Dongxue Lin

Papers

Total Citations

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About

Dongxue Lin is a pioneering researcher in brain-computer interfaces (BCIs), with a focus on hybrid systems that integrate multiple neural signals for enhanced control. Lin’s most cited work, “Design of a Multimodal EEG-based Hybrid BCI System with Visual Servo Module” (2015, 77 citations), introduces a novel framework combining steady-state visual evoked potentials (SSVEP), P300, and motor imagery—bridging the gap between stimulus-dependent and independent paradigms. This hybrid approach enables more flexible and robust command generation, advancing practical BCI applications for robotics and assistive technologies. Lin also contributed to signal detection methodology in “Design of an online BCI system based on CCA detection method” (2015, 5 citations), applying canonical correlation analysis (CCA) to improve SSVEP detection accuracy in real-time systems. By tackling key challenges in multimodal integration and online performance, Lin’s work has laid groundwork for more intuitive and reliable BCI control. With over 80 cumulative citations, Lin’s research continues to influence the development of next-generation neural interfaces, offering promising pathways for human-machine interaction in rehabilitation and beyond.