Feature Extraction, Feature Selection and Dimensionality Reduction Techniques for Brain Computer Interfaces By Tian Lan A thesis submitted to the Department of Biomedical Engineering of the Oregon Health & Science University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering July 2011 © Copyright 2011 by Tian Lan All Rights Reserved ii The Dissertation “Feature extraction, feature selection and dimensionality reduction techniques for Brain Computer Interfaces” by Tian Lan has been examined and approved by the following Examination Committee: Peter A. Heeman Associate Research Professor Department of Biomedical Engineering, OHSU Deniz Erdogmus Assistant Professor Northeastern University Xubo Song Associate Research Professor Department of Biomedical Engineering, OHSU Brian Roark Associate Professor Department of Biomedical Engineering, OHSU Jennifer G.Dy Associate Professor Northeastern University iii Contents Acknowledgements .......................................................................................................................................ix Abstract .......................................................................................................................................................... x Chapter 1: Introduction ................................................................................................................................ 1 1.1 Brain Computer Interfaces .................................................................................................................... 1 1.2 EEG based BCI ..................................................................................................................................... 2 1.3 BCI Applications .................................................................................................................................. 3 1.3.1 Sensorimotor Control .................................................................................................................... 3 1.3.2 Neuromuscular Disorders Rehabilitation ...................................................................................... 3 1.3.3 Augmented Cognition ................................................................................................................... 4 1.2.4 Target Recognition using Single Trial ERP Detection ................................................................. 4 1.4 Machine Learning Perspective of BCIs ................................................................................................ 5 1.5 Problem Definition ............................................................................................................................... 6 1.6 Thesis Statements ................................................................................................................................. 7 1.7 Thesis Contributions ............................................................................................................................. 8 1.8 Thesis Structure .................................................................................................................................... 9 Chapter 2: Background and Related Work .............................................................................................. 11 2.1 Introduction ........................................................................................................................................ 11 2.2 Pre-processing .................................................................................................................................... 11 2.2.1 Filtering ....................................................................................................................................... 11 2.2.2 Normalization ............................................................................................................................. 12 2.2.3 Artifacts Removal ....................................................................................................................... 13 2.3 Feature Extraction............................................................................................................................... 13 2.3.1 Time Domain Feature Extraction ................................................................................................ 14 2.3.2 Frequency Domain Feature Extraction ....................................................................................... 14 2.3.3 Time-Frequency Domain Feature Extraction .............................................................................. 14 2.4 Feature Selection and Dimensionality Reduction ............................................................................... 15 2.4.1 Feature Selection ......................................................................................................................... 16 2.4.2 Dimensionality Reduction .......................................................................................................... 16 2.5 Classification ...................................................................................................................................... 17 2.5.1 Linear Discriminant Analysis (LDA) Classifier ......................................................................... 17 iv 2.5.2 Support Vector Machine (SVM) Classifier ................................................................................. 18 2.5.3 MultiLayer Perceptron (MLP) Neural Networks ........................................................................ 18 2.5.4 Hidden Markov Model (HMM) .................................................................................................. 19 2.5.5 K-Nearest Neighbor (KNN) Classifier ....................................................................................... 19 2.5.6 Combinations of classifiers ......................................................................................................... 19 2.6 Post-processing ................................................................................................................................... 20 2.7 Summary............................................................................................................................................. 20 Chapter 3: Augmented Cognition .............................................................................................................. 21 3.1 EEG Data Collection .......................................................................................................................... 22 3.1.1 Mental Tasks ............................................................................................................................... 23 3.2.2 Hardware Platform ...................................................................................................................... 23 3.2 Signal Processing Module .................................................................................................................. 25 3.2.1 Pre-processing ............................................................................................................................. 26 3.2.2 Feature Extraction ....................................................................................................................... 26 3.3 Cognitive State Classification ............................................................................................................. 27 3.3.1 Gaussian Mixture Models (GMM) Classifier ............................................................................. 27 3.3.2 K-Nearest Neighbor (KNN) Classifier ....................................................................................... 29 3.3.3 Parzen Window Classifier ........................................................................................................... 29 3.3.4 Composite Classifier ................................................................................................................... 29 3.4 Experimental Results .......................................................................................................................... 30 3.5 Discussion ........................................................................................................................................... 31 Chapter 4: Linear Methods for Feature Selection and Dimensionality Reduction ............................... 32 4.1 Background ......................................................................................................................................... 32 4.1.1 Linear Projection vs. Non-linear Projection ................................................................................ 33 4.1.2 Wrapper Approach vs. Filter Approach ...................................................................................... 34 4.1.3 Feature Selection and Dimensionality Reduction Criterion - Mutual Information (MI) ............. 34 4.2 Mutual Information Estimation .......................................................................................................... 35 4.2.1 ICA-MI Mutual Information Estimation Framework ................................................................. 36 4.2.2 Linear ICA Solution .................................................................................................................... 38 4.2.3 Marginal Entropy Estimator ....................................................................................................... 39 4.3