neuroinformatics research at uo

Neuroinformatics Research at UO

NeuroInformatics Center Feb 2005BBMI: Brain, Biology, Machine Initiative

Experimental Methodology and Tool Integration

source localization constrained to cortical surface

processed EEG

BrainVoyager

BESA

CT / MRI

EEG segmentedtissues

16x256bits permillisec(30MB/m)

mesh generation

EMSEInterpolator 3D

NetStation


NeuroInformatics Center (NIC) at UO Application of computational science methods to

human neuroscience problems Tools to help understand dynamic brain function Tools to help diagnosis brain-related disorders HPC simulation, large-scale data analysis, visualization

Integration of neuroimaging methods and technology Need for coupled modeling (EEG/ERP, MR analysis) Apply advanced statistical analysis (PCA, ICA) Develop computational brain models (FDM, FEM) Build source localization models (dipole, linear inverse) Optimize temporal and spatial resolution

Internet-based capabilities for brain analysis services, data archiving, and data mining


Funding Support

BBMI federal appropriation DoD Telemedicine Advanced Technology Research

Center (TATRC) $40 million research attracted by BBMI $10 million gift from Robert and Beverly Lewis family

Established Lewis Center for Neuroimaging (LCNI) NSF Major Research Instrumentation

“Acquisition of the Oregon ICONIC Grid for Integrated COgnitive Neuroscience Informatics and Computation”

New proposal NIH Human Brain Project Neuroinformatics “GENI: Grid-Enabled Neuroimaging Integration”


Electrical Geodesics Inc. (EGI)

EGI Geodesics Sensor Net Dense-array sensor technology

64/128/256 channels 256-channel geodesics sensor net

AgCl plastic electrodes Carbon fiber leads

Net Station Advanced EEG/ERP data analysis

Stereotactic EEG sensor registration Research and medical services

Epilepsy diagnosis, pre-surgical planning


NeuroInformatics for Brainwave Research Electroencephalogram (EEG)

EEG time series analysis Event-related potentials (ERP)

Averaging to increase SNR Linking brain activity to sensory–motor, cognitive

functions (e.g., visual processing, response programming) Signal cleaning (removal of noncephalic signal, “noise”) Signal decomposition (PCA, ICA, etc.) Neural Source localization


EEG Dense-Array Methodology


APECS: A new tool for EEG data decomposition

Automated Protocol for Electromagnetic Component Separation Motivation

EEG data cleaning (increases SNR) Separation of EEG components (addresses superposition) Data preprocessing prior to source localization

Distinctive Features Implements variety of algorithms (PCA, ICA, SOBI, etc.) Uses multiple metrics for fast, automatic classification of

extracted components Applies multiple criteria to evaluate success of decomposition (to

ensure that artifacts are cleanly separated from cortical activity) Calls high-performance, parallel C++ implementations of

Infomax and FastICA algorithms


APECS Evaluation: Qualitative Criteria


APECS Evaluation: Quantitative Criteria

Covariance between “baseline” (blink-free) and ICA-filtered data. Yellow, Infomax; blue, FastICA.

Infomax gives consistently better results. FastICA results are more variable.

ICA decompositions most successful when only one spatial projector is strongly correlated with blink “template” (spatial filter).


High-Performance ICA

Parallel FastICA: Over 130 times faster than MATLAB fastica.m Greater than 8-fold increase in

performance on 32 processors

Parallel Infomax: Over 3 times faster than MATLAB runica.m Greater than 3-fold increase in performance on 4 processors


Brain, Machine, and Education Pittsburgh Science of Learning Center (PSCL) Collaboration

http://pslc.hcii.cs.cmu.edu/tiki-index.php LearnLab Research Facility (U. Pittsburgh, CMU)

Authoring tools for online courses, experiments, and integrated computational learner models

Support for running in vivo learning experiments Longitudinal microgenetic data from entire courses Data analysis tools, including software for learning curve

analysis and semi-automated coding of verbal data Parallel studies of learning using cognitive neuroscience (EEG,

fMRI) methods Multidisciplinary Effort

Computer Science (e.g., Maxine Eskanazi, Jamie Callan — CMU) Linguistics & ESL (e.g., Alan Juffs — U. Pittsburgh ) Psychology (Charles Perfetti — U. Pittsburgh)

neuroinformatics research at uo

Documents

brain analysis services

snrlinking brain activity

dynamic brain functiontools

uoneuroinformatics centerfeb

machine initiativeapecs

data archiving

largescale data analysis

neuroinformatics research