Steady-State Visual Evoked Potential Detection Algorithm Based on Harmonics Correction and Generalization
LÜ Yanhao1,2, LUO Tianjian1,2
1. College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117; 2. Digital Fujian Internet-of-Thing Laboratory of Environmental Monitoring, Fujian Normal University, Fuzhou 350117
Abstract Steady-state visual evoked potential(SSVEP) is widely utilized in the design of brain-computer interfaces with high information transfer rates(ITR). Existing SSVEP detection algorithms enhance SSVEP components with high signal-noise ratios while suppressing non-SSVEP components by computing optimal spatial filters. However, these algorithms heavily depend on the quality of training samples, resulting in performance degradation in the early stage of SSVEP presentation. To overcome this limitation, a steady-state visual evoked potential detection algorithm based on harmonics correction and generalization(HCG) is proposed. First, the average training templates are corrected by the sine-cosine harmonics reference signals to enhance ITR during the early-middle stage of SSVEP presentation. Subsequently, the task-related component analysis is employed to enhance ITR during the latter stage of SSVEP presentation. For SSVEP detection, the average training templates are weighted and matched by these two types to ensure high ITR during all stages of SSVEP presentation. Comparative experiments are conducted on two public SSVEP datasets. Experiments demonstrate that HCG outperforms the current benchmark algorithms in terms of detection accuracy and ITR, as well as computational efficiency. Moreover, ablation experiments confirm that the proposed algorithm meets lower calibration data requirements, providing a new solution for the design of resource-constrained brain-computer interfaces.
Fund:National Natural Science Foundation of China(No.62106049), Natural Science Foundation of Fujian Province(No.2
Corresponding Authors:LUO Tianjian, Ph.D., associate professor. His research interests include pattern recognition, brain-computer interface, and EEG signal processing.
About author:: LÜ Yanhao, Master student. His research interests include brain-computer interface, pattern recognition, and EEG signal proce-ssing.
[1] 肖晓琳,辛风然,梅杰,等.自适应脑机接口研究综述.电子与信息学报, 2023, 45(7): 2386-2394. (XIAO X L, XIN F R, MEI J, et al. A Review of Adaptive Brain-Computer Interface Research. Journal of Electronics & Information Technology, 2023, 45(7): 2386-2394.) [2] STEGMAN P, CRAWFORD C S, ANDUJAR M, et al. Brain-Computer Interface Software: A Review and Discussion. IEEE Transactions on Human-Machine Systems, 2020, 50(2): 101-115. [3] 郭晓冰,徐光华,李辉,等.联合经验模式分解和混沌理论的稳态视觉诱发电位脑电识别.西安交通大学学报, 2024, 58(6): 34-42. (GUO X B, XU G H, LI H, et al. Combined Empirical Pattern Decomposition and Chaos Theory for Steady-State Visual Evoked Potential Electroencephalogram Recognition. Journal of Xi'an Jiaotong University, 2024, 58(6): 34-42.) [4] HUANG W C, ZHANG P Q, YU T Y, et al. A P300-Based BCI System Using Stereoelectroencephalography and Its Application in a Brain Mechanistic Study. IEEE Transactions on Biomedical Engineering, 2021, 68(8): 2509-2519. [5] 郭孟澳,杨帮华,耿亦婷,等.基于增强现实和稳态视觉诱发电位的视觉目标检测系统.生物医学工程学杂志, 2024, 41(4): 684-691. (GUO M A, YANG B H, GENG Y T, et al. Visual Object Detection System Based on Augmented Reality and Steady-State Visual Evoked Potential. Journal of Biomedical Engineering, 2024, 41(4): 684-691.) [6] ARPAIA P, ESPOSITO A, NATALIZIO A, et al. How to Successfully Classify EEG in Motor Imagery BCI: A Metrological Analysis of the State of the Art. Journal of Neural Engineering, 2022, 19(3). DOI: 10.1088/1741-2552/ac74e0. [7] 郑晨光,刘洋,肖晓琳,等.高频稳态视觉诱发电位脑-机接口研究进展.生物医学工程学杂志, 2023, 40(1): 155-162. (ZHENG C G, LIU Y, XIAO X L, et al. Advances in Brain-Computer Interface Based on High-Frequency Steady-State Visual Evoked Potential. Journal of Biomedical Engineering, 2023, 40(1): 155-162.) [8] WANG Y J, WANG R P, GAO X R, et al. A Practical VEP-Based Brain-Computer Interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006, 14(2): 234-240. [9] JIA C, GAO X R, HONG B, et al. Frequency and Phase Mixed Coding in SSVEP-Based Brain-Computer Interface. IEEE Transactions on Biomedical Engineering, 2011, 58(1): 200-206. [10] WONG C M, WANG B Y, WANG Z, et al. Spatial Filtering in SSVEP-Based BCIs: Unified Framework and New Improvements. IEEE Transactions on Biomedical Engineering, 2020, 67(11): 3057-3072. [11] SÖZER A T, FIDAN C B. Novel Spatial Filter for SSVEP-Based BCI: A Generated Reference Filter Approach. Computers in Biology and Medicine, 2018, 96: 98-105. [12] 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. [13] 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. [14] SHAO X H, LIN M X.Filter Bank Temporally Local Canonical Correlation Analysis for Short Time Window SSVEPs Classification. Cognitive Neurodynamics, 2020, 14: 689-696. [15] YIN X G, LIN M X.Multi-information Improves the Performance of CCA-Based SSVEP Classification. Cognitive Neurodynamics, 2024, 18(1): 165-172. [16] ZHANG Y, ZHOU G X, JIN J, et al. L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 21(6): 887-896. [17] ZHANG Y, ZHOU G X, JIN J, et al. Sparse Bayesian Multiway Canonical Correlation Analysis for EEG Pattern Recognition. Neurocomputing, 2017, 225: 103-110. [18] CHERLOO M N, AMIRI H K, DALIRI M R. Spatio-Spectral CCA(SS-CCA): A Novel Approach for Frequency Recognition in SSVEP-Based BCI. Journal of Neuroscience Methods, 2022, 371. DOI: 10.1016/j.jneumeth.2022.109499. [19] YIN X G, LIN M X, LIANG J T, et al. Time-Frequency Feature Extraction Based on Multivariable Synchronization Index for Trai-ning-Free SSVEP-Based BCI. Cognitive Neurodynamics, 2024, 18(4): 1733-1741. [20] NAKANISHI M, WANG Y J, WANG Y T, et al. A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials. PloS One, 2015, 10(10). DOI: 10.1371/journal.pone.0140703. [21] 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. [22] WONG C M, WAN F, WANG B Y, et al. Learning Across Multi-Stimulus Enhances Target Recognition Methods in SSVEP-Based BCIs. Journal of Neural Engineering, 2020, 17(1). DOI: 10.1088/1741-2552/ab2373. [23] 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. [24] SUN Q, CHEN M Y, ZHANG L, et al. Improving SSVEP Identification Accuracy via Generalized Canonical Correlation Analysis // Proc of the 10th International IEEE/EMBS Conference on Neural Engineering. Washington, USA: IEEE, 2021: 61-64. [25] TANG J B, XU M P, HAN J, et al. Optimizing SSVEP-Based BCI System Towards Practical High-Speed Spelling. Sensors, 2020, 20(15). DOI: 10.3390/s20154186. [26] 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. [27] LUO T J, WU T.Sum of Similarity-Regularized Squared Correlations for Enhancing SSVEP Detection. Artificial Intelligence in Medicine, 2025, 162. DOI: 10.1016/j.artmed.2025.103100. [28] WANG Y J, CHEN X G, GAO X R, et al. A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(10): 1746-1752. [29] LIU B C, HUANG X S, WANG Y J, et al. BETA: A Large Benchmark Database Toward SSVEP-BCI Application. Frontiers in Neuroscience, 2020, 14. DOI: 10.3389/fnins.2020.00627. [30] 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. [31] ZHANG Y, ZHOU G X, JIN J, et al. Frequency Recognition in SSVEP-Based BCI Using Multiset Canonical Correlation Analysis. International Journal of Neural Systems, 2014, 24(4). DOI: 10.1142/S0129065714500130. [32] BIAN R, WU H Y, LIU B, et al. Small Data Least-Squares Transformation(sd-LST) for Fast Calibration of SSVEP-Based BCIs. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 31: 446-455. [33] WANG Z, WONG C M, WANG B Y, et al. Compact Artificial Neural Network Based on Task Attention for Individual SSVEP Recognition with Less Calibration. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, 31: 2525-2534.
2025, Vol. 38 
