Lipeng Zhang
Papers
2
Total Citations
27
H-Index
2
About
Lipeng Zhang is a leading researcher in neurorehabilitation engineering, with a primary focus on brain–computer interfaces (BCI) and motor imagery decoding for upper limb recovery. His work addresses a critical gap in the field by pioneering methods to recognize single upper limb motor imagery tasks from EEG signals, a challenge that most BCI systems have overlooked in favor of bilateral tasks. In his highly cited 2023 study, Zhang introduced a multi-branch fusion convolutional neural network that achieved robust classification of these nuanced neural patterns, earning 24 citations and establishing a new direction for targeted neurorehabilitation. He further advanced the field by developing a hybrid BCI system that integrates both active and passive rehabilitation training, enabling patients with craniocerebral injuries to engage more effectively in their recovery. This innovative approach, published in 2022, underscores his commitment to translating neural decoding into practical, patient-centered therapies. Zhang’s contributions are pivotal for the future of robot-assisted rehabilitation and closed-loop neurofeedback, positioning him as a key figure in the intersection of machine learning and clinical neuroscience.
Research Focus
Key Achievements
Top Papers
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