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
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.
刘旭阳, 李辉, 徐光华. 异步稳态视觉诱发电位脑机接口技术研究及其应用[J]. 模式识别与人工智能, 2026, 39(6): 473-488.
LIU Xuyang, LI Hui, XU Guanghua. Research on Asynchronous Steady-State Visual Evoked Potential Brain-Computer Interface Technology and Its Applications. Pattern Recognition and Artificial Intelligence, 2026, 39(6): 473-488.
[1] WOLPAW J R, BIRBAUMER N, MCFARLAND D J, et al. Brain-Computer Interfaces for Communication and Control. Clinical Neurophysiology, 2002, 113(6): 767-791. [2] NICOLAS-ALONSO L F, GOMEZ-GIL J. Brain Computer Interfaces, a Review. Sensors, 2012, 12(2): 1211-1279. [3] RAMADAN R A, VASILAKOS A V.Brain Computer Interface: Con-trol Signals Review. Neurocomputing, 2017, 223: 26-44. [4] ABIRI R, BORHANI S, SELLERS E W, et al. A Comprehensive Review of EEG-Based Brain-Computer Interface Paradigms. Journal of Neural Engineering, 2019, 16. DOI: 10.1088/1741-2552/aaf12e. [5] OJHA M K, MUKUL M K.Detection of Target Frequency from SSVEP Signal Using Empirical Mode Decomposition for SSVEP Based BCI Inference System. Wireless Personal Communications, 2021, 116: 777-789. [6] YANG C, YAN X Y, WANG Y J, et al. Spatio-Temporal Equalization Multi-window Algorithm for Asynchronous SSVEP-Based BCI. Journal of Neural Engineering, 2021, 18. DOI: 10.1088/1741-2552/ac127f. [7] ABU-ALQUMSAN M, PEER A.Advancing the Detection of Steady-State Visual Evoked Potentials in Brain-Computer Interfaces. Journal of Neural Engineering, 2016, 13. DOI: 10.1088/1741-2560/13/3/036005. [8] DA CRUZ J N, WAN F, WONG C M, et al. Adaptive Time-Window Length Based on Online Performance Measurement in SSVEP-Based BCIs. Neurocomputing, 2015, 149(A): 93-99. [9] XIA B, LI X, XIE H, et al. Asynchronous Brain-Computer Interface Based on Steady-State Visual-Evoked Potential. Cognitive Computation, 2013, 5: 243-251. [10] 许敏鹏,王有良,梅杰,等.非侵入式异步脑机接口技术研究综述.信号处理, 2023, 39(8): 1386-1398. (XU M P, WANG Y L, MEI J, et al. Review of Non-invasive Asynchronous Brain-Computer Interface Technology. Journal of Signal Processing, 2023, 39(8): 1386-1398.) [11] 施文强,肖晓琳,刘爽,等.混合范式脑-机接口研究进展综述.中国生物医学工程学报, 2022, 41(1): 73-85. (SHI W Q, XIAO X L, LIU S, et al. A Review of Research Progress of Hybrid Brain-Computer Interface. Chinese Journal of Biomedical Engineering, 2022, 41(1): 73-85.) [12] 雍颖琼,张宏江,程奇峰,等.混合脑机接口及其研究进展.计算机测量与控制, 2020, 28(9): 9-13, 28. (YONG Y Q, ZHANG H J, CHENG Q F, et al. Research Deve-lopment on Hybrid Brain-Computer Interface. Computer Measurement & Control, 2020, 28(9): 9-13, 28.) [13] ERKAN E, AKBABA M.A Study on Performance Increasing in SSVEP Based BCI Application. Engineering Science and Techno-logy: An International Journal, 2018, 21(3): 421-427. [14] ZHU Y L, LI Y, LU J L, et al. A Hybrid BCI Based on SSVEP and EOG for Robotic Arm Control. Frontiers in Neurorobotics, 2020, 14. DOI: 10.3389/fnbot.2020.583641. [15] SUN J X, LIU Y D.A Hybrid Asynchronous Brain-Computer Interface Based on SSVEP and Eye-Tracking for Threatening Pedestrian Identification in Driving. Electronics, 2022, 11(19). DOI: 10.3390/electronics11193171. [16] CHAI X K, ZHANG Z M, GUAN K, et al. A Hybrid BCI-Controlled Smart Home System Combining SSVEP and EMG for Individuals with Paralysis. Biomedical Signal Processing and Control, 2020, 56. DOI: 10.1016/j.bspc.2019.101687. [17] LI Y Q, PAN J H, WANG F, et al. A Hybrid BCI System Combining P300 and SSVEP and Its Application to Wheelchair Control. IEEE Transactions on Biomedical Engineering, 2013, 60(11): 3156-3166. [18] HUANG J Y, QIU L N, LIN Q M, et al. Hybrid Asynchronous Brain-Computer Interface for Yes/No Communication in Patients with Disorders of Consciousness. Journal of Neural Engineering, 2021, 18(5). DOI: 10.1088/1741-2552/abf00c. [19] DUAN F, LIN D X, LI W Y, et al. Design of a Multimodal EEG-Based Hybrid BCI System with Visual Servo Module. IEEE Tran-sactions on Autonomous Mental Development, 2015, 7(4): 332-341. [20] CHIANG C H, WON S M, ORSBORN A L, et al. Development of a Neural Interface for High-Definition, Long-Term Recording in Rodents and Nonhuman Primates. Science Translational Medicine, 2020, 12(538). DOI: 10.1126/scitranslmed.aay4682. [21] DU J L, KE Y F, LIU P X, et al. A Two-Step Idle-State Detection Method for SSVEP BCI // Proc of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Socie-ty. Washington, USA: IEEE, 2019: 3095-3098. [22] CHEN L L, CHEN P F, ZHAO S K, et al. Adaptive Asynchronous Control System of Robotic Arm Based on Augmented Reality-Assisted Brain-Computer Interface. Journal of Neural Engineering, 2021, 18(6). DOI: 10.1088/1741-2552/ac3044. [23] CECOTTI H.A Self-Paced and Calibration-Less SSVEP-Based Brain-Computer Interface Speller. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010, 18(2): 127-133. [24] DIEZ P F, MUT V A, PERONA E M A, et al. Asynchronous BCI Control Using High-Frequency SSVEP[J/OL]. [2026-04-23]. http://www.jneuroengrehab.com/content/8/1/39. [25] PAN J H, LI Y Q, ZHANG R, et al. Discrimination Between Control and Idle States in Asynchronous SSVEP-Based Brain Switches: A Pseudo-Key-Based Approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 21(3): 435-443. [26] AJAMI S, MAHNAM A, ABOOTALEBI V.An Adaptive SSVEP-Based Brain-Computer Interface to Compensate Fatigue-Induced Decline of Performance in Practical Application. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(11): 2200-2209. [27] PORYZALA P, MATERKA A.Cluster Analysis of CCA Coefficients for Robust Detection of the Asynchronous SSVEPs in Brain-Computer Interfaces. Biomedical Signal Processing and Control, 2014, 10: 201-208. [28] FRIMAN O, VOLOSYAK I, GRÄSER A. Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces. IEEE Transactions on Biomedical Engineering, 2007, 54(4): 742-750. [29] SUEFUSA K, TANAKA T.Asynchronous Brain-Computer Interfacing Based on Mixed-Coded Visual Stimuli. IEEE Transactions on Biomedical Engineering, 2018, 65(9): 2119-2129. [30] WANG R, ZHOU T Y, LI Z,et al. Using Oscillatory and Aperiodic Neural Activity Features for Identifying Idle State in SSVEP-Based BCIs Reduces False Triggers. Journal of Neural Enginee-ring, 2023, 20(6). DOI: 10.1088/1741-2552/ad1054. [31] 张洪欣,王俊淞,杨晨.面向SSVEP-BCI的最大后验准则异步检测算法.北京邮电大学学报, 2023, 46(6): 15-19. (ZHANG H X, WANG J S, YANG C.An Asynchronous Detection Algorithm of SSVEP-BCI Based on Maximum Posterior Crite-rion. Journal of Beijing University of Posts and Telecommunications, 2023, 46(6): 15-19.) [32] 丛艳平,曹林林,张伟,等.一种融合注意力检测和意图识别的异步脑控方法.燕山大学学报, 2023, 47(2): 121-126. (CONG Y P, CAO L L, ZHANG W, et al. An Asynchronous Brain-Computer Interface Method Fusing Attention Detection and Intention Recognition. Journal of Yanshan University, 2023, 47(2): 121-126.) [33] 徐光华,郭晓冰,白淑文,等. 一种用于SSVEP信号异步控制态判别的方法:中国, 202411309337.2024-12-17. (XU G H, GUO X B, BAI S W ,et al. A Method for Discrimina-ting Asynchronous Control States of SSVEP Signals: China. A Method for Discrimina-ting Asynchronous Control States of SSVEP Signals: China, CN202411309337.2024-12-17.) [34] 邱爽,杨帮华,陈小刚,等.非侵入式脑-机接口编解码技术研究进展.中国图象图形学报, 2023, 28(6): 1543-1566. (QIU S, YANG B H, CHEN X G, et al. A Survey on Encoding and Decoding Technology of Non-invasive Brain-Computer Interface. Journal of Image and Graphics, 2023, 28(6): 1543-1566.) [35] HEIDARI H, EINALOU Z.SSVEP Extraction Applying Wavelet Transform and Decision Tree with Bays Classification. International Clinical Neuroscience Journal, 2017, 4(3): 91-97. [36] LIN Z L, ZHANG C S, WU W, et al. Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs. IEEE Transactions on Biomedical Engineering, 2006, 53(12): 2610-2614. [37] BIN G Y, GAO X R, WANG Y J, et al. A High-Speed BCI Based on Code Modulation VEP. Journal of Neural Engineering, 2011, 8(2). DOI: 10.1088/1741-2560/8/2/025015. [38] ZHANG Y, ZHOU G X, ZHAO Q B, et al. Multiway Canonical Correlation Analysis for Frequency Components Recognition in SSVEP-Based BCIs // Proc of the 18th International Conference on Neural Information Processing. Berlin, Germany: Springer, 2011: 287-295. [39] JIAO Y, ZHANG Y, WANG Y, et al. A Novel Multilayer Correlation Maximization Model for Improving CCA-Based Frequency Re-cognition in SSVEP Brain-Computer Interface. International Journal of Neural Systems, 2017, 27(8). DOI: 10.1142/S0129065717500393. [40] PAN J, GAO X R, DUAN F, et al. Enhancing the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces Using Phase Constrained Canonical Correlation Analysis. Journal of Neural Engineering, 2011, 8(3). DOI: 10.1088/1741-2560/8/3/036027. [41] CHEN X G, WANG Y J, GAO S K, et al. Filter Bank Canonical Correlation Analysis for Implementing a High-Speed SSVEP-Based Brain-Computer Interface. Journal of Neural Engineering, 2015, 12(4). DOI: 10.1088/1741-2560/12/4/046008. [42] NAKANISHI M, WANG Y J, CHEN X G, et al. Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis. IEEE Transactions on Biomedical Engineering, 2018, 65(1): 104-112. [43] SUN Q, CHEN M Y, ZHANG L, et al. Similarity-Constrained Task-Related Component Analysis for Enhancing SSVEP Detection. Journal of Neural Engineering, 2021, 18(4). DOI: 10.1088/1741-2552/abfdfa. [44] LIU B C, CHEN X G, SHI N L, et al. Improving the Performance of Individually Calibrated SSVEP-BCI by Task-Discriminant Component Analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 1998-2007. [45] LAWHERN V J, SOLON A J, WAYTOWICH N R, et al. EEGNet: A Compact Convolutional Neural Network for EEG-Based Brain-Computer Interfaces. Journal of Neural Engineering, 2018, 15(5). DOI: 10.1088/1741-2552/aace8c. [46] DING W L, SHAN J H, FANG B, et al. Filter Bank Convolutional Neural Network for Short Time-Window Steady-State Visual Evoked Potential Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 2615-2624. [47] ZHANG X Y, QIU S, GENG M H, et al. Enhancing Detection of SSVEPs for High-Speed Brain-Computer Interface with a Siamese Architecture // Proc of the IEEE International Conference on Bioinformatics and Biomedicine. Washington, USA: IEEE, 2021: 1623-1627. [48] PAN Y D, CHEN J B, ZHANG Y S, et al. An Efficient CNN-LSTM Network with Spectral Normalization and Label Smoothing Technologies for SSVEP Frequency Recognition. Journal of Neural Engineering, 2022, 19(5). DOI: 10.1088/1741-2552/ac8dc5. [49] XU L, JIANG X Y, WANG R M, et al. Decoding SSVEP via Ca-libration-Free TFA-Net: A Novel Network Using Time-Frequency Features. IEEE Journal of Biomedical and Health Informatics, 2025, 29(4): 2400-2412. [50] DING W L, LIU A P, GUAN L, et al. A Novel Data Augmentation Approach Using Mask Encoding for Deep Learning-Based Asyn-chronous SSVEP-BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024, 32: 875-886. [51] ZHANG K, XU G H, DU C H, et al. Weak Feature Extraction and Strong Noise Suppression for SSVEP-EEG Based on Chaotic Detection Technology. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 862-871. [52] CHEN R Q, XU G H, ZHANG H Q, et al. A Novel Untrained SSVEP-EEG Feature Enhancement Method Using Canonical Correlation Analysis and Underdamped Second-Order Stochastic Resonance. Frontiers in Neuroscience, 2023, 17. DOI: 10.3389/fnins.2023.1246940. [53] CHEN R Q, XU G H, ZHANG H Q, et al. Filter Bank Second-Order Underdamped Stochastic Resonance Analysis for Implementing a Short-Term High-Speed SSVEP Detection. NeuroImage, 2024, 285. DOI: 10.1016/j.neuroimage.2023.120501. [54] LI H, XU G H, LI Z J,et al. A Precise Frequency Recognition Method of Short-Time SSVEP Signals Based on Signal Extension. IEEE Transactions on Neural Systems and Rehabilitation Enginee-ring, 2023, 31: 2486-2496. [55] YAN W Q, WU Y C, DU C H, et al. Cross-Subject Spatial Filter Transfer Method for SSVEP-EEG Feature Recognition. Journal of Neural Engineering, 2022, 19(3). DOI: 10.1088/1741-2552/ac6b57. [56] YAN W Q, DU C H, WU Y C, et al. SSVEP-EEG Denoising via Image Filtering Methods. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 1634-1643. [57] WANG Z, WONG C M, ROSA A, et al. Stimulus-Stimulus Transfer Based on Time-Frequency-Joint Representation in SSVEP-Based BCIs. IEEE Transactions on Biomedical Engineering, 2023, 70(2): 603-615. [58] NAKANISHI M, WANG Y J, WANG Y T, et al. A High-Speed Brain Speller Using Steady-State Visual Evoked Potentials. International Journal of Neural Systems, 2014, 24(6). DOI: 10.1142/S0129065714500191. [59] CHEN X G, WANG Y J, NAKANISHI M, et al. Hybrid Frequency and Phase Coding for a High-Speed SSVEP-Based BCI Speller // Proc of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Washington, USA: IEEE, 2014: 3993-3996. [60] YIN E W, ZEYL T, SAAB R, et al. A Hybrid Brain-Computer Interface Based on the Fusion of P300 and SSVEP Scores. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015, 23(4): 693-701. [61] 王忠民,刘攀岩.基于SSVEP的嵌入式中文输入系统.西安邮电大学学报, 2022, 27(4): 74-79. (WANG Z M, LIU P Y.Embedded Chinese Input System Based on SSVEP. Journal of Xi'an University of Posts and Telecommunications, 2022, 27(4): 74-79.) [62] PREETHA S, SASIKALA M.Design of Asynchronous Low-Complexity SSVEP-Based Brain Control Interface Speller. Computers in Biology and Medicine, 2025, 190. DOI: 10.1016/j.compbiomed.2025.110062. [63] MISTRY K S, PELAYO P, ANIL D G, et al. An SSVEP Based Brain Computer Interface System to Control Electric Wheelchairs // Proc of the IEEE International Instrumentation and Measurement Technology Conference. Washington, USA: IEEE, 2018. DOI: 10.1109/I2MTC.2018.8409632. [64] TURNIP A, SOETRAPRAWATA D, TURNIP M, et al. EEG-Based Brain-Controlled Wheelchair with Four Different Stimuli Frequencies. Internetworking Indonesia Journal, 2016, 8(1): 65-69. [65] LOPES A, RODRIGUES J, PERDIGAO J, et al. A New Hybrid Motion Planner: Applied in a Brain-Actuated Robotic Wheelchair. IEEE Robotics and Automation Magazine, 2016, 23(4): 82-93. [66] MOULI S, PALANIAPPAN R, MOLEFI E, et al. In-Ear Electrode EEG for Practical SSVEP BCI. Technologies, 2020, 8(4). DOI: 10.3390/technologies8040063. [67] GAO Q, ZHAO X W, YU X, et al. Controlling of Smart Home System Based on Brain-Computer Interface. Technology and Health Care, 2018, 26(5): 769-783. [68] ADAMS M, BENDA M, SABOOR A, et al. Towards an SSVEP-BCI Controlled Smart Home // Proc of the IEEE International Conference on Systems, Man and Cybernetics. Washington, USA: IEEE, 2019: 2737-2742. [69] SABOOR A, REZEIKA A, STAWICKI P, et al. SSVEP-Based BCI in a Smart Home Scenario // Proc of the 14th International Work-Conference on Artificial Neural Networks. Berlin, Germany: Springer, 2017: 474-485. [70] SHAO L, ZHANG L Y, BELKACEM A N, et al. EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface. Journal of Healthcare Engineering, 2020. DOI: 10.1155/2020/6968713. [71] ZENG X F, ZHU G L, YUE L, et al. A Feasibility Study of SSVEP-Based Passive Training on an Ankle Rehabilitation Robot. Journal of Healthcare Engineering, 2017. DOI: 10.1155/2017/6819056. [72] AI J K, MENG J J, MAI X M, et al. BCI Control of a Robotic Arm Based on SSVEP With Moving Stimuli for Reach and Grasp Tasks. IEEE Journal of Biomedical and Health Informatics, 2023, 27(8): 3818-3829. [73] 谢平,门延帝,甄嘉乐,等. 基于异步稳态视觉诱发电位的脑机融合“第三只手”.生物医学工程学杂志, 2024, 41(4): 664-672. (XIE P, MEN Y D, ZHEN J L, et al. The Supernumerary Robotic Limbs of Brain-Computer Interface Based on Asynchronous Steady-State Visual Evoked Potential. Journal of Biomedical Engineering, 2024, 41(4): 664-672.) [74] 陈玲玲,陈鹏飞,谢良,等.增强现实场景下基于稳态视觉诱发电位的机械臂控制系统.电子与信息学报, 2022, 44(2): 496-506. (CHEN L L, CHEN P F, XIE L, et al. Control System of Robotic Arm Based on Steady-State Visual Evoked Potentials in Augmented Reality Scenarios. Journal of Electronics and Information Technology, 2022, 44(2): 496-506.) [75] 李奇,宗子彦,武岩,等.混合现实场景下结合SSVEP与眼动追踪的脑控机械臂系统.重庆理工大学学报(自然科学), 2024, 38(7): 93-100. (LI Q, ZONG Z Y, WU Y, et al. Research on a Brain Controlled Robotic Arm System Combining SSVEP and Eye-Tracking in Mixed Reality Scenarios. Journal of Chongqing University of Technology(Natural Science), 2024, 38(7): 93-100.)
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