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Neuroengineering 1

Wednesday, April 17, 2:00-3:30
Ski-U-Mah, McNamara Alumni Center

Organizer: Suhasa Kodandaramaiah, University of Minnesota

"The Next Generation Artificial Intelligence Enabled Brain-Machine Interfaces and Neuroprosthetics"
Zhi Yang, University of Minnesota

"Engineering Brain Networks to Treat Mental Illness"
Alik Widge, Assistant Professor of Psychiatry, University of Minnesota

"Printed Electronics for Flexible and Customizable Neural Interface Devices"
Sarah Swisher, Assistant Professor, Department of Electrical and Computer Engineering, University of Minnesota


Session Abstract:



Session Organizer Bios:

 


Speaker Bios:

Zhi Yang, University of Minnesota
Zhi Yang is an Assistant Professor of Biomedical Engineering at the University of Minnesota. He is also a researcher in MnDRIVE Robotics, sensors and advanced manufacturing. Professor Yang’s research interests are in the areas of implantable electronics, neuro-artificial intelligence, and integrated circuits. He has directed 20 projects with a total amount exceeding $5 million. The current research is supported by federal funding agencies such as NSF, NIH, and DARPA. Professor Yang is a key contributor in the DARPA HAPTIX program for inventing a cutting edge neural AI chip that has enabled amputee subjects to control a robotic hand with life like dexterity. He is a scientist and innovator, who develops bioelectronics therapies to treat diseases and disorders where current drug or surgery based therapies fall short.

Alik Widge, Assistant Professor of Psychiatry, University of Minnesota
Alik Widge, MD, PhD is a brain stimulation psychiatrist and biomedical engineer. Dr. Widge's laboratory has demonstrated new algorithms for closed-loop brain stimulation and stimulation methods for modifying connectivity in the distributed circuits of mental illness. His laboratory studies rodent models for prototyping these new technologies and human patients to identify biomarkers and targets for future intervention. He also co-leads programs to design new neurostimulation technologies in the central and peripheral nervous systems, to evaluate technologies for safety and efficacy in humans, and to improve the quality of clinical biomarker research nationwide.

Sarah Swisher, Assistant Professor, Department of Electrical and Computer Engineering, University of Minnesota
Sarah L. Swisher received her B.S. in Electrical Engineering from the University of Nebraska-Lincoln, then – after several years in industry – received her M.S. and Ph.D. degrees in Electrical Engineering and Computer Sciences from the University of California, Berkeley. Prof. Swisher joined the Electrical and Computer Engineering faculty at the University of Minnesota in 2015. Her research – sitting at the intersection of semiconductor device physics, materials science, and bioengineering – leverages the beneficial properties of nanomaterials and flexible electronics to address societal-scale challenges.


Presentation Abstracts:

"The Next Generation Artificial Intelligence Enabled Brain-Machine Interfaces and Neuroprosthetics"
In this talk, I will first present a novel neural chip and an AI algorithm, a mind-reading technology invented in my lab representing a recent breakthrough in brain-machine interfaces and neural electronics. I will share demos and patient interviews to explain how our research could change their life. I will then discuss the challenges, recent innovations, and the next-step plan towards engineering a new hand for amputees. Finally, I will present our ongoing works and discuss future directions for innovating the next generation neural modulation technologies and their upcoming human clinical applications in a broader context.

"Engineering Brain Networks to Treat Mental Illness"
Mental disorders arise from brain circuit dysfunctions, but most of our treatments target the whole brain rather than defined circuits. I will present results of two lines of work directly targeting circuits: better biomarkers to guide network engagement, and new stimulation protocols that reliably produce network-level physiologic change.

"Printed Electronics for Flexible and Customizable Neural Interface Devices"
Flexible electronics are ideally suited for low-cost, conformable, and easily-customizable sensing applications. Here we show a process for patterning high-resolution electrocorticography (μECoG) electrode arrays using inkjet printing on a ubiquitous, flexible, and transparent substrate. These electrode arrays demonstrate an average impedance of 2.5 kΩ at 100 Hz, and were used to record local field potentials on the mouse somatosensory cortex in vivo. To expand the capabilities of such flexible sensors, our group is also developing solution-processed oxide semiconductors for flexible transistors, and exploring the impact of materials synthesis and device fabrication on the performance of these devices.


Related Sessions:

Neuroengineering 2

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