Course Information
Course Overview
Fundamentals of Neural Recording, Neural Stimulation, & Brain-Computer Interfaces for Medical & Robotic Applications
This 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 features hands-on student-led projects and a review of various neurotech companies. 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.
The course provides students with fundamental articles from the field and dozens of quizzes for students to assess their understanding and reinforce key concepts. Optional hands-on research projects are also available.
Course Content
- 10 section(s)
- 28 lecture(s)
- Section 1 Course Introduction
- Section 2 Neural Recordings
- Section 3 Neuromuscular Pathway
- Section 4 OPTIONAL Project 1 - EMG Signal Processing & Real-Time Control of a Bionic Arm
- Section 5 Population Dynamics
- Section 6 Neural Decoding & Interfaces
- Section 7 Artificial Neural Networks
- Section 8 OPTIONAL Project 2 - Neural Signal Processing & Machine Learning for Decoding
- Section 9 Stimulation Waveforms
- Section 10 Stimulation Safety
What You’ll Learn
- Learning objectives are listed categorically as software/hardware expertise, quantitative skills, critical thinking, biology knowledge, and scientific literacy
- Software: filter noisy biological signals
- Software: extract features from neuromuscular waveforms
- Software: decode information from neural and electromyographic recordings
- Software: implement an artificial neural network in MATLAB for real-time control
- Software: control a robotic hand in real-time using biological recordings
- Software: implement real-time bioinspired haptic feedback
- Software: 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
- Hardware: describe the principles of safe and effective neurostimulation
- Hardware: sketch various stimulation waveforms
- Hardware: describe chemical reactions for electrically exciting neurons
- Hardware: explain the pros and cons of various materials as neurostimulation electrodes
- Hardware: record electromyographic signals from the surface of the body
- Quantitative: model neurons as electrical circuits
- Quantitative: quantify ion and voltage changes during action potentials
- Quantitative: quantify spatiotemporal changes in electrical activity throughout neurons
- Quantitative: perform a safety analysis of neurostimulation
- Quantitative: measure how changes in neuron morphology (e.g., length, diameter) impact spatiotemporal changes in electrical activity
- Quantitative: 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
- Critical Thinking: describe how artificial neural networks relate to biological neural networks
- Critical Thinking: explain how artificial neural networks work in the context of neural engineering
- Critical Thinking: evaluate the performance of a motor-decode algorithm
- Critical Thinking: interpret physiological responses to neurostimulation
- Critical Thinking: debug common neurostimulation errors
- Critical Thinking: debug common electrophysiology errors
- Critical Thinking: develop novel neuromodulation applications
- Critical Thinking: critically evaluate brain-computer interface technology
- Biology: list several applications of neural engineering
- Biology: identify potential diseases suitable for next-generation neuromodulation applications
- Biology: draw and explain how biological neural networks transmit information and perform complex tasks
- Biology: describe the molecular basis of action potentials
- Biology: summarize the pathway from motor intent to physical movement
- Biology: explain the neural code for motor actions
- Biology: sketch various neuromuscular waveforms
- Biology: describe how biological neural networks encode sensory information
- Biology: use basic biological principles to guide the development of artificial intelligence
- Scientific Literacy: summarize the state of the neural engineering field
- Scientific Literacy: identify future research challenges in the field of neural engineering
- Scientific Literacy: cite relevant neural engineering manuscripts
- Scientific Literacy: write 4-page conference proceedings in IEEE format
- Scientific Literacy: use a reference manager
- Scientific Literacy: performance basic statistical analyses
Reviews
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TTeekawut Chaipanha
Should have Thai
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SSumit Panzade
good
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SSipho Mthembu
I particularly appreciated the section on neurostimulation and its applications in haptic feedback and reanimating paralyzed limbs. It was inspiring to see how theoretical concepts translate into life-changing technologies
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AAshley Petersen
The quizzes were well-designed and helped solidify my understanding of complex topics like signal processing and neural feature extraction. I felt confident after completing them