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Research on Hybrid Evoked Paradigms Based on Weak Spatial Modulation Visual Evoked Potentials |
Zhou Xiaoyu#, Xiao Xiaolin#*, Xu Minpeng#, Ming Dong# |
(Academy of Medical Engineering and Translational Medicine. Tianjin University, Tianjin 300072, China) |
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Abstract The spatial modulation characteristics of visual evoked potentials (VEPs) provide an effective approach for designing user-friendly and practical brain-computer interface systems. However, the spatially modulated VEPs are usually weak and have low signal-to-noise ratios, making it crucial to study how to efficiently elicit and accurately identify weak spatially modulated VEPs. In this study, we employed visual stimuli with a radius smaller than 0.5° of visual angle and designed two mixed spatial modulation induction paradigms including the "transient stimuli serial induction paradigm" and the "steady-state and transient stimuli parallel induction paradigm". Twelve healthy subjects participated in the experiments, and the two paradigms were quantitatively compared by calculating the spatial modulated signal-to-noise ratio (sm-SNR) and offline classification accuracy. Results indicated that the average sm-SNR of the "Low-Frequency Transient" spatial modulation feature under the "transient stimuli serial induction paradigm" could reach 0.0148, significantly higher than that under the "steady-state and transient stimuli parallel induction paradigm" for the "low-frequency transient", "high-frequency steady-state", and their mixed spatial modulation features. The offline classification results were consistent with the feature analysis results, showing that the spatial modulation VEPs recognition accuracy was higher under the "transient stimuli serial induction paradigm", with an average classification accuracy of 85%. This study is expected to provide a reference for the design of high-performance visual brain-computer interfaces based on the spatial modulation characteristics of VEPs.
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Received: 28 February 2024
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Corresponding Authors:
*E-mail: xiaoxiao0@tju.edu.cn
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About author:: #(Member, Chinese Society of Biomedical Engineering) |
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[1] Ramadan RA, Vasilakos AV. Brain computer interface: control signals review [J]. Neurocomputing, 2017, 223: 26-44. [2] Zhang Li, Gan JQ, Wang Haixian. Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method [J]. Cognitive Neurodynamics, 2015, 9(5): 495-508. [3] Munyon CN. Neuroethics of Non-primary brain computer interface: focus on potential military applications [J]. Frontiers in Neuroscience, 2018, 12: 696. [4] Ge Sheng, Ding Mengyuan, Zhang Zheng, et al. Temporal-spatial features of intention understanding based on EEG-fNIRS bimodal measurement [J]. IEEE Access, 2017, 5: 14245-14258. [5] 李鹏海,许敏鹏,万柏坤,等. 视觉诱发电位脑-机接口实验范式研究进展 [J].仪器仪表学报, 2016, 37(10):2340-2351. [6] Han Yuan, He Bin. Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives [J]. IEEE Transactions on Biomedical Engineering, 2014, 61(5): 1425-1435. [7] Li Gang, Lee BL, Chung WY. Smartwatch-based wearable EEG system for driver drowsiness detection [J]. IEEE Sensors Journal, 2015, 15(12): 7169-7180. [8] Gao Zhongke, Wang Xinmin, Yang Yuxuan, et al. EEG-based spatio-temporal convolutional neural network for driver fatigue evaluation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(9): 2755-2763. [9] Gao Shangkai, Wang Yijun, Gao Xiaorong, et al. Visual and auditory brain-computer interfaces [J]. IEEE Transactions on Biomedical Engineering, 2014, 61(5): 1436-1447. [10] 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. [11] 许敏鹏,罗睿心,韩锦,等. 刺激视野面积对稳态视觉诱发电位的影响 [J].信号处理, 2022, 38(10):2064-2073. [12] Chen Xiaogang, Wang Yijun, Nakanishi M, et al. High-speed spelling with a noninvasive brain-computer interface [J]. Proceedings of the National Academy of Sciences, 2015, 112(44): E6058-E6067. [13] Hoffmann U, Vesin JM, Ebrahimi T, et al. An efficient P300-based brain-computer interface for disabled subjects [J]. Journal of Neuroscience Methods, 2008, 167(1): 115-125. [14] 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. [15] Edelman BJ, Meng Jianjun, Suma D, et al. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control [J]. Science Robotics, 2019, 4(31): eaaw6844. [16] Paulun L, Wendt A, Kasabov N. A retinotopic spiking neural network system for accurate recognition of moving objects using neucube and dynamic vision sensors[J]. Frontiers in Computational Neuroscience, 2018, 12: 42. [17] Maye A, Zhang Dan, Engel AK. Utilizing retinotopic mapping for a multi-target SSVEP BCI with a single flicker frequency [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(7): 1026-1036. [18] Zhou Xiaoyu, Xu Minpeng, Xiao Xiaolin, et al. Detection of fixation points using a small visual landmark for brain-computer interfaces [J]. Journal of Neural Engineering, 2021, 18(4): 046098. [19] Chen Jingjing, Li Zhuoran, Hong Bo, et al. A single-stimulus, multitarget bci based on retinotopic mapping of motion-onset VEPS [J]. IEEE Transactions on Biomedical Engineering, 2019, 66(2): 464-470. [20] Xu Minpeng, Xiao Xiaolin, Wang Yijun, et al. A brain-computer interface based on miniature-event-related potentials induced by very small lateral visual stimuli [J]. IEEE Transactions on Biomedical Engineering, 2018, 65(5): 1166-1175. [21] Yue Liang, Xiao Xiaolin, Xu Minpeng, et al. A brain-computer interface based on high-frequency steady-state asymmetric visual evoked potentials [C]//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Montreal:IEEE, 2020: 3090-3093. [22] Rickert J, de Oliveira SC, Vaadia E, et al. Encoding of movement direction in different frequency ranges of motor cortical local field potentials [J]. Journal of Neuroscience, 2005, 25(39): 8815-8824. [23] 陈菁菁. 面向自然交互的空间编码视觉脑-机接口方法研究 [D]. 北京:清华大学, 2022. [24] Brendan Z. A, Jin Jing, Zhang Yu, et al. A four-choice hybrid P300/SSVEP BCI for improved accuracy [J]. Brain-Computer Interfaces, 2014, 1(1): 17-26. [25] Ming Gege, Pei Weihua, Gao Xiaorong, et al. A high-performance SSVEP-based BCI using imperceptible flickers [J]. Journal of Neural Engineering, 2023, 20(1): 016042. [26] Desimone R, Duncan J. Neural mechanisms of selective visual attention [J]. Annual Review of Neuroscience, 1995, 18(1): 193-222. [27] Katsuki F, Constantinidis C. Bottom-up and top-down attention: different processes and overlapping neural systems [J]. The Neuroscientist, 2014, 20(5): 509-521. [28] Bichot NP, Rossi AF, Desimone R. Parallel and serial neural mechanisms for visual search in macaque area V4 [J]. Science, 2005, 308(5721): 529-534. [29] Xu Minpeng, Qi Hongzhi, Wan Bokun, et al. A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature [J]. Journal of Neural Engineering, 2013, 10(2): 026001. [30] Chen Jingjing, Zhang Dan, Engel AK, et al. Application of a single-flicker online SSVEP BCI for spatial navigation [J]. PLoS ONE, 2017, 12(5): e0178385. |
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