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FIFER-DISSERTATION-2015.Pdf Mapping Sensorimotor Function and Controlling Upper Limb Neuroprosthetics with Electrocorticography by Matthew S. Fifer A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland September, 2015 © 2015 Matthew S. Fifer All Rights Reserved Abstract Electrocorticography (ECoG) occupies a unique intermediate niche between microelectrode recordings of single neurons and recordings of whole brain activity via functional magnetic resonance imaging (fMRI). ECoG’s combination of high temporal resolution and wide area coverage make it an ideal modality for both functional brain mapping and brain-machine interface (BMI) for control of prosthetic devices. This thesis demonstrates the utility of ECoG, particularly in high gamma frequencies (70-120 Hz), for passive online mapping of language and motor behaviors, online control of reaching and grasping of an advanced robotic upper limb, and mapping somatosensory digit representations in the postcentral gyrus. The dissertation begins with a brief discussion of the framework for neuroprosthetic control developed by the collaboration between Johns Hopkins and JHU Applied Physics Laboratory (JHU/APL). Second, the methodology behind an online spatial-temporal functional mapping (STFM) system is described. Trial- averaged spatiotemporal maps of high gamma activity were computed during a visual naming and a word reading task. The system output is subsequently shown and compared to stimulation mapping. Third, simultaneous and independent ECoG- based control of reaching and grasping is demonstrated with the Modular Prosthetic Limb (MPL). The STFM system was used to identify channels whose high gamma power significantly and selectively increases during either reaching or grasping. Using this technique, two patients were able to rapidly achieve naturalistic control over simple movements by the MPL. Next, high-density ECoG (hdECoG) was used to ii map the cortical responses to mechanical vibration of the fingertips. High gamma responses exhibited a strong yet overlapping somatotopy that was not well replicated in other frequency bands. These responses are strong enough to be detected in single trials and used to classify the finger being stimulated with over 98% accuracy. Finally, the role of ECoG is discussed for functional mapping and BMI applications. ECoG occupies a unique role among neural recording modalities as a tool for functional mapping, but must prove its value relative to stimulation mapping. For BMI, ECoG lags microelectrode arrays but hdECoG may provide a more robust long-term interface with optimal spacing for sampling relevant cortical representations. Primary Reader: Nathan E. Crone, M.D. Secondary Reader: Nitish V. Thakor, Ph.D. iii Acknowledgements I would like to offer my sincerest gratitude to my advisor, Dr. Nathan Crone, and my thesis committee members, Dr. Nitish Thakor and Dr. Stan Anderson. Thank you for sharing your world-class expertise with me and for providing me with invaluable support and feedback throughout my Ph.D. I would like to thank my fellow students and colleagues at Johns Hopkins for the friendship, support, and intellectual camaraderie that made my Ph.D. such a good experience. To members—past and present—of Crone Lab and the BCI Group, thank you for your guidance and input. You have not only profoundly shaped and contributed to this thesis work, but have also made coming to work fun. To my friends and family, thank you for your support and persistent interest in my work. Thank you for your understanding when my studies kept me away, and thank you for being a source of emotional nourishment during the last leg of my educational journey. You have made the Baltimore area feel like home. To Allison, Audrey, and Anna: thank you for being the best little sisters that a big brother could ask for. I wish I could have been around more in your last years at home, but I have cherished every single day that I have been able to spend with you. I’m extremely proud of all of you and look forward to watching you continue to grow into the smart, beautiful, amazing women that I have always known you would be. To Mom and Dad: thank you for enabling me to go to college and for convincing me to pursue a Ph.D. Thank you for never accepting less than my best, iv and for understanding when my best just wasn’t enough. Thank you for being great sounding boards on all issues and for remaining, somehow, genuinely interested in hearing about my studies. Knowing that I am making you proud has been a constant source of inspiration, not infrequently a lone beacon of encouragement at times when I wondered whether I belonged in the program. Last, and most importantly, I would like to thank my amazing wife and the love of my life, Erin. You have been a rock throughout all the unpredictable schedules, the ups and downs of the Ph.D. process, the stress of each milestone, and the general uncertainty about our future plans. Thank you for challenging me and loving me; please don’t ever stop. I love you so much! v Dedication I dedicate this thesis to my godparents, Uncle Bill and Aunt Fran. Thank you for helping me to get my start in this field, and for believing in me. I hope that I am making you proud. I would also like to dedicate this thesis to my grandfather, Floyd Ritchie. You have made it possible for two generations of your family to pursue their educations, and thus, their dreams. Thank you for everything that you have done for us and me. vi Summary and Organization of Dissertation In this dissertation, I will describe my efforts to map sensorimotor function and control neuroprosthetics with human electrocorticography. Each of the first four chapters has been developed into a manuscript for publication. The fifth chapter is a summary of my thoughts on the future of electrocorticography for functional mapping and neuroprosthetics, and is meant to conclude the efforts described in this thesis. The five chapters are each described briefly below. Chapter 1. The approach to ECoG-based neuroprosthetics taken by our team, which includes clinical and engineering teams at Johns Hopkins University and an engineering team at the Johns Hopkins University Applied Physics Laboratory, is discussed. A brief background for the ECoG neuroprosthetics project at the start of the thesis is presented, in addition to preliminary data describing our team’s ability to decode movement kinematics from ECoG signals. This chapter also presents a description and a schematic of our system which streams neural and kinematic data, performs neural decoding, and streams movement commands to either a virtual or physical Modular Prosthetic Limb (MPL). Chapter 2. The design and validation of a system for online ECoG-based spatial-temporal functional mapping (STFM) is described. High gamma amplitude is extracted in real-time from streaming ECoG signals, and the features from each channel are statistically compared to brief periods of rest before each cue. The results of these analyses can be visualized in real-time. This system was found to be vii more sensitive and more specific than stimulation mapping via a region of interest approach which estimated functional roles based on classical anatomical boundaries. Chapter 3. A version of the system described in Chapter 1 is used to provide online ECoG-based control over reaching and grasping of the Modular Prosthetic to two human subjects. The STFM system from Chapter 2 is used to identify channels whose high gamma activity robustly increases during movements and is selective for either reach or grasp. Using these tools, the two subjects are able to rapidly gain independent neural control over reaching and grasping of the prosthetic limb. Chapter 4. A high-density ECoG (hdECoG) array is used to map the cortical representations of mechanical vibration of a human subject’s fingertips. A strong somatotopic gradient is seen in the postcentral gyrus between thumb (inferiorly) and little finger (superiorly), especially in the high gamma power. The high gamma power is robust enough to be observed and classified with near perfect accuracy in single trials. Low frequency power and smoothed amplitude exhibit much weaker, and more disordered, somatotopy than the high gamma power. Chapter 5. The thesis is concluded with a discussion of ECoG’s current and likely future place amongst other prominent functional mapping and brain-machine interface tools, including microelectrode arrays, functional magnetic resonance imaging, electroencephalography, and electrocortical stimulation. The future of ECoG for clinical adoption over microelectrode arrays (for brain-machine interface) and electrocortical stimulation (for functional mapping) receive particular attention. Extensions of the three major projects described in Chapters 2-4 are also discussed. viii Contents Abstract ii Acknowledgements iv Dedication vi Summary and Organization of Dissertation vii Table of Contents ix List of Figures xiv List of Tables xxii 1 Toward electrocorticographic control of a dexterous upper limb prosthesis: building brain-machine interfaces 1 1.1 Abstract 1 1.2 Introduction 2 1.3 Neural Data Acquisition 4 1.4 ECoG-Based BMI System Implementation 7 1.5 Results 12 1.6 Future Directions 15 1.7 Study Acknowledgements 18 2 Spatial-Temporal Functional Mapping (STFM) with Online Spectral Analysis of Electrocorticography 19 2.1 Abstract 19 2.2 Introduction 20 2.3 Materials and Methods 24 ix 2.3.1 Patient and Clinical Settings 24 2.3.2 Experimental Testing and Event Markers 25 2.3.3 Electrode Localization 26 2.3.4 Online Data Acquisition and Analysis 27 2.3.5 Online Spectral Feature Extraction 28 2.3.6 Online Statistical Analysis 30 2.3.7 Raster Visualization 33 2.3.8 Brain Map Visualization 33 2.3.9 Offline Time-Frequency Analysis 34 2.3.10 ECoG Maps vs. Electrical Stimulation Maps 34 2.3.11 Region of Interest Analysis 35 2.4 Results 38 2.4.1 Spatial-Temporal Functional Mapping can be Performed Online 38 2.4.2 Spatial-Temporal Functional Maps are Task-Specific 42 2.4.3 Micro-ECoG vs.
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