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| Research on Asynchronous Steady-State Visual Evoked Potential Brain-Computer Interface Technology and Its Applications |
| LIU Xuyang1, LI Hui1, XU Guanghua1,2,3,4 |
1. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049;
2. State Key Laboratory for Manufacturing System Engineering, Xi'an Jiaotong University, Xi'an 710054;
3. The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061;
4. State Industry-Education Integration Center for Medical Innovations, Xi'an Jiaotong University, Xi'an 710049 |
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Abstract Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) are regarded as mainstream research paradigms among non-invasive brain-computer interfaces due to their high signal-to-noise ratio, low training cost, and high information transfer rate. Compared with traditional synchronous control modes, asynchronous SSVEP-BCIs are free from fixed timing constraints and are capable of adapting to free, continuous and natural human-computer interaction scenarios, offering outstanding advantages for real-world deployment. In this paper, the research status and development of asynchronous SSVEP-BCI technology are systematically reviewed from three perspectives: brain-state detection, electroencephalogram (EEG) decoding, and asynchronous brain-controlled applications. In the two core technical areas of brain state detection and EEG decoding, the existing technical solutions and their limitations are analyzed. New decoding techniques based on EEG nonlinear dynamics are discussed in detail. New approaches for weak EEG feature extraction and the suppression of strong noise are introduced. In terms of application, diverse application scenarios of asynchronous SSVEP-BCIs are presented. Brain-controlled hybrid intelligent robotic systems are highlighted. Finally, current bottlenecks in asynchronous SSVEP-BCI technology are summarized. It is pointed out that nonlinear dynamics modeling and decoding, together with the integration of multiple intelligent technologies, are the development directions for constructing high-performance and robust asynchronous BCIs.
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Received: 12 May 2026
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| Fund:General Program of National Natural Science Foundation of China(No.51775415), Science and Technology Innovation 2030-"Brain Science and Brain-Inspired Intelligence" Major Project of China(Young Scientists Program)(No.2022ZD0209800), Key Research and Development Project in Shaanxi Province(No.2025PT-ZCK-06) |
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Corresponding Authors:
XU Guanghua, Ph.D., professor. His research interests include brain-computer interfaces and rehabilitation robotics, biomechatronic signal processing, networked monitoring, and inte-lligent diagnosis.
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| About author:: About Author:
LIU Xuyang, Ph.D. candidate. His research interests include wearable brain-computer interfaces.
LI Hui, Ph.D. candidate. His research interests include SSVEP-based EEG decoding.Academic Papers of the 28th Annual Meeting of the China Association for Science and Technology |
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2026, Vol. 39 
