Brain Computer Interfaces, Neural Engineering, NeuroRobotics

Fundamentals of Neural Recording, Neural Stimulation, & Brain-Computer Interfaces for Medical & Robotic Applications

4.89 (53 reviews)

Udemy

platform

English

language

Data Science

What you will 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

Brain Computer Interfaces, Neural Engineering, NeuroRobotics

What you will 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

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