Fundamentals of Neural Recording, Neural Stimulation, & Brain-Computer Interfaces for Medical & Robotic Applications
What you will learn
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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
English
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