Brain Initiative Resource: Development Of A Human Neuroelectromagnetic Data Archive And Tools Resource (NEMAR)
A computed image of the distribution of a cortical source of high-density EEG data.

To take advantage of recent and ongoing advances in intensive and large-scale computational methods, and to preserve the scientific data created by publicly funded research projects, data archives must be created as well as standards for specifying, identifying, and annotating deposited data. The value of and interest in such archives among researchers can be greatly increased by adding to them an active computational capability and framework of analysis and search tools that support further analysis as well as larger scale meta-analysis and large scale data mining. The archive, begun as a repository for functional magnetic resonance imaging (fMRI) data, is such an archive. We propose to build a gateway to OpenNeuro for human electrophysiology data (EEG and MEG, as well as intracranial data recorded from clinical patients to plan brain surgeries or other therapies) – herein we refer to these modalities as neuroelectromagnetic (NEM) data. The Neuroelectromagnetic Data Archive and Tools Resource (NEMAR) at the San Diego Supercomputer Center will act as a gateway to OpenNeuro for NEM data research. Such data uploaded to NEMAR at SDSC will be deposited in the OpenNeuro archive. Still- private NEM data in OpenNeuro will, on user request, be copied to the NEMAR gateway for further user processing using the XSEDE high-performance resources at SDSC in conjunction with The Neuroscience Gateway (, a freely available and easy to use portal to use of high-performance computing resources for neuroscience research. Publicly available OpenNeuro NEM data will be able to be analyzed by running verified analysis applications on the OpenNeuro system. In this project we will build an application to evaluate the quality of uploaded NEM data, and another to visualize the data, for EEG and MEG at both the scalp and brain source levels, including time-domain and frequency-domain dynamics time locked to sets of experimental events learned from the BIDS- and HED-formatted data annotations. The NEMAR gateway will take a major step toward applying machine learning methods to a large store of carefully collected and stored human electrophysiologic brain data to spur new developments in basic and clinical brain research.

Press Release

Ongoing research projects aimed at widening the scope of EEG research to include the brain dynamics of affective, emotional and social cognition.

Modeling long-range brain interactions during affective processing. We are using independent component analysis (ICA) and time/frequency analysis techniques to study a phenomenon we have detected in high-dimensional EEG data, theta band synchronization events that appear to be produced in the EEG only in response to significant having immediate implications for reshaping behavioral planning. We are studying these events using a set of continuous performance tasks involving frequent choice behavior and immediate performance feedback. We believe the TSEs index coordinated activity of a corticolimbic system for assessing the emotional and behavioral significance of events and automatically adjusting the focus of attention (and distraction) accordingly. They appear to provide an opportunity to observe and monitor the cortical dynamics associated with subjects' "gut reaction" responses to stimuli and events.

EEG dynamics of emotional processing. We are studying EEG dynamics during 3-5 minute periods when the subject actively imagines a situation provoking a suggested emotion, attempting to recreate the bodily feelings associated with the emotion. First results on data from 15 such emotion imagination conditions shows that EEG dynamic changes in different emotional states are complex, requiring multidimensional modeling. Potentially, these results could be extended to emotion monitoring for clinical and other purposes.

The neurodynamics of social interaction. As social interactions are of high importance to our survival and propagation, both as individuals and as societies, it is natural to suppose that the brains our highly evolved for interacting with other humans. It is possible that the brain dynamics supporting these interactions cannot be (and have not been) recorded in the absence of experiments involving actual (or, to an unknown extent, simulated) interactions. Currently, use of hemodynamic brain imaging during social interactions is being explored in several university research centers. However, hemodynamic imaging only indexes the activity of the blood resupply system subsequent to brain activity. EEG and MEG imaging, on the other hand, observe the full time course and frequency range of macroscopic neural dynamics. A first experiment using our unique two-person high-density EEG and video recording system involves a two-person computer-mediated guessing game with financial (bonus) rewards.

Other projects concern the brain dynamics of learning and memory

EEG dynamics of learning. We are looking for EEG signs of growing expertise in a difficult visual task. We propose to give subjects EEG-based feedback in addition to standard performance-based feedback, to determine whether and how this might accelerate learning. Under a proposed DARPA project, we would extend this approach to subjects whose job is to review static and video imagery for security purposes. Under pilot Kavli Foundation funding, we will explore the application of these ideas to motor learning with Howard Poizner (UCSD), and under a proposed UCSD NSF Science of Learning Center, we would extend this approach to other types of learning.

6. EEG dynamics of memory processing. During short term working memory, 4-8 Hz theta band activity in the EEG recorded over the frontal midline increases in mean amplitude. Our research shows that the mean increase only captures one aspect of the dynamics of the EEG changes associated with memory processing. We have isolated several dynamic modes involving at least three frequency bands in which dynamic changes occur in frontal midline EEG related to current memory load. Other brain locations show a variety of other dynamic changes that this project seeks to model and to relate to behavior, task context, etc.

Ongoing basic research on the origin, functions, and dynamics of human EEG activityincludes:

Multiscale analysis of scalp and intracranial data. In this project, we are analyzing EEG and intracranial EEG (iEEG or ECOG) data collected by our collaborator Dr. Greg Worrell of the Mayo Clinic, Rochester MN, and soon, by our UCSD collaborator, neurosurgeon Dr. Robert Buchanan. We are using ICA to determine the relationship between electrical field activity recorded in the brain and on the scalp. This project will investigate the degrees to which synchronization of cortical field activity at smaller spatial scales produces activity recorded at a larger spatial scale on the scalp, and how much activity reaching the scalp can contribute to analysis of local phenomena collected from within the brain itself.

Advanced applications of independent component analysis to brain imaging data. Several efforts are underway in the Center to apply current developments in blind source separation to EEG and fMRI data. These include EMSICA of Arthur Tsai, working with Center associate director Tzyy-Ping Jung. EMSICA (ElectroMagnetic Spectrotemporal ICA) simultaneously maximizes the probability of a linear multi-source model of EEG specified by a time course of activation and a map of relative activity strength on a model of the cortex itself. If successful, this may be an advance in the decomposition of high-dimensional scalp EEG data into anatomically localized sources.

Other projects, in conjunction with students of UCSD engineering Profs. Rao and Kreutz-Delgado, are studying the application of sparse decomposition methods to EEG time series data. With Lars Kai Hansen of the Danish Technica University and his student Mads Dyrholm, we have been studying the application of convolutive ICA modeling to EEG analysis.

With collaborators from the UCSD medical school and elsewhere, we are beginning to explore medical research applications of our new analysis methods:

Independent factor analysis of structural and chemical brain imaging data. With collaborators from Taiwan and the National Institutes of Health USA, we are applying ICA methods to factor analysis of co-registered MRI, PET and SPECT brain images from clinical subject groups and controls, to determine the brain imaging correlates of clinical disease classification.

Finally, in addition to publishing our methods and results in international science journals, we are creating and freely distributing via the world wide web two open source environments for applying our analysis advances in brain data analysis.These open source platforms are also providing environments for other groups to test and distribute their new analysis approaches:

Open source software development and distribution - EEGLAB and FMRLAB. In 1997, we began putting Matlab functions implementing the new ICA and related analysis methods we first developed at Salk Institute for free download. The Swartz Center (SCCN) web site ( is now the download home for two software suites, EEGLAB and FMRLAB, that implement a wide range of analyses on EEG and functional magnetic resonance imaging (fMRI) data, respectively. Development and maintenance of EEGLAB is proceeding under a grant from the National Institutes of Health. We recently hosted the first international EEGLAB workshop on the UCSD campus. We are building these two Matlab software suites as open source environments for nearly any advanced processing of dynamic brain imaging data, and plan to add a third, bridging suite for analysis of EEG data collected during fMRI scanning.