1. Neuroinformatics Research at UO

2. NeuroInformatics CenterFeb 2005BBMI: Brain, Biology, Machine Initiative Experimental Methodology and Tool Integration source localization constrained to cortical surface processed EEG BrainVoyager BESA CT / MRI EEG segmented tissues 16x256 bits per millisec (30MB/m) mesh generation EMSE Interpolator 3D NetStation

3. NeuroInformatics CenterFeb 2005BBMI: Brain, Biology, Machine Initiative 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

4. NeuroInformatics CenterFeb 2005BBMI: Brain, Biology, Machine Initiative 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”

5. NeuroInformatics CenterFeb 2005BBMI: Brain, Biology, Machine Initiative 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

6. NeuroInformatics CenterFeb 2005BBMI: Brain, Biology, Machine Initiative 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

7. NeuroInformatics CenterFeb 2005BBMI: Brain, Biology, Machine Initiative EEG Dense-Array Methodology

8. NeuroInformatics CenterFeb 2005BBMI: Brain, Biology, Machine Initiative 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

9. NeuroInformatics CenterFeb 2005BBMI: Brain, Biology, Machine Initiative APECS Evaluation: Qualitative Criteria

10. NeuroInformatics CenterFeb 2005BBMI: Brain, Biology, Machine Initiative 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).

11. NeuroInformatics CenterFeb 2005BBMI: Brain, Biology, Machine Initiative 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

12. NeuroInformatics CenterFeb 2005BBMI: Brain, Biology, Machine Initiative 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)