Papers
17
Total Citations
231
H-Index
6
About
Guoli Song is a multidisciplinary researcher whose work spans brain-machine interfaces, rehabilitation robotics, surgical robot control, and robotic kinematics. His most influential contribution, "Decoding multiclass motor imagery EEG from the same upper limb by combining Riemannian geometry features and partial least squares regression" (2020, 89 citations), addresses a fundamental challenge in EEG-based brain-machine interfaces — accurately classifying motor imagery signals despite low spatial resolution and poor signal-to-noise ratios. This work has become a key reference in the BMI community. Song has also made notable strides in rehabilitation technology, developing a bioinspired musculoskeletal soft wrist exoskeleton for stroke patients (2020, 45 citations) that prioritizes natural human-machine coupling and humanoid kinematics. His research into gaze-based interaction for surgical robot control (2019, 25 citations) demonstrates a commitment to intuitive, accessible human-computer interfaces in clinical settings. Further contributions include closed-loop inverse kinematics solutions for redundant manipulators and medical image reconstruction from X-ray data, illustrating his broad engineering versatility. Across his body of work, Song consistently bridges neuroscience, biomechanics, and intelligent robotics to advance both clinical rehabilitation and precision surgical systems.
Research Focus
Key Achievements
Top Papers
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- 6Data fusion of multiple kinect sensors for a rehabilitation system9 citations · 2016
- 7X-CTCANet: 3D spinal CT reconstruction directly from 2D X-ray images5 citations · 2024
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- 9Mechanical and Control Design of a Hollow Modular Joint3 citations · 2015
- 10A closed-loop framework for inverse kinematics of the 7-DOF manipulator3 citations · 2016