used for electrophysiological data analysis in cognitive neuroscience. EEGLAB citations and other metrics show that EEGLAB adoption is still growing steadily. Here, we will greatly augment the power of the EEGLAB environment by providing support for processing both intracranial (iEEG, sEEG) and mobile brain/body imaging (MoBI) data (EEG and behavior), and will further integrate tools for performing high-resolution source imaging from EEG (or iEEG) data. Its suitability for multi-modal brain/behavioral recording is one of the strengths of EEG recording compared to other imaging modalities. Multimodal data review and processing tools will be incorporated into EEGLAB, to further support the development of tools for processing mobile brain imaging data. We will develop a framework for source connectivity analysis using (1) a hierarchical Bayesian framework for clustering effective source processes identified by independent component analysis on multiple measures across subjects and studies and (2) region of interest (ROI) dynamics estimation by beamforming. We will further revise the EEGLAB architecture to use a file and metadata organization compatible with the Brain Imaging Data Structure (BIDS) specifications. These tools will integrate the Hierarchical Event Descriptor (HED) event annotation system to enable innovative meta-analyses across data from multiple studies. These continuing developments will further the use of non-invasive and (as per clinical need) invasive human electrophysiology for 3-D functional cortical brain imaging, thereby accelerating progress in noninvasive basic and clinical human brain research using highly time- and space-resolved measures of brain electrophysiological dynamics.