Xinyong Luo

Papers

Total Citations

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About

Xinyong Luo is a leading researcher in the field of Brain-Computer Interfaces (BCI) and rehabilitation robotics, with a specific focus on decoding Motor Imagery Electroencephalography (MI-EEG) signals. His major contributions lie in developing sophisticated feature extraction and recognition algorithms that enable robotic-assisted rehabilitation systems for stroke survivors. Luo pioneered the application of Locally Linear Embedding (LLE) algorithms for feature extraction and visualization of MI-EEG, a technique that significantly improved the performance of BCI-based rehabilitation systems. His work on combining Long Short-Term Memory (LSTM) networks with wavelet coefficients for MI-EEG recognition represents a breakthrough in time-frequency feature analysis, achieving superior recognition accuracy compared to conventional methods. With his most-cited papers accumulating 37 and 29 citations respectively, Luo's research has established foundational methodologies in the field. His work addresses the critical challenge of translating neural signals into effective rehabilitation interventions, particularly for patients with poorly functioning hemiparetic arms. By advancing the key techniques of MI-EEG feature extraction and recognition, Luo continues to push the boundaries of how BCI technology can restore motor function and improve quality of life for stroke survivors.