Jianli Yang
Papers
1
Total Citations
51
H-Index
1
About
Dr. Jianli Yang is a leading researcher in brain–computer interfaces (BCI) and neural signal processing, with a primary focus on motor imagery (MI) EEG classification. His most cited work, "Multiscale space-time-frequency feature-guided multitask learning CNN for motor imagery EEG classification" (2021, 51 citations), introduces a novel deep learning architecture that integrates multiscale spatiotemporal and frequency features through a multitask learning framework. This approach significantly enhances the accuracy and robustness of decoding human intent from neural activity, addressing a critical challenge in BCI systems. Dr. Yang’s contributions advance the practical deployment of non-invasive BCIs for communication and control, particularly for individuals with motor disabilities. His work is widely recognized for bridging signal processing and deep learning, with his 2021 paper serving as a key reference in the field. By improving the reliability of MI-EEG classification, Dr. Yang is helping to make BCI technology more accessible and effective, with potential applications in rehabilitation, assistive devices, and human-machine interaction. His research continues to inspire new directions in neural decoding and adaptive learning systems.
Research Focus
Key Achievements
Top Papers
- 1