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About

Jingsheng Tang is a pioneering researcher in brain-computer interface (BCI) technology, with a focused specialization in EEG-based human-machine interaction systems designed to enhance the lives of individuals with physical disabilities. His work spans the development of innovative BCI-driven assistive technologies, including smart wheelchairs, robotic arm control systems, and mobile platforms, positioning him as a significant contributor to the rehabilitation engineering field. Tang's most influential work, "Towards BCI-Actuated Smart Wheelchair System" (2018), has garnered 118 citations, reflecting its substantial impact on accessible mobility research. His broader portfolio demonstrates expertise in steady-state visual evoked potentials (SSVEP), P300 paradigms, and shared-control frameworks, with notable contributions including a novel two-layer interface for humanoid robot navigation and an asynchronous SSVEP-BCI system leveraging multivariate synchronization index variance statistics. A recurring theme throughout Tang's research is the challenge of improving signal-to-noise ratios in EEG-based systems, which he addresses through hybrid control architectures and advanced signal processing techniques. His work collectively advances the frontier of non-muscular communication technologies, offering meaningful rehabilitation pathways for patients with upper limb disabilities and mobility impairments, and inspiring a growing community of BCI researchers worldwide.