People

Faculty

Research Scientist & Director

Analysis and modeling of human cognitive event-related brain dynamics as captured by high-dimensional EEG, MEG and other imaging modalities now including body motion capture.

Research Scientist & Associate Director, Adj. Professor of Biomedical Engineering, UCSD

Analysis and interpretation of neural activity measured by EEG, MEG and fMRI associated with human cognition, awareness and perception.
Brain-computer interface and interaction
Development of mobile wireless EEG and BCI systems that feature dry EEG sensor arrays and integrated data and signal processing hardware and software to obtain non-invasive, high spatial and temporal resolution, recording and interpretation of brain activity in unconstrained, freely-moving human subjects within operational environments.

Research Scientist & Associate Director

Brain mechanisms of motivation, reward, and conditioning
Neural basis of behavioral addictions (especially compulsive sexual behaviors and problematic pornography use)
Neural and psychological mechanisms of substance use disorders
Basic mechanisms of Transcranial Magnetic Stimulation (TMS) effects on brain activity
Use of TMS and EEG for addiction treatment
Development of more effective methods for addiction treatment

2014–present

Professor of Electrical & Computer Engineering, UCSD

The development of sensor-based intelligent learning systems that can function in unstructured, nonstationary environments and has on-going research activities in the areas of statistical signal processing; statistical learning theory and pattern recognition; adaptive sensory-motor control; nonlinear dynamics and multibody systems theory.

Project Scientist

Continued development of the EEGLAB software for EEG single-trial analysis and independent component analysis.
EEG data mining using super-computers in large sets of data comprising thousands of subjects.
Brain dynamics associated with mind wandering and its relationship to the default network.
Neural correlates of meditation andother transcendental experiences in different traditions.

Project Scientist

fMRI Basics
Most current analysis methods for fMRI data assume a priori knowledge of the time course of the hemodynamic response (HR) to experimental stimuli or events in brain areas of interest. In addition, they typically assume homogeneity of both the HR and the non-HR "noise" signals, both across brain regions and across similar experimental events. When HRs vary unpredictably, from area to area or from trial to trial, an alternative approach is needed. Here, we use infomax Independent Component Analysis (ICA) to detect and visualize variations in single-trial HRs in event-related fMRI data.

Assistant Project Scientist

Topographical mapping (retinotopy, tonotopy, somatotopy, multisensory maps) of cortical areas using fMRI
Multisensory representation of space in multiple reference frames; multisensory attention, interaction, and integration
Neural basis of metaphorical abstraction of space and time
Multimodal neuroimaging (EEG/fMRI/NIRS) of sensorimotor functions
Brain dynamics (EEG/fMRI) during sustained-attention tasks (driving); alertness monitoring
EEG-based brain computer interface (BCI)
Development of MR-compatible devices

Understanding and characterizing the alpha and theta rhythms using statistical signal processing, dynamical systems, and computational neuroscience methods.
General information theoretic properties of independence and mutual information based transformations for large sensor arrays, with application to EEG and speech, image and video coding.
Development of the AMICA program for large scale adaptive parallel ICA.
Development of VLSI implentation of convolutive ICA with adaptive linear process mixture model sources.