Research
SCCN research focuses on EEG and related electrophysiological signals as windows into neural dynamics, cognition, behavior, clinical state, and human experience.
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, identifyin
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
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
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 p
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 learni
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 t
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
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 as
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 Dy
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 s
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 (s