UNIVERSITY OF CALIFORNIA, SAN DIEGO Combining EEG Source Dynamics Results across Subjects, Studies and Cognitive Events A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Electrical and Computer Engineering (Intelligent Systems, Robotics, and Control) by Nima Bigdely-Shamlo Committee in charge: Professor Kenneth Kreutz-Delgado, Chair Professor Virginia De Sa Professor Gert Lanckriet Professor Scott Makeig Professor Nuno Vasconcelos 2014 Copyright Nima Bigdely-Shamlo, 2014 All rights reserved. The Dissertation of Nima Bigdely-Shamlo is approved, and it is acceptable in quality and form for publication on microfilm and electronically: ______________________________________________ ______________________________________________ ______________________________________________ Chair UNIVERSITY OF CALIFORNIA, SAN DIEGO 2014 iii DEDICATION To Audrey Buss, as without her encouragement and kind support this journey would not be possible. iv TABLE OF CONTENTS Signature Page……………………………………………………………………...…iii Dedication……………………………………………………………………………..iv Table of Contents……………………………………………………………………....v List of Figures…………………………………………………………………...…...xiv List of Tables………………………………………………………………………...xix Acknowledgements………………………………………………………......……….xx Vita…………………………………………………………………………………xxiii Abstract……………………………………………………………………………...xxv Chapter 1 Introduction ....................................................................................... 1 Chapter 2 EEGLAB Workflow.......................................................................... 7 2.1 Introduction ...................................................................................................... 7 2.2 Application of ICA in EEG Analysis .............................................................. 7 2.3 Equivalent Dipole Model for ICs..................................................................... 8 2.4 EEGLAB Workflow ...................................................................................... 10 2.5 Acknowledgments ......................................................................................... 13 2.6 Figures ........................................................................................................... 15 Chapter 3 EEG Independent Component Polarity Normalization by Two-way Partitioning ............................................................................................................ 17 3.1 Abstract .......................................................................................................... 17 3.2 Introduction .................................................................................................... 18 3.3 Methods ......................................................................................................... 20 v 3.3.1 Problem Description ............................................................................... 21 3.3.2 Convex solution ...................................................................................... 22 3.3.3 Monte Carlo solution .............................................................................. 23 3.4 Results ............................................................................................................ 25 3.5 Discussion and Conclusion ............................................................................ 30 3.6 Acknowledgments ......................................................................................... 32 3.7 Figures ........................................................................................................... 32 Chapter 4 Detecting Eye Activity Related ICs (EyeCatch) ............................. 38 4.1 Abstract .......................................................................................................... 38 4.2 Introduction .................................................................................................... 39 4.3 Methods ......................................................................................................... 40 4.3.1 Scalp maps Database Preprocessing ....................................................... 40 4.3.2 Eye-related template scalp map dataset .................................................. 40 4.4 Results ............................................................................................................ 42 4.5 Conclusions .................................................................................................... 43 4.6 Acknowledgments ......................................................................................... 44 4.7 Figures ........................................................................................................... 45 Chapter 5 Imaging half a million ICA-component scalp maps reveals EEG source hotspots ................................................................................................................ 49 5.1 Introduction .................................................................................................... 49 5.2 Methods ......................................................................................................... 50 5.2.1 Data Preprocessing ................................................................................. 50 vi 5.2.2 Dipole Density Calculation .................................................................... 51 5.2.3 Calculating Average Dipole Residual Variance ..................................... 52 5.2.4 Calculating Average Dipole Orientation ................................................ 53 5.2.5 Defining the radial vector direction for each voxel ................................ 55 5.3 Results ............................................................................................................ 56 5.4 Discussion ...................................................................................................... 57 5.5 Acknowledgements ........................................................................................ 59 5.6 Figures ........................................................................................................... 60 Chapter 6 Measure Projection Analysis: A Probabilistic Approach to EEG Source Comparison and Multi-Subject Inference ........................................................ 78 6.1 Abstract .......................................................................................................... 78 6.2 Introduction .................................................................................................... 80 6.3 Methods ......................................................................................................... 86 6.3.1 Experimental data ................................................................................... 87 6.3.2 Subject task ............................................................................................. 87 6.3.3 Data preprocessing. ................................................................................ 87 6.3.4 ERSP measure projection ....................................................................... 88 6.3.5 ERSP domain clustering ......................................................................... 91 6.3.6 PCA-based IC clustering ........................................................................ 92 6.3.7 PCA-based ERSP measure clustering .................................................... 94 6.4 Results ............................................................................................................ 95 6.4.1 ERSP measures for PCA-based IC clusters ............................................ 95 vii 6.4.2 ERSP measure projection results ............................................................ 96 6.4.3 Comparison of MPA and PCA-based clustering methods ..................... 97 6.4.4 Simulation ............................................................................................. 100 6.5 Discussion .................................................................................................... 103 6.5.1 Relative Parsimony ............................................................................... 104 6.5.2 Source Measure Consistency ................................................................ 106 6.5.3 Source Clustering Coherency ............................................................... 107 6.5.4 Cluster membership .............................................................................. 108 6.5.5 Cluster shape ........................................................................................ 109 6.5.6 Cluster equivalence across measures .................................................... 110 6.5.7 Subject comparisons ............................................................................. 111 6.5.8 Group-Level ICA decomposition ......................................................... 112 6.6 Conclusion ................................................................................................... 116 6.7 Acknowledgements ...................................................................................... 116 6.8 Figures ......................................................................................................... 118 6.9 Appendix A - Measure Projection Analysis (MPA) Method Description ... 128 6.10 Appendix B - Threshold-based Clustering and Outlier Rejection using Affinity Propagation ..............................................................................................