Spring 2023

ECE 421 - NEURAL INTERFACE ENGINEERING

  USEFUL RESOURCES


  COURSE FLYER (pdf)

  COURSE SYLLABUS (pdf)

  COURSE PAGE ON CANVAS 


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) Wed 9:00 AM 1250 MNTL

TAs Rao, Yug (yugrao2@illinois.edu) Moskal, Nathan (nmoskal2@illinois.edu) Office Hours: Monday 3:00pm ECEB 3036

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 01/17 L1 Introduction. Brain anatomy and functional organization. Neuroelectricity. Notes 01/19 L2 Membrane potential, ion channels. Action potential. Notes
PNS Ch1, Ch15, Ch5, TN Ch.5.5
2. Neuron models 01/24 L3 HH membrane model. LIF neuron model. Notes
In-class quiz
01/26 L4 Dendritic transmission. Axonal signal transmission. Notes
HW1 Assignment
PNS Ch.6, TN Ch.6.3
3. Modulating brain 01/31 L5 Noninvasive neuromodulation. Limbic system. Basal ganglia. Deep brain stimulation.
In-class quiz
02/02 L6 Auditory system. Auditory signal processing in cochlear.
HW2 Assignment
NE Ch7-Ch9, NE Ch.6, PNS Ch.30
4 Sensory organs prosthetics 02/07 L7 Cochlear implants. Signal processing.
In-class quiz
02/09 L8 Visual system. Hierarchical processing in visual system.
HW3 Assignment
NE Ch.20, PNS Ch.26,
5 Brain Plasticity 02/14 L9. Retinal implants. Advantages and challenges. Synaptic transmission.
In-class quiz
02/16 L10 Synaptic plasticity. Hebbian learning. PNS Ch.8
6 Neuro rehabilitation 02/21 L11 Midterm Exam 1 02/23 L12 Neuromuscular junction. FES. Neuro rehabilitation.
HW4 Assignment
TN Ch.8
7 Recording from brain 02/28 L13 Noninvasive recording of brain activity: MEG, PET, fMRI, IR imaging.
In-class quiz
03/02 L14 EEG biopotential amplifiers. EEG signal processing. ICA and PCA.
HW5 Assignment
NE Ch.2
PRML Ch12.4.1
8 EEG BMI 03/07 L15 Event related potential. Feature extraction.Classification. EEG-based BMI.
In-class quiz
03/09 L16 Invasive recording methods. ECOG. ECOG BMI.
HW6 Assignment
PRML Intro PRML Ch4
  03/14
Spring Break

03/16
Spring Break

9 Spike train analysis 03/21 L17 Multielectrode arrays. Spikes and LFP. Spike sorting. Spiking variability. Firing rate. 03/23 L18 Spike density. Tuning curve. Population coding. Neural coding problem.
HW7 Assignment
NE Ch.4
PRML Ch 12
TN Ch 1
10 Limb prosthetics 03/28 L19 Motor system. Neural control of movement.

03/30 L20 Decoding algorithms. Dimensionality reduction.
PNS Ch.37
TN Ch.3
11 Control engineering 04/06 L21 Midterm Exam II 04/08 L22 Intro to linear systems control. Kalman filter and optimal state estimation.
HW8 Assignment
NE Ch.5.4
PNS Ch.39
12 Neural decoding 04/11 L23 BMI examples. Linear and population vector decoding. Kalman filter decoding.
04/13 L24 BMI examples. Reverse engineering.

HW9 Assignment
NE Ch.5.1-5.3
PNS Ch.39
13 Sensory-motor prosthetics 04/18 L25 Dynamic feedback control. Manifold hypothesis. 04/20 L26 Sensory-motor integration. Sensory-motor BMI.
HW10 Assignment
PNS Ch.33
14 Beyond motor BMI 04/25 L27 Restoration of the sense of touch. Memory and spatial navigation system. 04/27 L28 Memory prosthetics. PNS Ch.22
15 Wrapping up 05/2 L29
Projects presentations.
Conclusions.
05/4 Reading
  05/09
Final Exam


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%