Dylan Forenzo

University of Pittsburgh, Carnegie Mellon University

Papers

Total Citations

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About

Dylan Forenzo is a rising leader in the field of non-invasive Brain-Computer Interfaces (BCIs), with a focused mission to restore motor function through neural control. His research centers on developing deep learning and advanced signal processing techniques to overcome the traditionally low signal-to-noise ratio of EEG, enabling real-time, continuous control of robotic arms. In his most cited work, "Continuous Reaching and Grasping With a BCI Controlled Robotic Arm in Healthy and Stroke-Affected Individuals" (2025, 7 citations), Forenzo demonstrates a significant breakthrough: a BCI system capable of fluid, multi-joint robotic arm control for both healthy users and, critically, stroke survivors. This work bridges the gap between laboratory demonstrations and practical clinical applications. His subsequent study, "Online Robotic Arm Control with a Deep Learning-Based EEG BCI" (2024, 2 citations), further refines this approach, showcasing how deep neural networks can decode complex motor intentions in real-time. By directly translating EEG signals into precise robotic movements, Forenzo’s contributions are paving the way for next-generation assistive technologies that could dramatically improve the quality of life for individuals with severe motor impairments.