Spring 2027
ECE 421 - NEURAL INTERFACE ENGINEERING
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USEFUL RESOURCES COURSE FLYER (pdf) COURSE SYLLABUS (pdf) |
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Course is focused on hardware and software technologies that enable control and readout of neural activity in the brain. Engineering-grounded innovation will accelerate our understanding of the brain, will impact new therapies for restoring lost neural functions, as well as will lead to neural interfaces that will augment our interaction with the world and machines. We will start with general introduction to neurobiology introducing concepts of neural activity, brain chemical and electrical signaling. neuroanatomy, and sensory information processing. We will further focus on using physical, chemical, and biological principles to understand technology design criteria governing ability to observe and alter brain structure and function. Topics include: noninvasive and invasive brain mapping and stimulation, neural interfaces and neural prosthetics, data processing problems, decoding techniques based on machine learning, future brain interfaces based on nanotechnology and optogenetics. Course pre-requisties ECE210, or BIOE205 and NE 330, or instructor approval. The course is building upon general knowledge of linear systems and analogue signals analysis The goal is to open this course to general population of EE, CS, and BioE students interested to learn principles and frontiers of neural interface engineering. Therefore, no prior knowledge of neurobiology is required. Information for graduate students (PDF): Graduate students will be required to complete an additional independent project based on reviewing the proposed scientific literature, writing an extended report, and presenting it in the class. Alternatively, to receive this credit, graduate students must complete an independent project based on analysis of publicly available datasets of brain electrophysiological recordings using machine learning based software. Graduate credit hours: 4 |
| Time | Room | Instructor | Office Hours | Office |
| 2:00pm-3:20pm Tue Thur |
2013 ECEB | Yurii Vlasov (yvlasov@illinois.edu) | Thurs 11:00 AM | 1250 MNTL |
TAs TBD (tbd@illinois.edu), TBD (tbd@illinois.edu) Office Hours: Wednesdays 1PM in ECEB 2036
Textbook: Mostly based on classnotes.
Recommended Reading:Principles of Neural Science (PNS), Eric Kandel, James Schwartz, Thomas Jessell , McGraw-Hill Medical, 2000.
Theoretical Neuroscience (TN), Peter Dayan and Larry.F. Abbott, MIT Press, 2001.
From Single Neurons to Networks and Models of Cognition (FSNMC) W. Gerstner, et al, Cambridge, 2014
Neural Engineering (NE) (edited by Bin He) Kluwer Academic Press 2005
Pattern Recognition and Machine Learning (PRML), Christopher Bishop. Springer, 2007
| Weeks/Topic | Wednesday 2:30pm ECEB 3017 |
Friday 2:30pm ECEB 3017 |
Recommended reading | |
| 1. Neuro electricity | 01/20 L1
Introduction Brain anatomy and functional organization. Neuroelectricity.
In-class quiz |
01/22 L2 Membrane potential, ion channels. Action potential.
HW1 Assignment |
PNS Ch1, Ch15, Ch5, TN Ch.5.5 | |
| 2. Neuron models | 01/27 L3 HH membrane model. LIF neuron model.
In-class quiz |
01/29 L4 Dendritic transmission. Axonal signal transmission.
HW2 Assignment. |
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| 3. Modulating brain | ! 02/03 L5 Noninvasive neuromodulation. Limbic system. Basal ganglia. Deep brain stimulation.
In-class quiz |
02/05 L6 Auditory system. Auditory signal processing in cochlear.
HW3 Assignment. |
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| 4 Sensory organs prosthetics | 02/10 L7 Cochlear implants. Signal processing.
In-class quiz |
02/12 L8 Visual system. Hierarchical processing in visual system. HW4 Assignment. | ||
| 5 Brain Plasticity | 02/17 L9. Retinal implants. Advantages and challenges.
In-class quiz |
02/19 L10 Synaptic transmission. Synaptic plasticity. | ||
| 6 Neuro rehabilitation | 02/24 L11 Midterm Exam 1. | 02/26 L12 Hebbian learning. Neuromuscular junction. FES. Neuro rehabilitation.
HW5 Assignment. |
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| 7 Recording from brain | 03/03 L13 Noninvasive recording of brain activity: MEG, PET, fMRI, IR imaging.
In-class quiz |
03/05 L14 EEG biopotential amplifiers. EEG signal processing. ICA and PCA.
HW6 Assignment. |
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| 8 EEG BMI | 03/10 L15 Event related potential. Feature extraction. Classification. EEG-based BMI.
In-class quiz |
03/12 L16 Invasive recording methods. ECOG. ECOG BMI. | ||
| 03/17 Spring Break |
03/19 Spring Break |
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| 9 Spike train analysis | 03/24 L17 Multielectrode arrays. Spikes and LFP. Spike sorting. Spiking variability. Firing rate.
In-class quiz |
03/26 L18 Spike density. Tuning curve. Population coding. Neural coding problem.
HW7 Assignment. |
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| 10 Limb prosthetics | 03/31 L19 Motor system. Neural control of movement.
In-class quiz |
04/02 L20 Decoding algorithms. Dimensionality reduction.
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| 11 Control engineering | 04/07 L21 Midterm Exam II | 04/09 L22 Intro to linear systems control. Kalman filter and optimal state estimation.
HW8 Assignment |
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| 12 Neural decoding | 04/14 L23 BMI examples. Linear and population vector decoding. |
04/16 L24 BMI examples. Kalman filter decoding.
HW9 Assignment. |
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| 13 Sensory-motor prosthetics | 04/21 L25 Reverse Engineering I.
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04/23 L26 Reverse Engineering II.
HW10 Assignment |
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| 14 Beyond motor BMI | 04/28 L27
Sensory-motor BMI. Restoration of the sense of touch.
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04/30 L28 Memory prosthetics. Projects presentations I | ||
| 15 Wrapping up | 05/05 L29 Projects presentations II. |
05/07 Reading |
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| Final Exam 05/13 1:30PM ECEB 3017 |
GRADING POLICY
| 3 credit hours | 4 credit hours | |
| Homework and Quizes | 20% | 20% |
| Exam 1 | 25% | 20% |
| Exam 2 | 25% | 20% |
| Final Exam | 30% | 25% |
| Graduate Project | NA | 15% |