Papers
6
Total Citations
177
H-Index
6
About
Francis R. Willett is a leading researcher in the field of brain-computer interfaces (BCIs) and neuroprosthetics, with a focus on restoring movement and communication for people with paralysis. His major contributions span signal processing, neural decoding, and the application of deep learning to neuroprosthetic control. Willett pioneered methods to reduce artifacts in microelectrode brain recordings caused by functional electrical stimulation, a critical step for integrating BCIs with real-world movement restoration. He also advanced BCI performance by decoding intended future movements, minimizing delays that degrade user control. Notably, Willett demonstrated the first brain control of bimanual movement using recurrent neural networks, a breakthrough for multi-effector prosthetics, and developed a high-performance finger-decoding BCI for quadcopter control, enabling leisure and social activities. His work on translating deep learning to neuroprosthetics has achieved over 100 citations across key papers, with his 2017 signal processing paper alone garnering 59 citations. Willett’s research has been recognized for its translational impact, including author responses to high-profile studies on communication BCIs for people with paralysis. His innovations are shaping the future of intuitive, high-speed neural control for assistive technologies.
Research Focus
Key Achievements
Top Papers
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- 3Brain control of bimanual movement enabled by recurrent neural networks44 citations · 2024
- 4Translating deep learning to neuroprosthetic control14 citations · 2023
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