Stefanos D. Georgiadis Perttu O. Rantaaho Mika P. Tarvainen Pasi A. Karjalainen. University of Kuopio Department of Applied Physics Kuopio, FINLAND


 Charla Lang
 1 years ago
 Views:
Transcription
1 5 Finnish Signal Processing Symposium (Finsig 5) Kuopio, Finland Stefanos D. Georgiadis Perttu O. Rantaaho Mika P. Tarvainen Pasi A. Karjalainen University of Kuopio Department of Applied Physics Kuopio, FINLAND Stefanos D. Georgiadis, Department of Applied Physics, University of Kuopio Slide 1
2 5 Finnish Signal Processing Symposium (Finsig 5) Kuopio, Finland INTRODUCTION To understand human neurophysiology, we relay on several types of noninvasive neuroimaging techniques. These techniques include electroencephalography (EEG), magnetoencephalography (MEG), anatomical magnetic resonance imaging (MRI) and functional MRI (fmri). Neural activity in the cerebral cortex generates small electric currents which create potential differences on the surface of the scalp (detected by EEG). EEG recording is a useful tool for studying the functional states of the brain and for diagnosing certain neurophysiological states and disorders. Stefanos D. Georgiadis, Department of Applied Physics, University of Kuopio Slide 2
3 5 Finnish Signal Processing Symposium (Finsig 5) Kuopio, Finland NOISE One of the challenging tasks is how to reliably detect, enhance and estimate very week, non stationary brain signals corrupted by noise. E.g. Denoising or estimation of Event Related Potentials (ERPs). Stefanos D. Georgiadis, Department of Applied Physics, University of Kuopio Slide 3
4 5 Finnish Signal Processing Symposium (Finsig 5) Kuopio, Finland EVENT RELATED POTENTIALS (ERPs) ERPs are voltage changes of brain electric activity due to stimulation, e.g external auditory. The simplest way to investigate them is to use ensemble averages of timelocked EEG data epochs. The investigation of the variability of ERP parameters can be used to reveal information related to changes of the cognitive state. Singletrial analysis methods are under concern. Different methods exist, e.g. digital filtering, wavelets, or multichannel methods, e.g Independent Component Analysis. Here we focus on singletrial single channel ERP estimation. We are interested in cases that the ERPs have dynamic changes form trialtotrial, e.g. some trend at the amplitude or latency of some peak. Recursive estimation methods can be used to track such changes. Stefanos D. Georgiadis, Department of Applied Physics, University of Kuopio Slide 4
5 5 Finnish Signal Processing Symposium (Finsig 5) Kuopio, Finland SINGLETRIAL ERP EXTRACTION Preprocessing (e.g. bandpass 14Hz) Channel CZ Stim. no. 1 Stim. no. 2 Stim. no. 3 Stim. no. 4 Amplitude and latency of different peaks Ensemble Average 1 N1 N P3 Singletrial estimation Difficulties with low frequencies. Spectra of the unknown interesting activity and background noise overlap heavily. Stefanos D. Georgiadis, Department of Applied Physics, University of Kuopio Slide 5
6 5 Finnish Signal Processing Symposium (Finsig 5) Kuopio, Finland ERP ESTIMATION The sampled ERP measurement of length M at stimulus t, t = 1, 2,..., T can be denoted with the column vector: z t = (z t,1, z t,2,..., z t,m ) T, (measurement epoch) ERPs can be modeled as a linear combination of some preselected basis vectors z t = H t θ t + υ t, (additive noise model) Ht, observation model, basis vectors of length M it its columns. st = H t θ t, part of the activity that is related to the stimulation. υt, noise vector. θt parameter vector to be estimated, e.g. Least Squares. ŝt = H t ˆθt, the estimated response. Stefanos D. Georgiadis, Department of Applied Physics, University of Kuopio Slide 6
7 5 Finnish Signal Processing Symposium (Finsig 5) Kuopio, Finland STATESPACE FORMALISM Of special interest is the case that some characteristics of the ERPs change dynamically from stimulus to stimulus. This case can be naturally modeled with a statespace model θ t+1 = F t θ t + ω t, (state equation) The hidden states are not observed directly, but through the measurement model z t = H t θ t + υ t, (space equation) Ft, known matrices, for F t = I we have a random walk model. ωt is white noise vector process, independent of θ and υ t. The covariance matrices Cυt and C ωt are known. Estimators for the parameters optimal in the mean square sense are given by Kalman filter and smoother algorithms. Stefanos D. Georgiadis, Department of Applied Physics, University of Kuopio Slide 7
8 5 Finnish Signal Processing Symposium (Finsig 5) Kuopio, Finland RECURSIVE MEAN SQUARE ESTIMATION The mean square estimator for the state θt given the past and present observations z 1,..., z t is given by the conditional mean ˆθ t = E{θ t z 1,..., z t }. If the processes υt and ω t are Gaussian then this estimator is linear and maximizes the posterior density p(θ t z 1,..., z t ), Bayesian maximum a posteriori estimator (MAP). Recursive solution (linear mean square) for the problem is given by Kalman filter algorithm. If all the data set is available, the mean square estimator given all observations z 1,..., z T is given by the conditional mean ˆθ s t = E{θ t z 1,..., z T }. And solution is obtained by Kalman smoother algorithm. Stefanos D. Georgiadis, Department of Applied Physics, University of Kuopio Slide 8
9 5 Finnish Signal Processing Symposium (Finsig 5) Kuopio, Finland KALMAN FILTER AND SMOOTHER Kalman filter equations can be written as ˆθ t t 1 = F t 1 ˆθt 1 C θt t 1 = F t 1 C θt 1 F T t 1 + C wt 1 K t = C θt t 1 H T t (H t C θt t 1 H T t + C υt ) 1 C θt = (I K t H t )C θt t 1 ˆθ t = ˆθt t 1 + K t (z t H t ˆθt t 1 ), θ t is the state estimation error θ t = θ t ˆθ t, C denotes covariance matrices and K t is the Kalman gain matrix. The solution for the fixedinterval Kalman smoother is ˆθ t s = ˆθt + A t (ˆθ t+1 s ˆθ t+1 t ) A t = F T C θt t C 1, θ t+1 t error covariances and Kalman filter state estimates need to be stored. ˆθ s t are then obtained by running backward in time. Stefanos D. Georgiadis, Department of Applied Physics, University of Kuopio Slide 9
10 5 Finnish Signal Processing Symposium (Finsig 5) Kuopio, Finland MEASUREMENTS AND SIMULATIONS simulations noisy simulations 4 epochs and mean Simulations resembling P3 type responses. As linear combinations of 3 Gaussian shaped functions, sinusoidal+random variability for both amplitude, latency of the third peak. Noisy simulations, prestimulus EEG as additive noise real measurments P3 responses from an oddball paradigm with auditory stimuli, channel CZ, sampling rate 5Hz, bandpass 14Hz. Epochs form 1ms to 6ms relative to deviant stimuli time. Stefanos D. Georgiadis, Department of Applied Physics, University of Kuopio Slide 1
11 5 Finnish Signal Processing Symposium (Finsig 5) Kuopio, Finland RESULTS: IMAGE PLOTS Kalman filter Estimates for simulations Kalman smoother epochs and mean Estimates for real measurements Kalman filter epochs and mean Kalman smoother Columns Observation model H Observation model 3 time shifted Gaussian shaped functions, random walk model: H t = H, F t = I, C υt = I, C ωt =.1I, t. Same selections for Kalman filter and smoother and for simulations and real data. Stefanos D. Georgiadis, Department of Applied Physics, University of Kuopio Slide 11
12 5 Finnish Signal Processing Symposium (Finsig 5) Kuopio, Finland RESULTS: DYNAMIC VARIABILITY time interval data points Simulations Time interval for the computation of amplitudes and latencies of the 3 peak, simple max. Timelag (noiseless) Amplitude trends (simulations) Amplitude trends (real data) Real data Latency trends (simulations) Latency trends (real data) MSE KF noisy KF KS noisy KS State noise variance parameter Average of the Mean Square Error C υt = I, C ωt = σ 2 ωi Stefanos D. Georgiadis, Department of Applied Physics, University of Kuopio Slide 12
13 5 Finnish Signal Processing Symposium (Finsig 5) Kuopio, Finland RESULTS: SINGLE TRIALS SingleTrial Estimates simulations: stim real data: stim simulations: stim real data: stim Stefanos D. Georgiadis, Department of Applied Physics, University of Kuopio Slide 13
14 5 Finnish Signal Processing Symposium (Finsig 5) Kuopio, Finland CONCLUSIONS Recursive mean square estimations methods provide excellent singletrial ERP estimates in realistic noise conditions. The methods are suitable when some characteristics of the ERPs change dynamically from trialtotrial, e.g. habituations to stimulation, fatigue, and other time varying effects. The benefit of the Kalman smoother approach is the avoidance of the timelag in the estimates. Kalman filter can be used for on line estimation, e.g. measuring the depth of anesthesia. Data based observation models can also be used, e.g. eigenvectors of the data correlation matrix There are extensions of the method for multichannel simultaneous processing Improvement of the methods relates to the so called statespace identification procedures for the time evolution of the states. Stefanos D. Georgiadis, Department of Applied Physics, University of Kuopio Slide 14
Instrumentation (EEG Electrodes)
EE 791 EEG 1b Recording the EEG To record either evoked or ambient neural activity we can use an assembly of surface electrodes placed at standardized locations on the scalp known as the 1020 system
More informationSoftware for Biosignal Analysis
Software for Biosignal Analysis Various methods for biosignal analysis have been developed during the last few years in collaboration between the of, Kuopio University Hospital and the Brain@Work Laboratory
More informationUniversity of Kuopio Department of Applied Physics Kuopio, FINLAND.
Mikko Kervinen, Marko Vauhkonen, Jari Kaipio and Pasi A. Karjalainen University of Kuopio Department of Applied Physics Kuopio, FINLAND Email: Mikko.Kervinen@uku.fi http://physics.uku.fi/research/biosignal
More informationA NEW SPATIOTEMPORAL FILTERING METHOD FOR SINGLETRIAL ERP SUBCOMPONENT ESTIMATION
8th European Signal Processing Conference (EUSIPCO) Aalborg, Denmark, August 37, A EW SPATIOTEMPORAL FILTERIG METHOD FOR SIGLETRIAL ERP SUBCOMPOET ESTIMATIO Delaram Jarchi, Bahador Makkiabadi and Saeid
More informationAnnouncements. Advanced Signal Processing I. Digital Vs. Analog Filtering. Advanced Signal Processing I 4/25/2016
Announcements Advanced Signal Processing I Digital Filters Time Frequency Approaches Ocular Artifacts Research Proposals due next Monday (May 2) no later than 2 pm via email to instructor Word format (DOCX
More informationFunctional neuroimaging. Imaging brain function in real time (not just the structure of the brain).
Functional neuroimaging Imaging brain function in real time (not just the structure of the brain). The brain is bloody & electric Blood increase in neuronal activity increase in metabolic demand for glucose
More informationCognitive Neuroscience. Questions. Multiple Methods. Electrophysiology. Multiple Methods. Approaches to Thinking about the Mind
Cognitive Neuroscience Approaches to Thinking about the Mind Cognitive Neuroscience Evolutionary Approach Sept 2022, 2004 Interdisciplinary approach Rapidly changing How does the brain enable cognition?
More informationFour Main Approaches COGNITIVE NEUROPSYCHOLOGY. Cognitive Neuropsychology. Experimental cognitive psychology. Cognitive neuropsychology
Four Main Approaches Experimental cognitive psychology Cognitive neuropsychology Computational cognitive science Cognitive neuroscience COGNITIVE NEUROPSYCHOLOGY Cognitive Neuropsychology Concerned with
More informationNonlinear Blind Source Separation and Independent Component Analysis
Nonlinear Blind Source Separation and Independent Component Analysis Prof. Juha Karhunen Helsinki University of Technology Neural Networks Research Centre Espoo, Finland Helsinki University of Technology,
More informationDocumentation Wadsworth BCI Dataset (P300 Evoked Potentials) Data Acquired Using BCI2000's P3 Speller Paradigm (http://www.bci2000.
Documentation Wadsworth BCI Dataset (P300 Evoked Potentials) Data Acquired Using BCI2000's P3 Speller Paradigm (http://www.bci2000.org) BCI Competition III Challenge 2004 Organizer: Benjamin Blankertz
More informationThe ERP Boot Camp! Averaging!
The! Averaging! All slides S. J. Luck, except as indicated in the notes sections of individual slides! Slides may be used for nonprofit educational purposes if this copyright notice is included, except
More informationTHESIS COMPARISON OF EEG PREPROCESSING METHODS TO IMPROVE THE CLASSIFICATION OF P300 TRIALS
THESIS COMPARISON OF EEG PREPROCESSING METHODS TO IMPROVE THE CLASSIFICATION OF P300 TRIALS Submitted by Zachary Cashero Department of Computer Science In partial fulfillment of the requirements for the
More informationResponseTime Corrected Averaging of EventRelated Potentials
ResponseTime Corrected Averaging of EventRelated Potentials Hecke Schrobsdorff hecke@nld.ds.mpg.de Bernstein Center for Computational Neuroscience Göttingen University of Göttingen, Institute for Nonlinear
More informationFEATURE ANALYSIS OF EEG SIGNALS USING SOM
FEATURE ANALYSIS OF EEG SIGNALS USING SOM L. Gráfová, O. Vyšata, and A. Procházka Institute of Chemical Technology Department of Computing and Control Engineering Abstract The electroencephalogram (EEG)
More informationProbability and Random Variables. Generation of random variables (r.v.)
Probability and Random Variables Method for generating random variables with a specified probability distribution function. Gaussian And Markov Processes Characterization of Stationary Random Process Linearly
More informationBayesian probability theory
Bayesian probability theory Bruno A. Olshausen arch 1, 2004 Abstract Bayesian probability theory provides a mathematical framework for peforming inference, or reasoning, using probability. The foundations
More informationThe Kalman Filter. x k+1 F k x k.
The Kalman Filter The RLS algorithm for updating the least squares estimate given a series of observations vectors looked like a filter : new data comes in, and we use it (along with collected knowledge
More informationBiomedical data analysis
Chapter 6 Biomedical data analysis Ricardo Vigário, Jaakko Särelä, Harri Valpola, Erkki Oja 85 86 Biomedical data analysis 6.1 Introduction In a combination of expert efforts from the Laboratory of Computer
More informationCCNY. BME I5100: Biomedical Signal Processing. Linear Discrimination. Lucas C. Parra Biomedical Engineering Department City College of New York
BME I5100: Biomedical Signal Processing Linear Discrimination Lucas C. Parra Biomedical Engineering Department CCNY 1 Schedule Week 1: Introduction Linear, stationary, normal  the stuff biology is not
More informationExtraction of ERPs with NE devices
Extraction of ERPs with NE devices Neuroelectrics White Paper WP201403 Author(s): J. Acedo, D. Ibáñez, A. SoriaFrisch (PhD) Released: Feb 14 th 2014 www.neuroelectrics.com 1 Extraction of ERPs with NE
More informationSingle trial analysis for linking electrophysiology and hemodynamic response. ChristianG. Bénar INSERM U751, Marseille christian.benar@univmed.
Single trial analysis for linking electrophysiology and hemodynamic response ChristianG. Bénar INSERM U751, Marseille christian.benar@univmed.fr Neuromath meeting Leuven March 1213, 29 La Timone MEG
More informationAnalysis of independent components in biomedical signals
Chapter 5 Analysis of independent components in biomedical signals Ricardo Vigário, Jaakko Särelä, Elina Karp, Jarkko Ylipaavalniemi 93 94 Analysis of independent components in biomedical signals 5.1 Biomedical
More informationOverview of Methodology. Human Electrophysiology. Computing and Displaying Difference Waves. Plotting The Averaged ERP
Human Electrophysiology Overview of Methodology This Week: 1. Displaying ERPs 2. Defining ERP components Analog Filtering Amplification Montage Selection AnalogDigital Conversion SignaltoNoise Enhancement
More informationTHE MEASUREMENT OF MOTOR PERFORMANCE. Chapter 3 1
THE MEASUREMENT OF MOTOR PERFORMANCE Chapter 3 1 THIS CHAPTER S CONCEPT The measurement of motor performance is critical to understanding motor learning & development Chapter 3 2 PERFORMANCE OUTCOME MEASURES
More informationDual Estimation and the Unscented Transformation
Dual Estimation and the Unscented Transformation EricA. Wan ericwan@ece.ogi.edu Rudolph van der Merwe rudmerwe@ece.ogi.edu Alex T. Nelson atneison@ece.ogi.edu Oregon Graduate Institute of Science & Technology
More informationSystem Identification for Acoustic Comms.:
System Identification for Acoustic Comms.: New Insights and Approaches for Tracking Sparse and Rapidly Fluctuating Channels Weichang Li and James Preisig Woods Hole Oceanographic Institution The demodulation
More informationLecture 3: Bayesian Optimal Filtering Equations and Kalman Filter
Equations and Kalman Filter Department of Biomedical Engineering and Computational Science Aalto University February 10, 2011 Contents 1 Probabilistics State Space Models 2 Bayesian Optimal Filter 3 Kalman
More informationTHESIS SINGLETRIAL P300 CLASSIFICATION USING PCA WITH LDA AND NEURAL NETWORKS. Submitted by. Nand Sharma. Department of Computer Science
THESIS SINGLETRIAL P300 CLASSIFICATION USING PCA WITH LDA AND NEURAL NETWORKS Submitted by Nand Sharma Department of Computer Science In partial fulfillment of the requirements For the Degree of Master
More informationfile://c:\eeg intro\eeg_intro.html
Seite 1 von 8 Written by Terence W. Picton, M.D., Ph.D. What is an electroencephalogram? When the neurons of the human brain process information, they do so by changing the flow of electrical currents
More informationArtifact Detection and Correction for Operator Functional State Estimation
Artifact Detection and Correction for Operator Functional State Estimation Chris A. Russell Air Force Research Laboratory Human Effectiveness Directorate 2255 H Street WrightPatterson AFB, OH 45433 christopher.russell@wpafb.af.mil
More informationAn Introduction to Beamforming in MEG and EEG. Stephanie Sillekens Skiseminar Kleinwalsertal Stephanie Sillekens Beamforming in EEG/MEG
An Introduction to Beamforming in MEG and EEG Stephanie Sillekens Skiseminar Kleinwalsertal 2008 Outline Introduction to EEG/MEG source analysis Basic idea of a Vector Beamformer Data Model Filter Design
More informationRobust Functional Mixed Models for Spatially Correlated Functional Regression
Robust Functional Mixed Models for Spatially Correlated Functional Regression  with Application to EventRelated Potentials for NicotineAddicted Individuals Hongxiao Zhu Virginia Tech June 15, 2015
More informationELECE8104 Stochastics models and estimation, Lecture 3b: Linear Estimation in Static Systems
Stochastics models and estimation, Lecture 3b: Linear Estimation in Static Systems Minimum Mean Square Error (MMSE) MMSE estimation of Gaussian random vectors Linear MMSE estimator for arbitrarily distributed
More informationIndependent Component Analysis and Its Applications TzyyPing Jung
Independent Component Analysis and Its Applications TzyyPing Jung Swartz Center for Computational Neuroscience Institute for Neural Computation University of California, San Diego & Department of Computer
More informationClassic EEG (ERPs)/ Advanced EEG. Quentin Noirhomme
Classic EEG (ERPs)/ Advanced EEG Quentin Noirhomme Outline Origins of MEEG Event related potentials Time frequency decomposition i Source reconstruction Before to start EEGlab Fieldtrip (included in spm)
More informationEvent Related Potentials in terms of Visual and Auditory Stimuli
Event Related Potentials in terms of Visual and Auditory Stimuli Seokbeen Lim, KyeongSeok Sim, DaKyeong Shin, Gilwon Yoon Abstract Eventrelated potential (ERP) is one of the useful tools for investigating
More informationNeural Decoding of Cursor Motion Using a Kalman Filter
Neural Decoding of Cursor Motion Using a Kalman Filter W. Wu M. J. Black Y. Gao E. Bienenstock M. Serruya A. Shaikhouni J. P. Donoghue Division of Applied Mathematics, Dept. of Computer Science, Dept.
More informationConsiderations When Using SingleTrial Parameter Estimates in Representational Similarity Analyses
Considerations When Using SingleTrial Parameter Estimates in Representational Similarity Analyses Jeanette A. Mumford, PhD Department of Psychology University of Texas at Austin Austin, Texas Considerations
More informationComponent Ordering in Independent Component Analysis Based on Data Power
Component Ordering in Independent Component Analysis Based on Data Power Anne Hendrikse Raymond Veldhuis University of Twente University of Twente Fac. EEMCS, Signals and Systems Group Fac. EEMCS, Signals
More informationFunctional Magnetic Resonance Imaging (fmri) Modelling: Observing the brain in action
Functional Magnetic Resonance Imaging (fmri) Modelling: Observing the brain in action Peter Drysdale Complex Systems, School of Physics, University of Sydney Brain Dynamics Center, Westmead Millennium
More informationKristine L. Bell and Harry L. Van Trees. Center of Excellence in C 3 I George Mason University Fairfax, VA 220304444, USA kbell@gmu.edu, hlv@gmu.
POSERIOR CRAMÉRRAO BOUND FOR RACKING ARGE BEARING Kristine L. Bell and Harry L. Van rees Center of Excellence in C 3 I George Mason University Fairfax, VA 220304444, USA bell@gmu.edu, hlv@gmu.edu ABSRAC
More informationAnalysis of Bayesian Dynamic Linear Models
Analysis of Bayesian Dynamic Linear Models Emily M. Casleton December 17, 2010 1 Introduction The main purpose of this project is to explore the Bayesian analysis of Dynamic Linear Models (DLMs). The main
More informationAUDITORY ATTENTION DECODING WITH EEG RECORDINGS USING NOISY ACOUSTIC REFERENCE SIGNALS.
AUDITORY ATTENTION DECODING WITH EEG RECORDINGS USING NOISY ACOUSTIC REFERENCE SIGNALS Ali Aroudi 1, Bojana Mirkovic 2, Maarten De Vos 3, Simon Doclo 1 1 Department of Medical Physics and Acoustics, University
More informationPart 2: Kalman Filtering COS 323
Part 2: Kalman Filtering COS 323 OnLine Estimation Have looked at offline model estimation: all data is available For many applications, want best estimate immediately when each new datapoint arrives
More information11. Time series and dynamic linear models
11. Time series and dynamic linear models Objective To introduce the Bayesian approach to the modeling and forecasting of time series. Recommended reading West, M. and Harrison, J. (1997). models, (2 nd
More informationSurface Laplacian. Chapter 22 John JB Allen
Surface Laplacian Chapter 22 John JB Allen Is a Spatial Filter In fact, is the second spatial derivative of the potentials (change in acceleration over space) Increases topographical specificity Filters
More informationInvoluntary Processing of Timbre: A Mismatch Negativity Study
Vol. 13, No. 4, pp.116 11 Involuntary Processing of Timbre: A Mismatch Negativity Study Yongxiu Lai, Weiyi Ma, Shujing Duan, Dezhong Yao Key laboratory For NeuroInformation of Ministry of Education, School
More informationMethods in Cognitive Neuroscience. Methods for studying the brain. Single Cell Recording
Methods in Cognitive Neuroscience Dr. Sukhvinder Obhi Department of Psychology & Centre for Cognitive Neuroscience 1 Methods for studying the brain Single Cell Recording Lesion Method Human Psychophysiology
More informationThe Effect of Transient Detection Errors on RF Fingerprint Classification Performance
The Effect of Transient Detection Errors on RF Fingerprint Classification Performance MEMDUH KÖSE SELÇUK TAŞCIOĞLU ZİYA TELATAR Computer Sciences Research and Electrical and Electronics Engineering Department
More informationEE 570: Location and Navigation
EE 570: Location and Navigation OnLine Bayesian Tracking Aly ElOsery 1 Stephen Bruder 2 1 Electrical Engineering Department, New Mexico Tech Socorro, New Mexico, USA 2 Electrical and Computer Engineering
More informationABSTRACT 1. INTRODUCTION 2. ARTIFACT REJECTION ON RAW DATA
AUTOMATIC ARTIFACT REJECTION FOR EEG DATA USING HIGHORDER STATISTICS AND INDEPENDENT COMPONENT ANALYSIS A. Delorme, S. Makeig, T. Sejnowski CNL, Salk Institute 11 N. Torrey Pines Road La Jolla, CA 917,
More informationAdvanced Signal Processing and Digital Noise Reduction
Advanced Signal Processing and Digital Noise Reduction Saeed V. Vaseghi Queen's University of Belfast UK WILEY HTEUBNER A Partnership between John Wiley & Sons and B. G. Teubner Publishers Chichester New
More informationPLS (PARTIAL LEAST SQUARES ANALYSIS)
PLS (PARTIAL LEAST SQUARES ANALYSIS) Introduction Partial Least Squares (PLS) Analysis was first developed in the late 60 s by Herman Wold, and works on the assumption that the focus of analysis is on
More informationFunctional connectivity in fmri
Functional connectivity in fmri Cyril Pernet, PhD Language and Categorization Laboratory, Brain Research Imaging Centre, University of Edinburgh Studying networks fmri can be used for studying both, functional
More informationDimensionality Reduction with PCA
Dimensionality Reduction with PCA Ke Tran May 24, 2011 Introduction Dimensionality Reduction PCA  Principal Components Analysis PCA Experiment The Dataset Discussion Conclusion Why dimensionality reduction?
More informationState Space Models and the Kalman Filter
State Space Models and the Kalman Filter Paul Pichler Seminar paper prepared for 40461 Vektorautoregressive Methoden by Prof. Robert Kunst Januaray 2007 Contents 1 Introduction 2 2 State Space models 2
More informationInvestigating the Neurophysiological Effects of Direct Current Stimulation. Joaquin de Rojas Steven Siegel Roy Hamilton
Investigating the Neurophysiological Effects of Direct Current Stimulation Joaquin de Rojas Steven Siegel Roy Hamilton Doing summer research 1. Define area of interest Interface of Cognitive Neurology
More informationDISCRETE TIME OPTIMAL CONTROL & THE KALMAN FILTER
AUTOMATIC CONTROL AND SYSTEM THEORY DISCRETE TIME OPTIMAL CONTROL & THE KALMAN FILTER Gianluca Palli Dipartimento di Ingegneria dell Energia Elettrica e dell Informazione (DEI) Università di Bologna Email:
More informationA general representation for a matched filter is illustrated in Figure 31. FIGURE 31 Matched filter
MATCHED FILTERS The matched filter is the optimal linear filter for maximizing the signal to noise ratio (SNR) in the presence of additive stochastic noise. Matched filters are commonly used in radar,
More informationDynamic Changes in Effective Connectivity Characterized by Variable Parameter Regression and Kalman Filtering
Human Brain Mapping 6:403 408(1998) Dynamic Changes in Effective Connectivity Characterized by Variable Parameter Regression and Kalman Filtering Christian Büchel* and K.J. Friston Leopold Müller Functional
More informationERPs to words correlate with behavioral measures in children with Autism Spectrum Disorder
ERPs to words correlate with behavioral measures in children with Autism Spectrum Disorder S. CoffeyCorina a, D. Padden b and P. K. Kuhl c a Center for Mind and Brain UC Davis, 267 Cousteau Pl, Davis,
More informationThe Deceptively Simple N170 Reflects Network Information Processing Mechanisms Involving Visual Feature Coding and Transfer Across Hemispheres
Cerebral Cortex, November 26;26: 423 435 doi:.93/cercor/bhw96 Advance Access Publication Date: 22 August 26 Original Article ORIGINAL ARTICLE The Deceptively Simple N7 Reflects Network Information Processing
More informationCognitive Functions: Principles, Assessment Techniques and Implications
Cognitive Functions: Principles, Assessment Techniques and Implications Tharaka Dassanayake MBBS MPhil (SL) PhD (Aus) Senior Lecturer in Physiology, University of Peradeniya Conjoint Senior Lecturer in
More information6.801/866. Tracking with Linear Dynamic Models. T. Darrell
6.801/866 Tracking with Linear Dynamic Models T. Darrell Tracking Applications Motion capture Recognition from motion Surveillance Targeting Things to consider in tracking What are the Real world dynamics
More informationThe Likelihood, the prior and Bayes Theorem
The Likelihood, the prior and Bayes Theorem Douglas Nychka, www.image.ucar.edu/~nychka Likelihoods for three examples. Prior, Posterior for a Normal example. Priors for Surface temperature and the CO 2
More informationThe ERP Boot Camp! ERP Localization!
The! ERP Localization! All slides S. J. Luck, except as indicated in the notes sections of individual slides! Slides may be used for nonprofit educational purposes if this copyright notice is included,
More informationCognitive Neuroscience
Cognitive Neuroscience Exploring Brain/Behavior relations Neuroscience Psychology Cognitive Neuroscience Computational Sciences / Artificial intelligence Franz Joseph Gall & J. C. Spurzheim localization
More informationAn Introduction to ERP Studies of Attention
An Introduction to ERP Studies of Attention Logan Trujillo, Ph.D. PostDoctoral Fellow University of Texas at Austin Cognitive Science Course, Fall 2008 What is Attention? Everyone knows what attention
More informationBrain Evoked Potential Latencies Optimization for Spatial Auditory Brain Computer Interface
DOI 1.17/s15591398x Brain Evoked Potential Latencies Optimization for Spatial Auditory Brain Computer Interface Zhenyu Cai Shoji Makino Tomasz M. Rutkowski Received: 11 December 1 / Accepted: 5 July
More informationParametric Models Part I: Maximum Likelihood and Bayesian Density Estimation
Parametric Models Part I: Maximum Likelihood and Bayesian Density Estimation Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr CS 551, Fall 2015 CS 551, Fall 2015
More informationAn Introduction to Kalman Filtering. Eric Wan Rudolph van der Merwe, Alex T. Nelson, Anindya Paul
An Introduction to Kalman Filtering Eric Wan Rudolph van der Merwe, Ale T. Nelson, Anindya Paul Overview Introduction & Bacground What is a Kalman Filter? Probabilistic Inference & Optimal Estimation StateSpace
More informationThe neural bases of infant attention. John E. Richards. University of South Carolina. Greg D. Reynolds. University of Tennessee. Mary L.
Neural bases of infant attention development 1 The neural bases of infant attention John E. Richards University of South Carolina Greg D. Reynolds University of Tennessee Mary L. Courage Memorial University
More informationBiobehavioral Correlates of Autism Spectrum Disorder in Infants with Fragile X Syndrome
Biobehavioral Correlates of Autism Spectrum Disorder in Infants with Fragile X Syndrome Jane E. Roberts, Ph.D., Bridgette Tonnsen, M.A., Margaret Guy, Ph.D., Laura Hahn, Ph.D., & John E. Richards, Ph.D.
More informationIntroduction to Neuroimaging Data. Phebe Kemmer BIOS 516 Sept 17, 2015
Introduction to Neuroimaging Data Phebe Kemmer BIOS 516 Sept 17, 2015 The Human Brain Controls all body activities Heart rate, breathing, sexual function Motor activities and senses Learning, memory, language
More informationThe Brain Machine Interface:
The Brain Machine Interface: Alan S. Rudolph DEC 2001 DSO Explore Thrust Areas in Biology to Capture New Paradigm Shifts Controlled BioSystems Cell And Tissue Sensors Metabolic Engineering Stasis Advanced
More informationAN ITERATIVE EIGENDECOMPOSITION APPROACH TO BLIND SOURCE SEPARATION. Ana Maria Tomé
AN ITERATIVE EIGENDECOMPOSITION APPROACH TO BLIND SOURCE SEPARATION Ana Maria Tomé Dep. Electrónica e Telecomunicações/IEETA Universidade AveiroAveiroPortugal email:ana@ieeta.pt ABSTRACT In this work
More informationParameter Estimation for Linear Dynamical Systems. Zoubin Ghahramani. Georey E. Hinton. University of Toronto. Toronto, Canada M5S 1A4
Parameter Estimation for Linear Dynamical Systems Zoubin Ghahramani Georey E. Hinton Department of Computer Science University of Toronto 6 King's College Road Toronto, Canada M5S A4 Email: zoubin@cs.toronto.edu
More informationEnsemble SWLDA Classifiers for the P300 Speller
Ensemble SWLDA Classifiers for the P300 Speller Garett D. Johnson and Dean J. Krusienski University of North Florida Jacksonville, FL 32224 dean.krusienski@unf.edu Abstract. The P300 Speller has proven
More informationThe Unscented Kalman Filter for Nonlinear Estimation
The Unscented Kalman Filter for Nonlinear Estimation Eric A. Wan and Rudolph van der Merwe Oregon Graduate Institute of Science & Technology 2 NW Waler Rd, Beaverton, Oregon 976 ericwan@ece.ogi.edu, rvdmerwe@ece.ogi.edu
More informationICAbased artifact removal in EEG
ICAbased artifact removal in EEG John J.B. Allen http://jallen.faculty.arizona.edu/ica_workshop Würzburg Workshop, 26 June, 2015 ICAbased artifact removal in EEG John J.B. Allen http://jallen.faculty.arizona.edu/ica_workshop
More informationNIH Public Access Author Manuscript Conf Proc IEEE Eng Med Biol Soc. Author manuscript; available in PMC 2012 August 1.
NIH Public Access Author Manuscript Published in final edited form as: Conf Proc IEEE Eng Med Biol Soc. 2011 August ; 2011: 4725 4728. doi:10.1109/iembs.2011.6091170. Robust TimeVarying Multivariate Coherence
More informationfmri Classification of Cognitive States Across Multiple Subjects
fmri Classification of Cognitive States Across Multiple Subjects Lalla Mouatadid Department of Computer Science University of Toronto Toronto, ON M5R 0A3 lalla@cs.toronto.edu Abstract With the evolvement
More informationSURELET InterscaleIntercolor Wavelet Thresholding for Color Image Denoising
SURELET InterscaleIntercolor Wavelet Thresholding for Color Image Denoising Florian Luisier a and Thierry Blu a a Biomedical Imaging Group (BIG), Swiss Federal Institute of Technology (EPFL), Lausanne,
More informationTracking Algorithms. Lecture17: Stochastic Tracking. Joint Probability and Graphical Model. Probabilistic Tracking
Tracking Algorithms (2015S) Lecture17: Stochastic Tracking Bohyung Han CSE, POSTECH bhhan@postech.ac.kr Deterministic methods Given input video and current state, tracking result is always same. Local
More information5 Summary of Results The novel tones also elicited a sequence of deflection comprised of N1, N2 and P3 (Fig. 6). In children, noveltyelicited N2 responses were larger to the left ear stimuli irrespective
More informationJava Modules for Time Series Analysis
Java Modules for Time Series Analysis Agenda Clustering Nonnormal distributions Multifactor modeling Implied ratings Time series prediction 1. Clustering + Cluster 1 Synthetic Clustering + Time series
More informationOnline Chapter 11 A Closer Look at Averaging: Convolution, Latency Variability, and Overlap
Overview Online Chapter 11 A Closer Look at Averaging: Convolution, Latency Variability, and Overlap You might think that we discussed everything there is to know about averaging in the chapter on averaging
More informationON and OFF Pathways of Ganglion Cells in the Salamander Retina
ON and OFF Pathways of Ganglion Cells in the Salamander Retina Bongsoo Suh Department of Electrical Engineering, Stanford University bssuh@stanford.edu One of the main goals in neuroscience is to explain
More informationData Analysis Methods: Net Station 4.1 By Peter Molfese
Data Analysis Methods: Net Station 4.1 By Peter Molfese Preparing Data for Statistics (preprocessing): 1. Rename your files to correct any typos or formatting issues. a. The General format for naming files
More informationBESA Research Tutorial 3: Batch Scripts, Multiple Subjects & Conditions, MATLABInterface
BESA Research Tutorial 3: A. Introduction: The data and the paradigm file 3 B. Batch Scripts for individual & grand averages and combining conditions 7 Using batch scripts to create individual averages
More informationAdaptive DemandForecasting Approach based on Principal Components Timeseries an application of datamining technique to detection of market movement
Adaptive DemandForecasting Approach based on Principal Components Timeseries an application of datamining technique to detection of market movement Toshio Sugihara Abstract In this study, an adaptive
More informationECE 645 Kalman Filter
ECE 645 Kalman Filter J. V. Krogmeier April 21, 2014 Contents 1 Background References 3 2 State Space Model 3 3 Statistical Assumptions 5 4 Problem 6 5 Properties of the State 8 1 6 Formulas for Mean and
More informationTHE MEASUREMENT OF MOTOR PERFORMANCE. Chapter 2 1
THE MEASUREMENT OF MOTOR PERFORMANCE Chapter 2 1 THIS CHAPTER S CONCEPT The measurement of motor performance is critical to understanding motor learning & development Chapter 3 2 PERFORMANCE OUTCOME MEASURES
More informationIMAGE SEGMENTATION AND BIAS FIELD CORRECTION ON BRAIN MRI IMAGES
IMAGE SEGMENTATION AND BIAS FIELD CORRECTION ON BRAIN MRI IMAGES K.manimala 1 PG Scholar, Department of Electronics & Communication Engineering K.S.Rangasamy College of Technology, Tiruchengode, Namakkal,
More informationCondition Monitoring, Fault Diagnostics and Prognostics of Industrial Equipment. Enrico Zio
Condition Monitoring, Fault Diagnostics and Prognostics of Industrial Equipment Enrico Zio PROGNOSTICS AND HEALTH MANAGEMENT (PHM) PHM: what PHM = DDP = = + + Detect Diagnose Predict Normal operation f
More informationChapter 10 Introduction to Time Series Analysis
Chapter 1 Introduction to Time Series Analysis A time series is a collection of observations made sequentially in time. Examples are daily mortality counts, particulate air pollution measurements, and
More informationModifiedCS: Modifying Compressive Sensing for Problems with Partially Known Support
ModifiedCS: Modifying Compressive Sensing for Problems with Partially Known Support Namrata Vaswani and Wei Lu ECE Dept., Iowa State University, Ames, IA 50011, USA, Email: namrata@iastate.edu Abstract
More informationAutomated Classification of EEG Signals Using Component Analysis and Support Vector Machines
Grand Valley State University ScholarWorks@GVSU Masters Theses Graduate Research and Creative Practice 122014 Automated Classification of EEG Signals Using Component Analysis and Support Vector Machines
More informationLecture 11: Introduction to Random Processes cont d
Lecture 11: Introduction to Random Processes cont d ELE 525: Random Processes in Information Systems Hisashi Kobayashi Department of Electrical Engineering Princeton University October 16, 2013 Textbook:
More information