Learn about Neural Engineering and NeuroRobotics in the classroom!
Dr. George teaches a graduate course on Neural Engineering and NeuroRobotics that is offered in person at the University of Utah. There are no formal prerequisites, but a basic understanding of electrical circuits, biology and MATLAB programming is recommended. The course is taught every Fall and is cross-listed as BME 6440 (for BME majors), ECE 6654 (for ECE majors), and ROBOT 6400 (for all other majors). This course is an excellent opportunity to get hands-on experience in neurorobotics. Many graduates from this course go on to complete an independent study performing research in the Utah NeuroRobotics Lab working directly with Dr. George.
Course Description
This 4-credit course will cover tools and applications in the field of Neural Engineering with an emphasis on real-time robotic applications. Neural Engineering is an interdisciplinary field that overlaps with many other areas including neuroanatomy, electrophysiology, circuit theory, electrochemistry, bioelectric field theory, biomedical instrumentation, biomaterials, computational neuroscience, computer science, robotics, human-computer interaction, and neuromuscular rehabilitation. This course is designed around the central idea that Neural Engineering is the study of transferring electromagnetic information into or out of the nervous system. With this framework, the course is divided into three broad segments: neurorecording, neurostimulation and closed-loop neuromodulation. The neurorecording segment includes: invasive and non-invasive recording techniques, signal processing, neural feature extraction, biological and artificial neural networks, and real-time control of robotic devices using neurorecordings. The neurostimulation segment includes: invasive and non-invasive stimulation techniques, signal generation, physiological responses, safety analysis, and real-time stimulation for haptic feedback and for reanimating paralyzed limbs. The closed-loop neuromodulation segment will feature hands-on student-led group projects where students can combine techniques from the first two segments to create various neurorobotic applications. Example applications include bionic arms controlled by thought that restore a natural sense of touch, or neural-links that can decode a person’s thoughts to reanimate a paralyzed limb.
Course Content
This class is comprised of quantitative homework sets based on primary literature reading assignments, four hands-on lab exercises, and a final project. There are no exams! Core learning objectives will be mastered through the lab exercises and final project. Lectures will leverage student presentations, quantitative exercises and software challenges to build the skills necessary for each lab (e.g., signal acquisition/generation, signal processing, control algorithms).
Example Course Schedule
Learning Objectives
By the end of this course, you will be able to:
- Software
- Filter noisy biological signals
- Extract features from neuromuscular waveforms
- Decode information from neural and electromyographic recordings
- Implement an artificial neural network in MATLAB for real-time control
- Control a robotic hand in real-time using biological recordings
- Implement real-time bioinspired haptic feedback
- Develop real-time functional electrical stimulation for assistive and rehabilitative tech
- Hardware
- Describe how to implement various electrophysiology techniques (e.g., space clamp, voltage clamp) and what they are used for
- Describe the principles of safe and effective neurostimulation
- Sketch various stimulation waveforms
- Describe chemical reactions for electrically exciting neurons
- Explain the pros and cons of various materials as neurostimulation electrodes
- Record electromyographic signals from the surface of the body
- Quantitative
- Model neurons as electrical circuits
- Quantify ion and voltage changes during action potentials
- Quantify spatiotemporal changes in electrical activity throughout neurons
- Perform a safety analysis of neurostimulation
- Measure how changes in neuron morphology (e.g., length, diameter) impact spatiotemporal changes in electrical activity
- Measure how changes in neuron electrical properties (e.g., capacitance, resistance) impact spatiotemporal changes in electrical activity
- Critical Thinking
- Explain the characteristics of good training data for neural engineering applications
- Describe how artificial neural networks relate to biological neural networks
- Explain how artificial neural networks work in the context of neural engineering
- Evaluate the performance of a motor-decode algorithm
- Interpret physiological responses to neurostimulation
- Debug common neurostimulation errors
- Debug common electrophysiology errors
- Develop novel neuromodulation applications
- Critically evaluate brain-computer interface technology
- Biology
- List several applications of neural engineering
- Identify potential diseases suitable for next-generation neuromodulation applications
- Draw and explain how biological neural networks transmit information and perform complex tasks
- Describe the molecular basis of action potentials
- Summarize the pathway from motor intent to physical movement
- Explain the neural code for motor actions
- Sketch various neuromuscular waveforms
- Describe how biological neural networks encode sensory information
- Use basic biological principles to guide the development of artificial intelligence
- Scientific Literacy
- Summarize the state of the neural engineering field
- Identify future research challenges in the field of neural engineering
- Cite relevant neural engineering manuscripts
- Write 4-page conference proceedings in IEEE format
- Assess authorship and report author contributions
- Teamwork
- Identify novel research challenges appropriate for your team
- Program in an agile software development framework
- Work effectively in teams to solve engineering challenges