Papers
2
Total Citations
16
H-Index
2
About
Xingsen Li is a researcher advancing the intersection of neuroscience and computational data analysis, with a primary focus on electroencephalography (EEG) signal processing and brain–computer interface (BCI) applications. His work centers on developing innovative methods to decode complex brain wave patterns for practical control systems. A key contribution is his research on EEG self-adjusting data analysis using optimized sampling techniques for robot control, which has garnered 14 citations and demonstrates a novel approach to non-invasive BCI systems. Li also pioneered the construction of weighted networks based on EEG data segmentation for brain wave pattern recognition, offering a sophisticated framework for understanding individual neural signatures. By applying data mining techniques to EEG datasets, he addresses the challenge of translating subtle brain activity into actionable commands, bridging the gap between raw neural signals and real-world robotic applications. His work holds promise for assistive technologies and human-machine interaction, reflecting a commitment to making BCI systems more adaptive and reliable through advanced analytical methods.
Research Focus
Key Achievements
Top Papers
- 1
- 2