Papers
3
Total Citations
24
H-Index
2
About
Xiaorong Gao is a researcher whose work spans brain-computer interfaces (BCI), non-destructive testing, and intelligent recognition systems, with primary expertise in electroencephalography (EEG)-based neural engineering. Gao's most impactful contribution lies in advancing motor imagery (MI) BCI research, most notably through the development of a multi-day, high-quality EEG dataset designed to address one of the field's most persistent challenges: achieving reliable classification performance across multiple recording sessions. This foundational work, already garnering 17 citations shortly after its 2025 publication, directly confronts the significant hurdles of inter-session variability and low signal-to-noise ratios that have long limited real-world BCI deployment. Beyond neural engineering, Gao has demonstrated a breadth of technical expertise through contributions to railway safety, including work on an automated laser-ultrasonic inspection system for in-service wheelsets, and more recently through lightweight deep learning approaches to drill tool recognition. With a cumulative citation profile reflecting growing influence across diverse applied domains, Gao represents a researcher committed to translating rigorous signal processing and machine learning methodologies into practical, safety-critical engineering solutions. Their work offers valuable resources and insights for students entering both BCI research and industrial inspection technologies.
Research Focus
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Top Papers
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