Rongrong Fu

About

Rongrong Fu is a rising researcher in brain–computer interfaces (BCIs) and neural signal processing, with a focus on enhancing human–robot interaction for individuals with movement disorders. Her work centers on developing robust, real-time BCI systems that translate electroencephalography (EEG) signals into precise control commands. In her 2022 study on data augmentation for cross-subject EEG features using Siamese neural networks, Fu addressed a critical challenge in BCI—generalizing models across different users—earning 8 citations for this innovative approach. More recently, her 2024 paper on controlling a robotic arm system with a steady-state visual evoked potential (SSVEP)-based BCI demonstrated how to achieve rapid, accurate online task completion, further advancing assistive robotics. Though early in her career, Fu’s contributions are already shaping the future of non-invasive neural interfaces, bridging the gap between laboratory algorithms and practical, user-friendly devices. Her work holds promise for restoring autonomy to those with severe motor impairments, marking her as a researcher to watch in the evolving landscape of neurotechnology.

Research Focus

Key Achievements

Top Papers

1. 1
   Data augmentation for cross-subject EEG features using Siamese neural network

   8 citations · 2022
2. 2
   Control of the robotic arm system with an SSVEP-based BCI

   8 citations · 2024

Key Collaborators

Contact & Links