ECE 421 Neural Interface Engineering

Spring 2024

ECE 421 - NEURAL INTERFACE ENGINEERING

USEFUL RESOURCES

COURSE FLYER (pdf)

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: 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 Alex Armstrong (alexga2@illinois.edu), Hrishikesh Iyer (htiyer2@illinois.edu) Office Hours: Wednesdays 10AM 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	Tuesday 2:00pm ECEB 2013	Thursday 2:00pm ECEB 2013	Recommended reading
1. Neuro electricity	01/16 L1 Introduction. Brain anatomy and functional organization. Neuroelectricity. Notes	01/18 L2 Membrane potential, ion channels. Action potential. Notes HW1 Assignment Solutions	PNS Ch1, Ch15, Ch5, TN Ch.5.5
2. Neuron models	01/23 L3 HH membrane model. LIF neuron model. Notes Video	01/25 L4 Dendritic transmission. Axonal signal transmission. Notes In-class quiz solutions HW2 Assignment. Solutions.	PNS Ch.6, TN Ch.6.3
3. Modulating brain	01/30 L5 Noninvasive neuromodulation. Limbic system. Basal ganglia. Deep brain stimulation. Notes In-class quiz solutions	02/01 L6 Auditory system. Auditory signal processing in cochlear. Notes HW3 Assignment. Solutions.	NE Ch7-Ch9,NE Ch.6, PNS Ch.30
4 Sensory organs prosthetics	02/06 L7 Cochlear implants. Signal processing. Notes	02/08 L8 Visual system. Hierarchical processing in visual system. Notes HW4 Assignment. Solutions.	NE Ch.20, PNS Ch.26,
5 Brain Plasticity	02/13 L9. Retinal implants. Advantages and challenges. Notes	02/15 L10 Synaptic transmission. Synaptic plasticity. Notes	PNS Ch.8
6 Neuro rehabilitation	02/20 L11 Midterm Exam 1. Exam_1 Solutions	02/22 L12 Hebbian learning. Neuromuscular junction. FES. Neuro rehabilitation. Notes HW5 Assignment. Solutions	TN Ch.8, PNS Ch.9
7 Recording from brain	02/27 L13 Noninvasive recording of brain activity: MEG, PET, fMRI, IR imaging. Notes In-class quiz	03/01 L14 EEG biopotential amplifiers. EEG signal processing. ICA and PCA. Notes HW6 Assignment. Solutions	NE Ch.2 ,NE Ch.11 PRML Ch12.4.1
8 EEG BMI	03/06 L15 Event related potential. Feature extraction.Classification. EEG-based BMI. Notes	03/08 L16 Invasive recording methods. ECOG. ECOG BMI. Notes HW7 Assignment. Solutions	PRML Intro, Ch4
	03/13 Spring Break	03/15 Spring Break
9 Spike train analysis	03/20 L17 Multielectrode arrays. Spikes and LFP. Spike sorting. Spiking variability. Firing rate. Notes	03/22 L18 Spike density. Tuning curve. Population coding. Neural coding problem. Notes HW8 Assignment. Solutions	NE Ch.4 PRML Ch 12 TN Ch 1
10 Limb prosthetics	03/27 L19 Motor system. Neural control of movement. Notes	03/29 L20 Decoding algorithms. Dimensionality reduction. Notes	PNS Ch.37 TN Ch.3
11 Control engineering	04/02 L21 Midterm Exam II Exam 2 Solutions	04/04 L22 Intro to linear systems control. Kalman filter and optimal state estimation. Notes HW9 Assignment	NE Ch.5.4 PNS Ch.39
12 Neural decoding	04/09 L23 BMI examples. Linear and population vector decoding. Notes	04/11 L24 BMI examples. Kalman filter decoding. Notes HW10 Assignment.	NE Ch.5.1-5.3 PNS Ch.39
13 Sensory-motor prosthetics	04/16 L25 Reverse Engineering. Notes Notes 2	04/18 L26 Dynamic feedback control. Manifold hypothesis. Sensory-motor integration. Notes HW11 Assignment	PNS Ch.33
14 Beyond motor BMI	04/23 L27 Sensory-motor BMI. Restoration of the sense of touch. Notes	04/25 L28 Memory and spatial navigation system. Memory prosthetics. Notes	PNS Ch.22
15 Wrapping up	04/30 L29 Projects presentations. Conclusions. Notes	05/02 Reading
	Final Exam 05/08 7:00PM ECEB 2013

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%