Preeya Khanna

Preeya Khanna is an Assistant Professor in the Department of EECS at the University of California, Berkeley. Her research spans Biosystems & Computational Biology (BIO), Human-Computer Interaction (HCI), Signal Processing (SP), and Artificial Intelligence (AI). She focuses on sensorimotor systems neuroscience, network modeling, and neurotechnology development to understand how distributed brain networks coordinate dexterous movements. Her work aims to design neurophysiologically-grounded brain-machine interface therapies for restoring movement control in patients with damaged sensorimotor systems. Khanna's research areas include sensorimotor learning and control, neural engineering, brain-machine interfaces, and rehabilitation engineering. She is affiliated with the Helen Wills Neuroscience Institute (HWNI).

Preeya Khanna is a researcher at the University of California, Berkeley, specializing in Neural Engineering. Her work focuses on understanding neural population signals and their role in motor control, particularly through the analysis of beta band oscillations in motor cortex. Khanna investigates how neural co-firing and oscillatory activity can be modulated to enhance motor function after stroke, using brain-machine interfaces and neurofeedback techniques. Her research also explores the use of vibrotactile feedback and closed-loop neurostimulation to improve dexterity and motor recovery. Additionally, she examines the dynamics of neural ensembles during movement and behavioral exploration, aiming to uncover invariant patterns that drive motor commands.