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About

Xiaoyuan Dang is a leading researcher in brain–computer interfaces (BCIs) and intelligent signal processing, with a particular focus on motor imagery (MI) EEG recognition and transfer learning. Dang’s most influential work, “Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network,” has garnered 162 citations, establishing a robust framework for decoding neural signals with high accuracy. A key contribution is the development of a transfer learning method that uses rotation alignment with Riemannian mean to tackle the notoriously difficult challenges of cross-session and cross-subject classification in BCIs, a breakthrough that has direct applications in assistive technologies such as wheelchair control. This work, cited 21 times, demonstrates Dang’s ability to bridge theoretical advances with real-world impact. Additionally, Dang has explored the intersection of intelligent robotics and sustainable energy, applying multimedia quality evaluation and numerical control technology to improve the stability of new energy power generation systems. Through these diverse contributions, Dang has proven to be a versatile innovator, advancing both neural engineering and applied intelligent systems.