Xuepu Wang
Papers
1
Total Citations
2
H-Index
1
About
Xuepu Wang is a researcher specializing in brain-computer interfaces (BCI), particularly focusing on electroencephalography (EEG)-based systems. Their key research areas include rapid serial visual presentation (RSVP) paradigms and cross-subject classification, where they address the challenge of developing subject-agnostic models that generalize across individuals. Wang’s major contribution is the development of a multi-scale EEGNet, a convolutional neural network architecture designed to enhance event-related potential (ERP) classification in RSVP-based BCI systems. This work, published in 2022, has garnered 2 citations and represents a significant step toward practical, user-independent BCI applications by leveraging prior knowledge from multiple subjects’ EEG data. By tackling the cross-subject variability that often hinders real-world BCI deployment, Wang’s research advances the field’s goal of creating robust, plug-and-play neural interfaces. Their work is particularly notable for its focus on improving classification accuracy without requiring per-subject calibration, a key barrier to widespread BCI adoption.
Research Focus
Key Achievements
Top Papers
- 1A multi-scale EEGNet for cross-subject RSVP-based BCI system2 citations · 2022