Peng Xiong
Papers
1
Total Citations
51
H-Index
1
About
Dr. Peng Xiong is a leading researcher in brain–computer interface (BCI) systems, with a primary focus on motor imagery (MI) electroencephalography (EEG) decoding. His most cited work, "Multiscale space-time-frequency feature-guided multitask learning CNN for motor imagery EEG classification" (2021, 51 citations), addresses a critical challenge in BCI: accurately translating neural activity into external commands. Dr. Xiong’s major contribution lies in developing a novel multitask learning convolutional neural network (CNN) that integrates multiscale spatial, temporal, and frequency features. This approach significantly enhances classification performance, moving beyond traditional single-scale methods to capture the complex, non-stationary nature of EEG signals. By guiding the network with feature-level insights, his work improves the robustness and accuracy of MI-based BCIs, directly impacting assistive technologies for individuals with motor disabilities. Dr. Xiong’s research has garnered growing attention, reflecting its practical importance in advancing human-computer interaction. His innovative framework not only pushes the boundaries of EEG signal processing but also offers a scalable solution for real-world BCI applications, making him a notable figure in the field of neural engineering.
Research Focus
Key Achievements
Top Papers
- 1