Each course is three credit hours and counts as a Specialty Elective in the area(s) indicated.
Offered at NC State as BME 590, Offered at UNC as BMME 590
All Areas
Data Science and Machine Learning in Medicine (Spring)
Prerequisites: [BM(M)E 301 and BM(M)E 302] or [ST 370 and BM(M)E 201]
Methods of data science and machine learning in the context of medical decision support. Types of data and models. Data visualization. Descriptive statistics. Analysis of variance. Dimensionality reduction. Clustering. Models and evaluation methods for classification problems and estimation problems. Deep learning. Extensive use of MATLAB.
Pharmacoengineering
Drug and Cell Delivery (Spring)
Prerequisites: BM(M)E 302 and BM(M)E 315 and (CH 221 or CHEM 261)
This course covers the engineering of novel pharmaceutical and cell delivery systems with enhanced efficacy and safety profiles, with an emphasis on the design and application of materials that overcome delivery barriers or challenges. Topics will include drug delivery fundamentals and transport mechanisms, cell modification and administration, materials and formulations for delivery, and clinical applications.
Microphysiological Systems for Drug Development (Spring)
Prerequisites: BM(M)E 301 and BM(M)E 302 and BM(M)E 315
In this course, students will learn about the emerging role of microphysiological systems (MPS) in drug development, including the regulatory context surrounding their use, the design, development, and implementation of organ-specific systems, and methods for data collection and quantification in these complex 3D in vitro systems.
Network Pharmacology (Fall)
Prerequisites: BM(M)E 325 or CHEM 430; MATH 383; Proficiency in a programming language such as Python, R, Matlab, Mathematica or similar; Basic knowledge of linear algebra
This course, "Principles of Network Pharmacology," is designed to introduce senior undergraduate and first-year graduate students from biomedical engineering, computational biology, and related disciplines to the interdisciplinary field of network pharmacology. During this course students will explore the integration of pharmacology, network theory, and computational biology to understand the complex interactions within biological systems and their implications for drug discovery and development. The course covers foundational principles, computational tools, and methodologies essential for analyzing and interpreting biological networks, drug-target interactions, disease modules, and personalized medicine approaches. Through lectures, hands-on projects, and case studies, students will gain insights into the current trends, challenges, and future directions in network pharmacology.
Rehabilitation Engineering
Applied Instrumentation for Movement Analysis (Spring) Will not be offered Spring 2026
Prerequisites: BM(M)E 205. Preferred BMME 543 (previously BMME 405)
The goal of this course is twofold. First, this course will provide hands-on experience with state-of-the-art technologies in the field of movement analysis, with a focus on – in this semester - markerless motion capture, inertial measurement units, and electromyography. Second, this course will provide fundamental training in the professional skills needed to successfully integrate those technologies into practice, to include critical evaluation of literature, hypothesis generation, and data analytics and visualization. The course will also strive to invite guest lectures to reinforce principles on applying these technologies to real-world areas of inquiry.
Brain Machine Interfaces (Spring)
Prerequisites: BM(M)E 301 and BM(M)E 365
This course explores the concepts and components that constitute a brain-machine interface. Topics include neural recording, signal processing, neural decoding, prosthetic control, sensory feedback, and electrical stimulation. Along with providing historical context, the course will cover contemporary brain-machine interface technologies and explore future applications. All topics will be presented within the context of clinical applications of neural engineering methods. The format includes lectures, discussion of primary research papers, and student projects.
Haptic Robotics and Human Motor Control (Spring)
Prerequisites: BM(M)E 301 and BM(M)E 355 and BM(M)E 385
The goal of this course is to explore the use of haptic feedback provided through robotics to enhance understanding of human sensorimotor control, surgical simulators, and physical rehabilitation. Students will examine the physiology of sensation, admittance/impedance control, and the implementation of haptic feedback to perturb or enhance the human-robot task performance. Practical experiments with a haptic device will complement lectures and reviews of key publications.
Neural Engineering (Spring)
Prerequisites: BM(M)E 301 and BM(M)E 365
Principles and technologies of neuroengineering and clinical applications in neuromotor rehabilitation; basic human neurophysiology; bi-directional neural-machine interfaces; neuromodulation for motor function improvement; artificial visual/auditory devices for augmented sensory perception.
Biosignals and Imaging
Brain Machine Interfaces (Spring)
Prerequisites: BM(M)E 301 and BM(M)E 365
This course explores the concepts and components that constitute a brain-machine interface. Topics include neural recording, signal processing, neural decoding, prosthetic control, sensory feedback, and electrical stimulation. Along with providing historical context, the course will cover contemporary brain-machine interface technologies and explore future applications. All topics will be presented within the context of clinical applications of neural engineering methods. The format includes lectures, discussion of primary research papers, and student projects.
Magnetic Resonance Image Acquisition and Processing (Spring)
Prerequisites: BM(M)E 365
This course is an introduction to the theory and design of magnetic resonance imaging systems, with an emphasis on theory from an engineering perspective. Mathematical derivations of fundamental principles will be explored. Topics include image acquisition and reconstruction, mechanisms for image contrast and resolution, and an overview of system design.
Ultrasound Imaging and Therapy (Spring)
Prerequisites: BM(M)E 301 and BM(M)E 302 and BM(M)E 365
This course introduces the fundamental principles of ultrasound imaging and therapy. The course is structured into three main areas: ultrasound imaging, contrast-enhanced ultrasound (CEUS), and therapeutic ultrasound. Students will explore both theoretical and practical aspects of ultrasound, covering topics such as acoustic wave propagation, transducer technology, CEUS techniques in perfusion quantification and microvascular imaging. Additionally, ultrasound bioeffects and their therapeutic applications will be discussed. The course will be complemented by selected guest lectures, hands-on tutorials/guided simulations and experiments, and clinical practice simulation scenarios. Students will engage in individual and team-based projects and research paper presentations throughout the course.
NEW! XR Engineering Applications for Healthcare (Fall)
Prerequistes: BMME 365
This course discusses the principles of Extended Reality (XR) technologies and their applications to healthcare and biomedical engineering. The course will study the instrumentation that is currently used to provide an XR experience including headset displays, eye tracking technologies, motion sensors and hand controllers. It will research emerging technologies which use biosignals and additional sensory stimulation devices to enhance the user experience. The class will explore use-cases of XR in healthcare including training applications, surgical assistance, and neurological research. Students will gain hands-on experience in the development of XR applications using Unity or Unreal software.
Medical Microdevices
Brain Machine Interfaces (Spring)
Prerequisites: BM(M)E 301 and BM(M)E 365
This course explores the concepts and components that constitute a brain-machine interface. Topics include neural recording, signal processing, neural decoding, prosthetic control, sensory feedback, and electrical stimulation. Along with providing historical context, the course will cover contemporary brain-machine interface technologies and explore future applications. All topics will be presented within the context of clinical applications of neural engineering methods. The format includes lectures, discussion of primary research papers, and student projects.
Electroceuticals and Bioelectronic Medicine (Spring)
Prerequisites: BMME 385
Applications of bioelectronic medicine and electroceuticals with a focus on implantable and wearable devices. The course will cover multimodal biosensing and stimulation methods in analog and digital regimes, power requirements of medical microdevices and relevant energy harvesting techniques, communication protocols and design challenges for wireless body area networks, and micro and nanoscale fabrication methods for biocompatible and resorbable devices. Devices and techniques to be studied include pacemakers, cochlear and retinal implants, spinal cord stimulators, deep brain and vagus nerve stimulation, and wound healing. A semester-long system design project will give students hands-on experience with each of these topics through a series of labs, culminating in a benchtop model of a modern electroceutical device.
NEW! XR Engineering Applications for Healthcare (Fall)
Prerequistes: BMME 365
This course discusses the principles of Extended Reality (XR) technologies and their applications to healthcare and biomedical engineering. The course will study the instrumentation that is currently used to provide an XR experience including headset displays, eye tracking technologies, motion sensors and hand controllers. It will research emerging technologies which use biosignals and additional sensory stimulation devices to enhance the user experience. The class will explore use-cases of XR in healthcare including training applications, surgical assistance, and neurological research. Students will gain hands-on experience in the development of XR applications using Unity or Unreal software.