Integrating Artificial Sensation to Close the Sensorimotor Loop for Bilateral Neural Prostheses with Maria Dadarlat
In this talk, I will describe a long-term vision for developing bilateral neural prostheses, including an examination of how the intact nervous system naturally encodes bilateral sensory information and motor commands. Using a "learning-centric" framework, we demonstrate that multichannel ICMS can encode multivariable, task-relevant information that subjects integrate with natural vision to improve performance. By shifting the focus from simple sensory substitution to multisensory integration, this work highlights how artificial sensations modulate the brain's internal models, ultimately leading to more intuitive control and improved embodiment of neuroprosthetic systems.
Maria Dadarlat, PhD is an Assistant Professor of Biomedical Engineering at the Purdue University Weldon School of Biomedical Engineering.
Students registered for the seminar are expected to attend in person.
Teams Meeting ID: 211 123 896 292 8 Passcode: Uh9qs2pf
2026-04-22 09:30:00 2026-04-22 10:30:00 America/Indiana/Indianapolis Integrating Artificial Sensation to Close the Sensorimotor Loop for Bilateral Neural Prostheses with Maria Dadarlat Dexterous neural prostheses require complex sensory feedback to complement accurate neural decoding of movement. While visual feedback is the primary sensory source for current users, it is insufficient for the complex, high-degree-of-freedom tasks required in daily life, particularly for coordinated bilateral movements. MJIS 1001