My primary research interest is in analysis and modeling
of cognitive
event-related brain dynamics
as captured by high-dimensional EEG, MEG and other brain imaging
modalities using
Independent Component Analysis,
time-frequency and
machine learning
methods.
In particular, I have studied the dynamics of
performance and electrophysiology accompanying
alertness lapses
during sustained monitoring tasks, and have used the results of
this research to design
real-time alertness monitoring systems,
one application in the emerging field of
neural human-system interface technology.
Currently, I am working to apply
Independent Component Analysis
to
EEG, ERP
and fMRI data to open wider windows for noninvasive observation
of cognitive brain dynamics.
Current collaborators include
Howard Poizner (INC/UCSD),
Terry Sejnowski
at the
Computational Neurobiology Laboratory, Salk Institute,
Tzyy-Ping Jung,
Arnaud Delorme,
and J-R Duann
at SCCN,
Jim Stieben at York University, Toronto,
and Eric Courchesne at the
Department of Neurosciences,
UCSD.
I now direct the new
Swartz Center for Computational Neuroscience of the
Institute for Neural Computation, UCSD.
The Center is housed in a suite of offices at the edge of the UCSD campus and
includes two EEG laboratory spaces and a workstation cluster. We are looking to
build collaborations
with researchers wanting to apply new analytic methods to EEG, fMRI
and other types of cognitive neuroscience data, as well as with
physicists and mathematicians wanting to model human brain dyanmics
underlying brain cognitive capacities, including attention, memory, emotion,
social interaction and creativity.
Current projects include concurrent EEG and fMRI, concurrent EEG and face video,
EEG and nonverbal emotional expression, advanced ICA imaging, working memory,
social cognition, and brain dynamics during EEG biofeedback.