Xinhong Hei
Papers
2
Total Citations
193
H-Index
2
About
Xinhong Hei is a leading researcher at the intersection of artificial intelligence and brain-computer interfaces (BCIs), with a primary focus on motion trajectory prediction and motor imagery decoding. His most impactful work, "Motion trajectory prediction based on a CNN-LSTM sequential model" (2020), has garnered 189 citations, establishing a foundational deep learning framework that integrates convolutional neural networks with long short-term memory networks for precise, real-time movement forecasting. This contribution has been instrumental in advancing human-robot interaction and assistive technologies. Hei also played a pivotal role in the organization and analysis of the Algorithm Contest of Calibration-free Motor Imagery BCI, part of the BCI Controlled Robot Contest at the World Robot Contest 2021. His survey of this competition, which featured eleven teams employing innovative EEG-based methods, highlights his commitment to pushing the boundaries of calibration-free BCI systems—a critical step toward practical, user-friendly neural interfaces. Through his work, Hei continues to shape the future of intelligent systems that seamlessly bridge human intent and robotic action.
Research Focus
Key Achievements
Top Papers
- 1Motion trajectory prediction based on a CNN-LSTM sequential model189 citations · 2020
- 2