The study of Brain-Machine Interfaces (BMIs) and Neural Engineering represents one of our most dynamic and rapidly advancing research pillars. This interdisciplinary area bridges classical engineering principles with neurobiology to interface with, repair, and augment the human nervous system. By leveraging BME’s unique positioning at the intersection of medicine and technology, we design systems that can decode neural activity to control external devices, as well as therapeutic technologies that deliver targeted electrical or optical stimulation to modulate damaged or diseased neural circuits.
Research in this BME specialization focuses heavily on the mathematical and computational challenges of neural decoding. The computational training within this track emphasizes the application of machine learning, statistical signal processing, and deep learning models to translate raw, noisy neural activity into predictable control signals for computer cursors, robotic limbs, or speech synthesizers. A defining feature of pursuing neural engineering within a BME department is the direct pathway to translational medicine and clinical application.
Our academic ecosystem is closely aligned with clinical departments, including neurosurgery, neurology, and physical rehabilitation. This integration enables the development of advanced neuroprosthetics, robotic exoskeletons, and sensory-feedback systems designed to restore mobility and tactile sensation to individuals with spinal cord injuries, stroke, or limb loss. Additionally, our research addresses the essential regulatory, clinical-trial, and ethical frameworks required to safely bring these advanced medical devices from the laboratory bench to patient care.
Faculty members in this research area
