Bohui Hao

Chongqing University of Posts and Telecommunications

Papers

Total Citations

H-Index

About

Bohui Hao is a researcher whose work bridges the frontiers of brain–computer interfaces (BCIs) and natural language processing, with a focus on making intelligent systems more adaptive and context-aware. His most cited paper, "Transfer Learning: Rotation Alignment With Riemannian Mean for Brain–Computer Interfaces and Wheelchair Control" (2021, 21 citations), tackles the persistent challenge of cross-session and cross-subject variability in motor imagery EEG signals. By introducing a novel transfer learning method that aligns Riemannian means, Hao’s work enables more reliable BCI control for assistive technologies like wheelchairs, directly impacting real-world rehabilitation and accessibility. In parallel, his research on "Learning Long-text Semantic Similarity with Multi-Granularity Semantic Embedding Based on Knowledge Enhancement" (2020, 5 citations) advances the understanding of complex textual relationships, integrating external knowledge to improve semantic matching. This dual expertise—spanning neural signal processing and semantic understanding—demonstrates Hao’s commitment to developing robust, generalizable AI systems. His contributions are particularly notable for addressing the "domain shift" problem in BCIs, a critical barrier to practical deployment, and for enhancing the interpretability of long-text comparisons. Hao’s work continues to inspire students and researchers exploring the intersection of human cognition and machine learning.