Fall Semester

BME 6086-004: Fundamentals of Neural Control (Graduate Level)

The course introduces fundamental tools to model, estimate, and control the behavior of neural systems of increasing complexity (i.e., from single neurons to large cortical layers), with cutting-edge applications in the field of movement disorders, epilepsy, brain-machine interface, and rehabilitation. The first part of the course introduces biophysical-based and empirical models to describe the dynamics of neural systems, and studies the observability/controllability of these systems. The second part explores linear and nonlinear state estimation theory for neural systems with application to neural decoding and optimal state estimation. The third part focuses on designing state-based control algorithms that interact with the proposed models to achieve the assigned goals. Some of the topics covered during the course are:

  • Single-Unit Neural Models, Wilson-Cowan Models, Neural Oscillations
  • Observability and Controllability, Neuromodulation via Electric Fields
  • Kalman Filter, Bayesian Estimation, Neural Decoding
  • Empirical Models, Principal Component Decomposition, Numerical Analysis in MATLAB
  • Motor Control, Learning, and Principle of Optimality


BME 3100: Physiological Modeling (Undergraduate Level)

The course presents how physiological problems can be formulated mathematically, and how such models can be used for analysis, prediction, and therapy design. A wide selection of mathematical models in physiology is presented from the cellular level up to the systems level, including respiration/perfusion, muscle contraction, inner ear, and retina. Differential equations, Markov models, Laplace transform, and computer-aided tools are introduced and used during the course for modeling, simulation, and analysis purposes. Some of the topics covered during the course are:

  • Cellular Homeostasis, Enzyme Reactions, Membrane Ion Channels, Electrical Flow in Neurons
  • Heart and Blood Circulation, Alveolar Gas Exchange, Gastrointestinal Fluid Absorption
  • Muscle Contraction, Force-Velocity Models, Photoreceptors and Receptive Fields
  • Differential Equations, Elements of Statistical Modeling, Numerical Simulations in MATLAB



Spring Semester

BME 6086-017: Advanced Methods for Biomedical Signal Analysis (Graduate Level)

The course introduces advanced statistical methods to process non-stationary, noise-corrupted, complex biomedical signals like ECG, EEG, and LFP. It also introduces mathematical tools to properly model and analyze biomedical signals in various domains of application. Students get hands-on experience in applying the methods learnt in class to real world problems and a course project provides the opportunity to explore current problems in biomedical signal analysis, with specific application to neural and ECG data. Some of the topics covered during the course are:

  • Probability, Random Variables, and Random Vectors
  • Linear Regression, Maximum Likelihood Estimation, Generalized Linear Models
  • Poisson Processes, Point Processes, ROC Analysis, Kolmogorov Tests
  • Nonlinear Regression, Nonparametric Models, Adaptive Filtering
  • Bayesian Estimation, Hidden Markov Models, Change-Point Detection, Artificial Neural Networks


ENGR 1166: Foundations of Engineering (Undergraduate Level)

The course provides an introduction to several areas of research found in Biomedical Engineering. Topics include basic biomechanics, bioinstrumentation systems, circuit elements and concepts, linear network analysis, bio-potentials, biosensors, various imaging techniques, fundamentals of bioinformatics and molecular engineering. Some of the topics covered during the course are:

  • Vectors, Forces, Equilibrium, Free Body Diagrams
  • Biomechanical Modeling and Testing Techniques
  • Basic Bioinstrumentation System, Basic Circuit Elements and Concepts, Linear Network Analysis
  • Biopotentials and Biosensors, ECG, EEG, and EMG Signals
  • Imaging Techniques, Fundamentals of Bioinformatics, Fundamentals of Molecular Engineering


For Outreach