Overview
- Bridges the gap between neuroscience and engineering tools
- Reveals space-time dynamics of brain waves via modal analysis and imaging
- Addresses nonlinear and stochastic brain wave dynamics
Part of the book series: Synthesis Lectures on Biomedical Engineering (SLBE)
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Table of contents (7 chapters)
Keywords
- Real-time EEG Imaging
- Modal Analysis of Brain Waves
- Imaging Using Biomarkers
- Brain Wave Mapping Using EEG
- Biomarker Dynamics
- Operational Modal Analysis of Biomarkers
- Adaptive State Estimation of Biomarker Dynamics
- Black Box Biomarker System Identification
- State-space Models for Brain Waves
- Brain Wave Adaptive Unknown Input Synthesis
About this book
A Modal Approach to the Space-Time Dynamics of Cognitive Biomarkers provides a much-needed reference for practicing researchers in biomarker modeling leveraging the lens of engineering dynamics.
Authors and Affiliations
About the authors
James E. Hubbard, Jr., Ph.D., is a mechanical engineer who has made significant contributions to the field of aerospace engineering throughout a career spanning more than four decades in academia and industry. Dr. Hubbard is considered a pioneer in adaptive structures, having developed piezo-film sensors and piezoelectric actuation systems for suppressing vibration and noise, surface morphing, and other applications. He has published more than 100 technical papers and four books on adaptive structures and aeronautics. He co-founded three companies and has received 24 U.S. and worldwide patents, leading to technological advances benefiting the aerospace, medical, defense, and other industries. Dr. Hubbard is currently the Oscar S. Wyatt, Jr. '45 Chair I Professor and Fellow of the Hagler Institute for Advanced Studies at Texas A&M University in College Station, Texas. He was inducted into the National Academy of Engineering in 2016 and the National Academy of Inventors in 2021.
Mark Balas, Ph.D., is the Leland Jordan Professor in the Mechanical Engineering Department at Texas A&M University. He was formerly the Guthrie Nicholson Professor of Electrical Engineering and former Head of the Electrical and Computer Engineering Department at the University of Wyoming. He has the following technical degrees: Ph.D. in Mathematics, MS Electrical Engineering, MA in Mathematics, and BS in Electrical Engineering. Dr. Balas has held various positions in industry, academia, and government. He has been a university professor for over 40 years with Rensselaer Polytechnic institute, Massachusetts Institute of Technology, University of Colorado Boulder, University of Wyoming, and Embry-Riddle Aeronautical University and has mentored 45 doctoral students. He has over 350 publications in archive journals, refereed conference proceedings, and technical book chapters. Dr. Balas has been a visiting faculty member at the California Institute of Technology, Air Force Research Laboratory, NASA Jet Propulsion Laboratory, NASA Ames Research Center, and the University of Wyoming, where he was also the Associate Director of the Wind Energy Research Center. He is a life fellow of the AIAA and IEEE and a fellow of the ASME.
Bibliographic Information
Book Title: A Modal Approach to the Space-Time Dynamics of Cognitive Biomarkers
Authors: Tristan D. Griffith, James E. Hubbard Jr., Mark J. Balas
Series Title: Synthesis Lectures on Biomedical Engineering
DOI: https://doi.org/10.1007/978-3-031-23529-0
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 12
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-23528-3Published: 02 March 2023
Softcover ISBN: 978-3-031-23531-3Published: 03 March 2024
eBook ISBN: 978-3-031-23529-0Published: 01 March 2023
Series ISSN: 1930-0328
Series E-ISSN: 1930-0336
Edition Number: 1
Number of Pages: XIII, 132
Number of Illustrations: 9 b/w illustrations, 31 illustrations in colour
Topics: Biomedical Engineering and Bioengineering, Neurosciences, Biomedicine, general, Mathematical Models of Cognitive Processes and Neural Networks, Mathematical Models of Cognitive Processes and Neural Networks