(Photo Credit: University of Illinois Urbana-Champaign)
NE 412 – Neural Data Analysis – Spring 2026
Subject: Neural Engineering
Course Number: 412
Title: Neural Data Analysis
Credit Hours: 3
Instructor: Yang, Yuan
Tuesday, Thursday 09:30 AM – 10:50 AM
Urbana-Champaign Campus | Sidney Lu Mech Engr Bldg | Room 1047
Modern technologies for recording brain activity hold the potential to enable a range of applications in neurology, neurobiology, and neuroscience in general. However, those recording technologies generate data at such scale and complexity that rigorous data analysis approaches for automatic information retrieval are required to fully leverage their potential. This course will introduce students to multiple neural data modalities (e.g., EEG and fMRI) and illustrate through examples, how modern data analysis techniques such as machine learning can be used to automatically extract meaningful information from those data. We will cover basics of neural data acquisition, preprocessing methods, data representation, dimensionality reduction, clustering, supervised learning, unsupervised learning, and some select advanced analytic concepts. This course will put equal emphasis on the understanding of analytical methods as well as practical hands-on experience, and equip the students with the essential skills to analyze neural data using advanced data analysis techniques such as machine learning. This course is designed for junior/senior undergraduate students with no or very limited prior experience in data science. Although prior exposure to the Python programming language is preferred, it is not required. Course Information: 3 undergraduate hours. No graduate credit. Prerequisite: BIOE 210, BIOE 310, or instructor consent.
ECE/NE 410 – Neural Circuits and Systems – Fall 2025
Instructor: Prof. Yuan Yang
Contact Information: yuany@illinois.edu
Location: 3018 Campus Instructional Facility
Course Description:
Introduction to modeling functions of neurons and systems of neurons in the brain. Topics include Boolean signal processing, nonlinear diffusion equations, delay-and-add synaptic signal processing. Integrates information from the structure and physiology from a single neuron up to the assembly of brain circuits. Examples presented to discuss neural circuit and systems include the auditory, and to a lesser extent, visual system. Course concludes with a look at theories of brain function built up from systems of neurons.
Credit: 3 OR 4 hours
BIOE 210 – Linear Algebra for Biomedical Data Science – Spring 2025
Using analytical and computational tools from linear algebra, students will Solve large systems of linear equations, systems of linear ODEs, and linear PDEs; Analyze large, multivariable datasets to quantify relationships between variables; Decompose complex datasets into simpler representations; Introduce and solve common problems in classification, image processing, and machine learning; Develop a geometric understanding of high-dimensional spaces. Prerequisite: CS 101 or CS 124, and MATH 231.
Credit: 3 hours
CRN: 69415
Type: Lecture
Section: LEC
Time: 09:30 AM- 10:50 AM
Days: TR
Location: 3025 – Campus Instructional Facility
Instructor: Prof. Yuan Yang
Restricted to BS:Bioengineering – UIUC or BS:Neural Engineering – UIUC.
BIOE 210 – Linear Algebra for Biomedical Data Science – Fall 2024
Instructor: Yuan Yang
Teaching assistant: Junxi Yi (junxiyi2@illinois.edu)
Office hours: TA will hold office hours on Wednesdays from 3-4 pm in Room 3213 Everitt Lab. For those need more help with Matlab, this is an opportunity to catch up.
Course Contents and Objectives: BIOE 210 is a core course required for all bioengineering undergraduates. The goal is to introduce students to essential analytical and computational tools from linear algebra. In addition to describing vector and matrix arithmetic, students will solve systems of linear equations. These methods can be applied to analyze large, multivariable datasets to quantify relationships between variables; decompose complex datasets into simpler representations; solve common problems in classification and image processing; and develop a geometric view of high-dimensional data spaces. Course topics include definitions of vector spaces; linear systems; solvability; rank; basis; transformation matrices; and vector & matrix decompositions (eigenanalysis, SVD, PCA). The course focuses on mathematical and computation aspects of problem solving, and consequently requires students to access Matlab. Completing assignments with other array-based scientific computing software, e.g., Python, is acceptable.
ECE/NE 410 – Neural Circuits and Systems – Spring 2024
Instructor: Professor Yuan Yang
Contact Information: yuany@illinois.edu
Office Location: Everitt Lab 0242B or Virtual
Office Hours: By appointment
Course Description:
Introduction to modeling functions of neurons and systems of neurons in the brain. Topics include Boolean signal processing, nonlinear diffusion equations, delay-and-add synaptic signal processing. Integrates information from the structure and physiology from a single neuron up to the assembly of brain circuits. Examples presented to discuss neural circuit and systems include the auditory, and to a lesser extent, visual system. Course concludes with a look at theories of brain function built up from systems of neurons.