语义刺激与亮度闪烁诱发的稳态视觉诱发电位响应特性对比研究

doi:10.3969/j.issn.0258-8021.2026.04.003

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Abstract Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCI) provide an efficient communication pathway for individuals with severe motor impairments. Conventional SSVEP paradigms typically rely on luminance flicker stimuli, which primarily engage early visual pathways and may therefore be constrained by the response characteristics of the primary visual cortex. To expand the encoding capacity of SSVEP-based systems, it is necessary to explore alternative stimuli capable of engaging richer visual processing mechanisms. However, the response characteristics of semantic visual stimuli under SSVEP paradigms remain insufficiently investigated. In this study, we compared SSVEP responses evoked by faces, cars, Chinese characters, and luminance flicker at four stimulation frequencies (3, 6, 9, and 12 Hz). The results showed that, compared with luminance flicker, semantic stimuli exhibited distinct temporal, spatial, and spectral response patterns. Among the tested stimuli, Chinese characters showed better overall performance, with higher signal-to-noise ratio (SNR) than faces and cars, and better classification performance than luminance flicker in some time windows. Notably, at 6 Hz, character stimuli produced a broader spatial distribution and a peak fundamental SNR of 13.29 dB. These findings suggest that character-based stimulation may offer a promising alternative to luminance flicker and provide new directions for visual encoding in SSVEP-BCI.

Key words： brain-computer interfaces steady-state visual evoked potential ventral stream semantic stimuli luminance flicker

Received: 23 March 2026

PACS:

R318

Corresponding Authors: ^*E-mail: gxr-dea@mail.tsinghua.edu.cn

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	Li Xiaoyang
	Gao Xiaorong

Cite this article:

Li Xiaoyang,Gao Xiaorong. Comparative Study of SSVEP Characteristics Evoked by Semantic Stimuli and Luminance Flicker[J]. Chinese Journal of Biomedical Engineering, 2026, 45(4): 416-427.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2026.04.003 OR http://cjbme.csbme.org/EN/Y2026/V45/I4/416

[1] Chaudhary Ujwal, Birbaumer Niels, Ramos-Murguialday A. Brain-computer interfaces for communication and rehabilitation[J]. Nature Reviews Neurology, 2016, 12(9): 513-525.
[2] Värbu K, Muhammad N, Muhammad Y. Past, present, and future of EEG-based BCI applications[J]. Sensors, 2022, 22(9): 3331.
[3] Sun Yike, Chen Xiaogang, Liu Bingchuan, et al. Signal acquisition of brain-computer interfaces: A medical-engineering crossover perspective review[J]. Fundamental Research, 2025, 5(1): 3-16.
[4] Chen Xiaogang, Wang Yijun, Nakanishi Masaki, et al. High-speed spelling with a noninvasive brain-computer interface[J]. Proceedings of the National Academy of Sciences, 2015, 112(44): E6058-E6067.
[5] Shi Nanlin, Miao Yining, Huang Changxing, et al. Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface[J]. NeuroImage, 2024, 289: 120548.
[6] Zhu Danhua, Bieger Jordi, Garcia Molina Gary, et al. A survey of stimulation methods used in SSVEP-Based BCIs[J]. Computational Intelligence and Neuroscience, 2010, 2010: 1-12.
[7] Gu Meng, Pei Weihua, Gao Xiaorong, et al. An open dataset for human SSVEPs in the frequency range of 1-60 Hz[J]. Scientific Data, 2024, 11(1): 196.
[8] Si-Mohammed H, Holz C, Wilson A, et al. On the effect of size and contrast of the SSVEP visual stimulations on classification accuracy and user-friendliness in virtual reality[C]//2023 11th International Winter Conference on Brain-Computer Interface (BCI). Gangwon: IEEE, 2023: 1-6.
[9] Chen Jingjing, Maye A, Engel Andreas K, et al. Simultaneous decoding of eccentricity and direction information for a single-flicker SSVEP BCI[J]. Electronics, 2019, 8(12): 1554.
[10] Ming Gege, Zhong Hui, Pei Weihua, et al. A new grid stimulus with subtle flicker perception for user-friendly SSVEP-based BCIs[J]. Journal of Neural Engineering, 2023, 20(2): 026010.
[11] Floriano AF, Diez P, Freire Bastos-Filho T. Evaluating the influence of chromatic and luminance stimuli on SSVEPs from behind-the-ears and occipital areas[J]. Sensors, 2018, 18(2): 615.
[12] Floriano A, Delisle-Rodriguez D, Diez Pablo F, et al. Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field[J]. Computer Methods and Programs in Biomedicine, 2020, 184: 105271.
[13] Zhao Deyu, Dong Guoya, Pei Weihua, et al. Comparisons of stimulus paradigms for SSVEP-based brain-computer interfaces[J]. Journal of Neural Engineering, 2025, 22(2): 026064.
[14] Cheng Yu, Yan Lirong, Shoukat MU, et al. An improved SSVEP-based brain-computer interface with low-contrast visual stimulation and its application in UAV control[J]. Journal of Neurophysiology, 2024, 132(3): 809-821.
[15] Chen Xiaogang, Wang Yijun, Gao Shangkai, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface[J]. Journal of Neural Engineering, 2015, 12(4): 046008.
[16] Gu Meng, Pei Weihua, Gao Xiaorong, et al. Optimizing visual stimulation paradigms for user-friendly SSVEP-based BCIs[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024, 32: 1090-1099.
[17] Zehra Syeda R, Mu Jing, Burkitt Anthony N, et al. Effect of alpha range activity on SSVEP decoding in brain-computer interfaces[C]//2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Sydney: IEEE, 2023: 1-4.
[18] Pastor Maria A, Artieda J, Arbizu J, et al. Human cerebral activation during steady-state visual-evoked responses[J]. The Journal of Neuroscience, 2003, 23(37): 11621-11627.
[19] Wittevrongel B, Khachatryan E, Fahimi Hnazaee M, et al. Decoding steady-state visual evoked potentials from electrocorticography[J]. Frontiers in Neuroinformatics, 2018, 12: 65.
[20] Wittevrongel B, Khachatryan E, Fahimi Hnazaee M, et al. Representation of steady-state visual evoked potentials elicited by luminance flicker in human occipital cortex: an electrocorticography study[J]. NeuroImage, 2018, 175: 315-326.
[21] Han Jin, Xu Minpeng, Xiao Xiaolin, et al. A high-speed hybrid brain-computer interface with more than 200 targets[J]. Journal of Neural Engineering, 2023, 20(1): 016025.
[22] Bi Yanchao, Wang Xiaoying, Caramazza A. Object domain and modality in the ventral visual pathway[J]. Trends in Cognitive Sciences, 2016, 20(4): 282-290.
[23] Carreiras M, Armstrong Blair C, Perea M, et al. The what, when, where, and how of visual word recognition[J]. Trends in Cognitive Sciences, 2014, 18(2): 90-98.
[24] Freud E, Plaut David C, Behrmann M. ‘What’ is happening in the dorsal visual pathway[J]. Trends in Cognitive Sciences, 2016, 20(10): 773-784.
[25] Baker C, Kravitz D. Insights from the evolving model of two cortical visual pathways[J]. Journal of Cognitive Neuroscience, 2024, 36(12): 2568-2579.
[26] Guo Fei, Hong Bo, Gao Xiaorong, et al. A brain-computer interface using motion-onset visual evoked potential[J]. Journal of Neural Engineering, 2008, 5(4): 477-485.
[27] Han Chengcheng, Xu Guanghua, Xie Jun, et al. Highly interactive brain-computer interface based on flicker-free steady-state motion visual evoked potential[J]. Scientific Reports, 2018, 8(1): 5835.
[28] Xie Jun, Xu Guanghua, Wang Jing, et al. Effects of mental load and fatigue on steady-state evoked potential based brain computer interface tasks: a comparison of periodic flickering and motion-reversal based visual attention[J]. PLoS ONE, 2016, 11(9): e0163426.
[29] Zheng Xiaowei, Xu Guanghua, Zhang Yubin, et al. Anti-fatigue performance in SSVEP-Based visual acuity assessment: a comparison of six stimulus paradigms[J]. Frontiers in Human Neuroscience, 2020, 14: 301.
[30] Simon G, Petit L, Bernard C, et al. N170 ERPs could represent a logographic processing strategy in visual word recognition[J]. Behavioral and Brain Functions, 2007, 3(1): 21.
[31] Itier RJ. N170 or N1? Spatiotemporal differences between object and face processing using ERPs[J]. Cerebral Cortex, 2004, 14(2): 132-142.
[32] Eimer M. The face-sensitivity of the N170 component[J]. Frontiers in Human Neuroscience, 2011, 5:119.
[33] Norcia Anthony M, Appelbaum L. Gregory, Ales Justin M, et al. The steady-state visual evoked potential in vision research: a review[J]. Journal of Vision, 2015, 15(6): 4.
[34] Amora KK, Tretow A, Verwimp C, et al. Typical and atypical development of visual expertise for print as indexed by the visual word N1 (N170w): a systematic review[J]. Frontiers in Neuroscience, 2022, 16: 898800.
[35] Mercure E, Kadosh Kathrin C, Johnson Mark H. The N170 shows differential repetition effects for faces, objects, and orthographic stimuli[J]. Frontiers in Human Neuroscience, 2011, 5:6.
[36] Collins E, Robinson Amanda K, Behrmann M. Distinct neural processes for the perception of familiar versus unfamiliar faces along the visual hierarchy revealed by EEG[J]. NeuroImage, 2018, 181: 120-131.
[37] Lochy A, Van Belle G, Rossion B. A robust index of lexical representation in the left occipito-temporal cortex as evidenced by EEG responses to fast periodic visual stimulation[J]. Neuropsychologia, 2015, 66: 18-31.
[38] Zhang Yu, Zhao Qibin, Jing Jin, et al. A novel BCI based on ERP components sensitive to configural processing of human faces[J]. Journal of Neural Engineering, 2012, 9(2): 026018.
[39] Jin Jing, Allison Brendan Z, Kaufmann T, et al. The changing face of P300 BCIs: a comparison of stimulus changes in a P300 BCI involving faces, emotion, and movement[J]. PLoS ONE, 2012, 7(11): e49688.
[40] Fazel-Rezai R, Allison Brendan Z, Guger C, et al. P300 brain computer interface: current challenges and emerging trends[J]. Frontiers in Neuroengineering, 2012, 6: 5.
[41] Kapgate Deepak D. The use of happy faces as visual stimuli improves the performance of the hybrid SSVEP+P300 brain computer interface[J]. Journal of Neuroscience Methods, 2024, 408: 110170.
[42] Yan Wenqiang, Xu Guanghua, Li Min, et al. Steady-state motion visual evoked potential (SSMVEP) based on equal luminance colored enhancement[J]. PLoS ONE, 2017, 12(1): e0169642.
[43] Ma Debbie S, Correll J, Wittenbrink B. The Chicago Face Database: a free stimulus set of faces and norming data[J]. Behavior Research Methods, 2015, 47(4): 1122-1135.
[44] Nakanishi M, Wang Yijun, Chen Xiaogang, et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis[J]. IEEE Transactions on Biomedical Engineering, 2018, 65(1): 104-112.
[45] Dehaene S, Cohen L. The unique role of the visual word form area in reading[J]. Trends in Cognitive Sciences, 2011, 15(6): 254-262.
[46] Alonso-Prieto E, Van Belle G, Liu-Shuang J, et al. The 6 Hz fundamental stimulation frequency rate for individual face discrimination in the right occipito-temporal cortex[J]. Neuropsychologia, 2013, 51(13): 2863-2875.
[47] Herrmann CS. Human EEG responses to 1-100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena[J]. Experimental Brain Research, 2001, 137(3-4): 346-353.
[48] Szwed M, Dehaene S, Kleinschmidt A, et al. Specialization for written words over objects in the visual cortex[J]. NeuroImage, 2011, 56(1): 330-344.
[49] Woodhead ZVJ, Wise RJS, Sereno M, et al. Dissociation of sensitivity to spatial frequency in word and face preferential areas of the fusiform gyrus[J]. Cerebral Cortex, 2011, 21(10): 2307-2312.
[50] Kim CU, Park S, Im CH. Performance enhancement of an SSVEP-based brain-computer interface in augmented reality through adaptive color adjustment of visual stimuli for optimal background contrast[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2025, 33: 514-521.