Neuroinformatics Research at UO. NeuroInformatics CenterFeb 2005BBMI: Brain, Biology, Machine Initiative Experimental Methodology and Tool Integration.

Slides:

Advertisements

Similar presentations

FMRI Methods Lecture 10 – Using natural stimuli. Reductionism Reducing complex things into simpler components Explaining the whole as a sum of its parts.

Advertisements

Simultaneous recording of EEG and BOLD responses

Electroencephalogram (EEG) and Event Related Potentials (ERP) Lucy J. Troup 28 th January 2008 CSU Symposium on Imaging.

Brain-computer interfaces: classifying imaginary movements and effects of tDCS Iulia Comşa MRes Computational Neuroscience and Cognitive Robotics Supervisors:

1st practice Medical Informatics Biomedical Signal Processing TAMUS, Zoltán Ádám

1 Cyberinfrastructure Framework for 21st Century Science & Engineering (CF21) IRNC Kick-Off Workshop July 13,

HST 583 fMRI DATA ANALYSIS AND ACQUISITION Neural Signal Processing for Functional Neuroimaging Emery N. Brown Neuroscience Statistics Research Laboratory.

Artifact (artefact) reduction in EEG – and a bit of ERP basics CNC, 19 November 2014 Jakob Heinzle Translational Neuromodeling Unit.

MAPPING RESULTS Experiments were performed on two simulated datasets, each using both metric sets. Cross spatial join: calculate the Euclidean distance.

Standard electrode arrays for recording EEG are placed on the surface of the brain. Detection of High Frequency Oscillations Using Support Vector Machines:

REAL-TIME INDEPENDENT COMPONENT ANALYSIS IMPLEMENTATION AND APPLICATIONS By MARCOS DE AZAMBUJA TURQUETI FERMILAB May RTC 2010.

NEUR 4850 Advanced Techniques in Cognitive Neuroscience.

Application of Statistical Techniques to Neural Data Analysis Aniket Kaloti 03/07/2006.

Neuroscientific Modeling of Deception with HD-ERP and fMRI Data: Experimental and Computational Problems Jennifer M. C. Vendemia Michael Jay Schillaci.

High-Performance Computing, Computational Science, and NeuroInformatics Research Allen D. Malony Department of Computer and Information Science NeuroInformatics.

Neuroinformatics, the ICONIC Grid, and Oregon’s Science Industry Allen D. Malony University of Oregon Oregon’s 2004 Bioscience ConferenceMay 10, 2004 Professor.

Multi-Cluster, Mixed-Mode Computational Modeling of Human Head Conductivity Adnan Salman 1, Sergei Turovets 1, Allen Malony 1, and Vasily Volkov 1 NeuroInformatics.

ICONIC Grid – Improving Diagnosis of Brain Disorders Allen D. Malony University of Oregon Professor Department of Computer and Information Science Director.

The Neural ElectroMagnetic Ontology (NEMO) System: Design & Implementation of a Sharable EEG/MEG Database with ERP ontologies G. A. Frishkoff 1,3 D. Dou.

Biomedical Image Analysis and Machine Learning BMI 731 Winter 2005 Kun Huang Department of Biomedical Informatics Ohio State University.

HELSINKI UNIVERSITY OF TECHNOLOGY LABORATORY OF COMPUTER AND INFORMATION SCIENCE NEURAL NETWORKS RESEACH CENTRE Variability of Independent Components.

Neuroinformatics, the ICONIC Grid, and GEMINI Allen D. Malony University of Oregon Professor Department of Computer and Information Science Director NeuroInformatics.

Applications of Independent Component Analysis Terrence Sejnowski Computational Neurobiology Laboratory The Salk Institute.

Allen D. Malony Performance Research Laboratory (PRL) Neuroinformatics Center (NIC) Department.

ERP DATA ACQUISITION & PREPROCESSING EEG Acquisition: 256 scalp sites; vertex recording reference (Geodesic Sensor Net)..01 Hz to 100 Hz analogue filter;

Neuroinformatics for Telemedicine and Medical Services Neuroinformatics Center University of Oregon Allen D. Malony Department of Computer and Information.

Data Processing Machine Learning Algorithm The data is processed by machine algorithms based on hidden Markov models and deep learning. They are then utilized.

Neuroimaging Methods: Visualising the brain & its injuries Structural (brain structure) –X-rays –CT (Computer Tomography) –MRI (Magnetic Resonance Imaging)

 Background: Electrophysiological Data Management  Challenges: Big Data, Multicenter studies  Cloudwave Framework: Features, Components  Current Results.

Brain Innovation BVTurbo BrainVoyager Training Course January, 2011 Real-time Independent Component Analysis of functional MRI time-series A new TBV (3.0)

Parallel and Distributed Computing for Neuroinformatics Allen D. Malony University of Oregon Professor Department of Computer and Information Science Director.

Mark van Rossum Mark van Rossum Edinburgh.

THE TUH EEG CORPUS: A Big Data Resource for Automated EEG Interpretation A. Harati, S. López, I. Obeid and J. Picone Neural Engineering Data Consortium.

Robust Functional Mixed Models for Spatially Correlated Functional Regression -- with Application to Event-Related Potentials for Nicotine-Addicted Individuals.

Network modelling using resting-state fMRI: effects of age and APOE Lars T. Westlye University of Oslo CAS kickoff meeting 23/

Exploration of Instantaneous Amplitude and Frequency Features for Epileptic Seizure Prediction Ning Wang and Michael R. Lyu Dept. of Computer Science and.

GENII: Grid-based Environment for Neuroimaging Integration and Interoperation Human Brain Project – Neuroinformatics Proposal Allen D. Malony Don Tucker.

Parallel Simulated Anealing for Computational Modeling of Human Head Conductivity A. Salman 1, Allen D. Malony 1,2, S. Turovets 1, D. Tucker 3 Department.

EXPERIMENT DESIGN  Variations in Channel Density  The original 256-channel data were downsampled:  127 channel datasets  69 channels datasets  34.

Design, Optimization, and Control for Multiscale Systems

Allen D. Malony, Professor  University of Illinois, Urbana-Champaign  Fulbright Research Scholar  The Netherlands  Austria  Alexander von Humboldt.

Advanced Analysis and Modeling Tools for Columnar- and Laminar-Level High- Resolution fMRI Data at 7+ Tesla Rainer Goebel Maastricht Brain Imaging Center.

Analysis Techniques for Weak Brain Activity Alain de Cheveigné CNRS / Université Paris Descartes / École normale supérieure / UCL Ear Institute AIM Isolate.

Workshop on direct brain/computer interface & control Febo Cincotti Fondazione Santa Lucia IRCCS Brussels, August 2, 2006.

High-Performance and Grid Computing for Neuroinformatics: NIC and Cerebral Data Systems Allen D. Malony University of Oregon Professor Department of Computer.

High-Performance and Grid Computing for Neuroinformatics: EGI, UO, and Cerebral Data Systems Allen D. Malony University of Oregon Professor Department.

National Alliance for Medical Image Computing Core What We Need from Cores 1 & 2 NA-MIC National Alliance for Medical Image Computing.

Automatic Discovery and Processing of EEG Cohorts from Clinical Records Mission: Enable comparative research by automatically uncovering clinical knowledge.

Acknowledgement Work supported by NINDS (grant NS39845), NIMH (grants MH42900 and 19116) and the Human Frontier Science Program Methods Fullhead.

EEG DATA EEG Acquisition: 256 scalp sites; vertex recording reference (Geodesic Sensor Net)..01 Hz to 100 Hz analogue filter; 250 samples/sec. EEG Preprocessing:

Intelligent Systems Research Centre University of Ulster, Magee Campus BCI Research at the ISRC, University of Ulster N. Ireland, UK By Dr. Girijesh Prasad.

Makeig-Worrell NCRR Project Overview Scott Makeig, Ph.D. is the Director of the Swartz Center for Computational Neuroscience Institute for Neural Computation.

The Neural Engineering Data Consortium Mission: To focus the research community on a progression of research questions and to generate massive data sets.

Introduction to autism research Ilan Dinstein

Development of NeuroElectroMagnetic Ontologies (NEMO): A Framework for Mining Brainwave Ontologies Dejing Dou 1, Gwen Frishkoff 2, Jiawei Rong 1, Robert.

NeuroInformatics Center

Directed Component Analysis

[Ran Manor and Amir B.Geva] Yehu Sapir Outlines Review

Parallel and Distributed Computing for Neuroinformatics

A new open-source tool for EEG source reconstruction in infants

EEG Correlates of Mindfulness and Expanded Self Experience

Brain Initiative Informational Conference Call

Dr. Allen D. Malony Computer & Information Science Department

Adnan Salman1 , Sergei Turovets1, Allen Malony1, and Vasily Volkov

To learn more, visit The Neural Engineering Data Consortium Mission: To focus the research community on a progression of research questions.

CHAPTER 7: Information Visualization

Machine Learning for Visual Scene Classification with EEG Data

M/EEG Statistical Analysis & Source Localization

BCI Research at the ISRC, University of Ulster N. Ireland, UK

Feature Selection in BCIs (section 5 and 6 of Review paper)

Presentation transcript:

Neuroinformatics Research at UO

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

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

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”

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

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

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

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

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

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).

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

NeuroInformatics CenterFeb 2005BBMI: Brain, Biology, Machine Initiative Brain, Machine, and Education  Pittsburgh Science of Learning Center (PSCL) Collaboration   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)