KEYNOTE SPEAKERS

 

 

Prof. Bin He (Fellow of IEEE, IAMBE, NAI, AIMBE, and BMES)

Carnegie Mellon University, USA

 

Bin He is a Trustee Professor of Biomedical Engineering, Electrical and Computer Engineering, and Neuroscience, and director of NIH Neural Interfacing Training Program at Carnegie Mellon University. He’s major research interests include transcranial focused ultrasound neuromodulation, brain-computer interface, and electrophysiological neuroimaging. He has published over 300 peer-reviewed journal articles in international journals including PNAS, Nature Communications, Science Robotics, Advanced Science, and Neuron, the Proceedings of IEEE, and given 200+ plenary, keynote, and invited talks at a number of national and international conferences and institutions. He is a Fellow of the International Academy of Medical and Biological Engineering (IAMBE), the National Academy of Inventors (NAI), IEEE, the American Institute of Medical and Biological Engineering (AIMBE), and the Biomedical Engineering Society (BMES). His research has been recognized by major awards including the IEEE Biomedical Engineering Award, IEEE EMBS William J. Morlock Award, the IEEE EMBS Academic Achievement Award, and the Ear Bakken Distinguished Lecture Award from AIMBE. He is the Editor-in-Chief of the IEEE Reviews in Biomedical Engineering and was the former Editor-in-Chief of the IEEE Transactions on Biomedical Engineering. Dr. He serves as the immediate Past Chair of International Academy of Medical and Biological Engineering and was a Past President of IEEE Engineering in Medicine and Biology Society.

 

Speech Title: "Dynamic Brain Mapping and Brain-Computer Interface"

 

Abstract: "Brain activity is distributed over the 3-dimensional volume and evolves in time. Mapping spatio-temporal distribution of brain activation with high spatial resolution and high temporal resolution is of great importance for understanding the brain and aiding in the clinical diagnosis and management of brain disorders. Electrophysiological source imaging from noninvasively recorded electroencephalogram (EEG) has played a significant role in advancing our ability to image brain function and dysfunction. We will discuss the principles of electrophysiological source imaging (ESI) and how AI/ML can greatly facilitate addressing technical challenges in ESI imaging, and applications to mapping epileptogenic networks using high density EEG. We will also discuss principles and state of the art of brain-computer interface (BCI) using noninvasive EEG, from which human intention is decoded using novel ML/AI algorithms. We show that human is able to control the flight of a drone and a robotic arm for reach, grasp and continuously move in 3D space, using only “thoughts” decoded from noninvasive EEG. Our results show that experience with mindful meditation can improve human’s capability for mind control, suggesting the importance of human-machine intelligence. Our recent work further highlights the advantages of utilizing transcranial focused ultrasound neuromodulation at V5 to amplify theta and alpha oscillations, thereby enhancing BCI performance for motion VEP spellers in humans."

 

 

Prof. Ki Chon (Fellow of IEEE, NAI, IAMBE, AIMBE)

University of Connecticut, USA

 

Ki H. Chon received the B.S. degree in electrical engineering from the University of Connecticut, Storrs; the M.S. degree in biomedical engineering from the University of Iowa, Iowa City; and the M.S. degree in electrical engineering and the Ph.D. degree in biomedical engineering from the University of Southern California, Los Angeles. He spent three years as an NIH Post-Doctoral fellow at the Harvard-MIT Division of Health Science and Technology. He is currently a Board of Trustees Distinguished Professor and Krenicki Chair of Biomedical Engineering at University of Connecticut, Storrs, CT. His current research interests include medical instrumentation, biomedical signal processing, wearable sensors and devices including use of smart phones for vital signs and monitoring cardiac arrhythmias, development of hydrophobic vital sign sensors and identification and modeling of physiological systems. He has published 223 peer-reviewed journal articles, and more than 135 book chapters, conference proceedings and abstracts to date, and has 14 U.S. patents granted. He has received more than $30M in grants from the NIH, NSF, DoD and industry. His patent concerning an algorithm for real-time detection of atrial fibrillation has been licensed to a Holter company and the Holter monitor is currently on the market. He was an Associate Editor of the IEEE Transactions on Biomedical Engineering from 2007-2013. He has chaired many international conferences including his role as the Program Co-Chair for the IEEE EMBS conference in NYC in 2006, and as the Conference Chair for the 6th International Workshop on Biosignal Interpretation in New Haven, CT in 2009. He is fellow of IEEE, National Academy of Inventors, International Academy of Medical and Biological Engineering, American Institute of Medical and Biological Engineering, Asia-Pacific Artificial Intelligence Association, and Artificial Intelligence Industry Alliance.

 

Speech Title: "Noninvasive And Quantitative Assessment of The Sympathetic Nervous System Via Electrodermal Activity and Skin Nerve Activity Signals"

 

Abstract: "In the field of biosensors, extensive effort has been made to understand and monitor the autonomic nervous system (ANS), particularly the sympathetic nervous system (SNS). The SNS regulates a broad spectrum of autonomic functions, especially under extreme emotional or physical states, such as pain, to maintain bodily homeostasis. Therefore, there has been especial focus on understanding and monitoring the mechanisms of the SNS, especially via noninvasive approaches, including wearable devices.One measure of SNS response is electrodermal activity (EDA). When the sudomotor nerves stimulate sweat production, the skin’s surface conductivity changes due to sweat secretion and alterations in the ionic permeability of sweat gland membranes. Therefore, EDA has served as an effective tool for SNS assessment, including detecting pain, anxiety, and emotion. Recently, skin sympathetic nerve activity (SKNA), which can be noninvasively captured using electrocardiogram recording with a sampling frequency >2,000 Hz, has been shown to be a promising approach for assessing the SNS. Research has shown a connection between the stellate ganglion and SKNA, suggesting that SKNA can be used as a physiomarker of cardiac sympathetic nerve activity. This talk will present several biomedical applications illustrating the potential of using EDA and SKNA as noninvasive physiomarkers of the SNS. "