Spatio temporal approaches to denoising and feature extraction in rapid image triage

SPATIO-TEMPORAL APPROACHES TO DENOISING AND FEATURE EXTRACTION IN RAPID IMAGE TRIAGE YU KE (B.Eng., Zhejiang University) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF BIOENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2012 i Acknowledgments I would like to express my sincere gratefulness to my supervisor Professor Li Xiaoping, Director of Neuroengineering Laboratories, for his generous sharing, encouraging attitude, insightful vision and enlightening guidance. It is my pleasure to enjoy a wonderful 4-year study with so many amazing lab mates, Dr. Shen Kaiquan, Dr. Ng Wu Chun, Dr. Fan Jie, Dr. Ning Ning, Dr. Shao Shiyun, Dr. Wu Xiang, Mr. Khoa Wei Long Geoffrey, Mr. Wu Ji, Mr. Rohit Tyagi, Mr. Bui Ha Duc, Miss Wang Yue, Miss Ye Yan and Mr. Wu Tiecheng. I benefit a lot from their selfless support and valuable suggestions, and would like to take this opportunity to show my deep thankfulness. Special acknowledgments are given to my parents. Their love accompanies me whenever and wherever I am. Last but not the least, I am very grateful to the National University of Singapore for granting me the financial support, with which I can endeavor myself to this doctoral research. NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE ii Table of Contents Acknowledgments i Summary vii List of Tables xi List of Figures xvii List of Symbols xviii Acronyms xxi Introduction 1.1 A Snapshot of Image Screening Strategies . . . . . . . . . . . . 1.1.1 Artificial intelligence based . . . . . . . . . . . . . . . 1.1.2 Human intelligence oriented . . . . . . . . . . . . . . . 1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE TABLE OF CONTENTS Literature Review 2.1 2.2 2.3 2.4 2.5 iii 10 EEG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1.1 Physiological background . . . . . . . . . . . . . . . . 11 2.1.2 Technical background . . . . . . . . . . . . . . . . . . 12 2.1.3 Event-related potentials . . . . . . . . . . . . . . . . . 17 Brain Computer Interface . . . . . . . . . . . . . . . . . . . . . 23 2.2.1 Invasive BCI . . . . . . . . . . . . . . . . . . . . . . . 23 2.2.2 Noninvasive BCI . . . . . . . . . . . . . . . . . . . . . 25 EEG Signal Processing Methods . . . . . . . . . . . . . . . . . 29 2.3.1 Signal modeling . . . . . . . . . . . . . . . . . . . . . 30 2.3.2 Denoising . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.3.3 Feature extraction . . . . . . . . . . . . . . . . . . . . . 36 2.3.4 Classification . . . . . . . . . . . . . . . . . . . . . . . 39 Rapid Image Triage . . . . . . . . . . . . . . . . . . . . . . . . 41 2.4.1 Rationale of RIT . . . . . . . . . . . . . . . . . . . . . 41 2.4.2 Past work on RIT . . . . . . . . . . . . . . . . . . . . . 43 Mathematical Supplement . . . . . . . . . . . . . . . . . . . . 50 2.5.1 Common spatial pattern . . . . . . . . . . . . . . . . . 50 2.5.2 Weighted support vector machine . . . . . . . . . . . . 52 Rapid Image Triage System NATIONAL UNIVERSITY OF SINGAPORE 59 SINGAPORE TABLE OF CONTENTS 3.1 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.2 RIT for Online Broad-area Imagery Screening . . . . . . . . . . 61 3.3 iv 3.2.1 Image preparation . . . . . . . . . . . . . . . . . . . . 61 3.2.2 Experimental procedure . . . . . . . . . . . . . . . . . 64 3.2.3 Data processing . . . . . . . . . . . . . . . . . . . . . . 67 3.2.4 Broad-area imagery screening . . . . . . . . . . . . . . 67 Real-life experiments . . . . . . . . . . . . . . . . . . . . . . . 68 3.3.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . 69 3.3.2 Experimental paradigm . . . . . . . . . . . . . . . . . . 69 3.3.3 Data acquisition . . . . . . . . . . . . . . . . . . . . . 70 A Spatio-Temporal Filtering Approach to Denoising of Single-Trial ERP in Rapid Image Triage 72 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.2 Proposed Spatio-Temporal Filtering Approach . . . . . . . . . . 76 4.3 4.2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . 76 4.2.2 Spatial filtering . . . . . . . . . . . . . . . . . . . . . . 77 4.2.3 Spatio-temporal filtering . . . . . . . . . . . . . . . . . 78 4.2.4 Estimating the ERP latency difference between channels 79 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.3.1 Simulation tests . . . . . . . . . . . . . . . . . . . . . . 81 NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE TABLE OF CONTENTS 4.3.2 4.4 4.5 v Real-life RIT experiments . . . . . . . . . . . . . . . . 82 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . 84 4.4.1 Simulation test I . . . . . . . . . . . . . . . . . . . . . 84 4.4.2 Simulation test II . . . . . . . . . . . . . . . . . . . . . 85 4.4.3 Simulation test III . . . . . . . . . . . . . . . . . . . . 88 4.4.4 Real RIT experiments . . . . . . . . . . . . . . . . . . 90 4.4.5 Future work . . . . . . . . . . . . . . . . . . . . . . . . 95 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . 96 Common Spatio-Temporal Pattern for Single-Trial Detection of ERP in Rapid Image Triage 97 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.2 Single-Trial ERP Detection . . . . . . . . . . . . . . . . . . . . 100 5.3 5.4 5.2.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . 100 5.2.2 Common spatio-temporal pattern method . . . . . . . . 101 5.2.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 105 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 5.3.1 Filters and patterns . . . . . . . . . . . . . . . . . . . . 106 5.3.2 Classification . . . . . . . . . . . . . . . . . . . . . . . 109 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 5.4.1 Scenario I . . . . . . . . . . . . . . . . . . . . . . . . . 111 NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE TABLE OF CONTENTS 5.4.2 5.5 Scenario II . . . . . . . . . . . . . . . . . . . . . . . . 115 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . 116 Bilinear Common Spatial Pattern for Single-Trial Detection of ERP in Rapid Image Triage vi 117 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 6.2 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . 119 6.2.1 Common spatial pattern . . . . . . . . . . . . . . . . . 119 6.2.2 Bilinear common spatial pattern . . . . . . . . . . . . . 121 6.2.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 125 6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 6.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . 132 Conclusions and Recommendations 134 7.0.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . 134 7.0.2 Recommendations . . . . . . . . . . . . . . . . . . . . 137 Author’s Publications 139 Bibliography 141 NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE vii Summary Nowadays, along with the advances in imaging and storage technology, there has been an accentuated contradiction between the fast increasing sets of largevolume imagery and limited number of skilled image analysts. Rapid image triage (RIT) which leverages split-second human perceptual judgement via the interpretation of electroencephalogram (EEG) signals, can effectively improve the efficiency of imagery screening. This thesis is mainly concerned with developing novel single-trial EEG signal processing methods which are the backbone of RIT system, to augment visual target object detection. These novel singletrial methods are characterized by explicitly exploiting the spatio-temporal propagation of event related potentials (ERP) across the scalp, which are particularly informative for ERP detection. Improvements regarding the RIT protocol are also taken into account. The measured scalp EEG signals are always contaminated by physiological artifacts and environmental artifacts. These artifacts are of much stronger amplitude than the EEG signals and thus significantly deteriorate the decoding of informative cerebral signals. In this work, a non-sophisticated and highly effective denoising approach is put forward to strengthen the signal-to-noise ratio (SNR). NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE SUMMARY viii The approach performs spatial smoothing in a temporally adjusted space, in which noises become less correlated and can be easily suppressed, while retaining inherent signals. The results from simulation experiments and real-life experiments indicate that the proposed approach is well suited for RIT. Single-trial feature extraction serves as an important mean of counteracting “curse of dimensionality” which refers to the situation that a large volume of data is required to achieve statistical significant result in a high-dimensional space. By solely preserving underlying meaningful features, the RIT system is less vulnerable to irrelevant and misleading information. Hence the optimization problem in RIT can be greatly simplified. This work implements two single-trial feature extraction methods, extending the common spatial pattern analysis (CSP) to accommodate additional temporal structures. The incorporation of discriminative temporal information has been proven to be meaningful and very effective as demonstrated in the comparison with competing methods on real-life experiments. The real-life experiments were conducted on the developed near real-time RIT system, which is primarily designed for online broad-area imagery screening. The RIT system integrates software platform with hardware devices. It streamlines the procedures and is characterized by an image analyst-centric protocol: 1) centering visual objects for convenient observation; 2) maintaining the spatial information flow of imagery so as to avoid eliciting interfering brain signals. 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NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE [...]... effective triage techniques for rapid high-volume imagery screening during recent years Triage is a preliminary process that identifies a subset of images containing mostly target objects and a few false positive images, which will be followed-up by further inspection Among those triage techniques, the brain NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE 1.1 A Snapshot of Image Screening Strategies 2 computer integrated... exploring and utilizing the discriminative temporal patterns of ERP for robust feature extraction; (3) managing the unbalanced classification problem • System-level (1) easing the difficulties of observing target objects during RIT experiments; (2) minimizing the interfering EEG responses elicited by distractors This doctoral research contributes to the development of single-trial EEG signal processing... automatically comparing his/her fingerprint with those in the database The screening method depends on extraction and comparison of individually unique biometric (Jain et al., 1999), such as the characteristics of ridges (Jain et al., 2007; Ji and Yi, 2008) and minutia points (Jea and Govindaraju, 2005; Jianjiang and Feng, 2008) Handwriting recognition is another well explored area, which makes sense in applications... evaluated and compared to some competing methods in simulated experiments and real-life RIT experiments Chapter 5 presents a new single-trial EEG feature extraction algorithm which acquires discriminative temporal information besides spatial information in reallife RIT experiments The relationship between the temporal information and spatial information is discussed in details NATIONAL UNIVERSITY OF SINGAPORE... intelligence in terms of serial processing speed, human intelligence is a marvel at other aspects, e.g learning and generalizing Human beings are born with the abilities of conscious thinking, associating and reasoning as well as sub-conscious feeling In addition, they are able to grasp and recognize the gist of an image just at a brief glance For these reasons, human vision system has always been a meaningful... concerned with developing novel single-trial EEG processing methods which are capable of granting the brain computer integrated rapid image triage system sound ERP detection performance The specific objectives could be grouped into two parts: NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE 1.2 Objectives 7 • Algorithm-level (1) designing a novel denoising approach for enhancing the SNR in the context of RIT... order of image preparation Target is the image containing point of interest (POI) 63 3.4 Ordinary image preparation The rectangular in color stands for the image boundary 63 NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE LIST OF FIGURES 3.5 xii Image preparation with overlapping Adjacent images share a portion of imagery 64 3.6 Checking impedance... multivariate autoregressive PARAFAC parallel factor analysis PCA principle component analysis POI point of interest RIT rapid image triage RSVP rapid serial visual presentation SCP shifted CANDECOMP/PARAFAC model SNR signal -to- noise ratio SVM support vector machine WSVM weighted support vector machine NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE ACRONYMS 2D-G xxiii 2-dimensional Gaussian smoothing NATIONAL... integrated triage system which leverages human vision is very promising and is being intensively investigated, due to its unmatchable generalizing capabilities Since the human vision guided triage system heavily relies on the decoding of brain signals, i.e electroencephalogram (EEG), the key issue is to develop novel signal processing methods tailored for triage task 1.1 A Snapshot of Image Screening Strategies... processing capability of human vision, the screening of images under great NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE 1.1 A Snapshot of Image Screening Strategies 5 variations such as in scale, lighting and pose (bottlenecks for computer vision), is narrowed down and simplified to a binary discrimination problem: only two types of distinctive brain signals are to be differentiated, i.e event-related potentials . SPATIO-TEMPORAL APPROACHES TO DENOISING AND FEATURE EXTRACTION IN RAPID IMAGE TRIAGE YU KE (B.Eng., Zhejiang University) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT. 70 4 A Spatio-Temporal Filtering Approach to Denoising of Single-Trial ERP in Rapid Image Triage 72 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.2 Proposed Spatio-Temporal. analyst-centric protocol: 1) centering visual objects for convenient observation; 2) maintaining the spatial information flow of imagery so as to avoid eliciting interfering brain signals. The present work