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具体与抽象图形对 N200 和 P300 电位的影响研究

李梦凡 杨光

李梦凡, 杨光. 具体与抽象图形对 N200 和 P300 电位的影响研究[J]. 机械工程学报, 2020, 37(3): 427-433, 441. doi: 10.7507/1001-5515.201903042
引用本文: 李梦凡, 杨光. 具体与抽象图形对 N200 和 P300 电位的影响研究[J]. 机械工程学报, 2020, 37(3): 427-433, 441. doi: 10.7507/1001-5515.201903042
Mengfan LI, Guang YANG. Influence of the concrete and abstract graphs on N200 and P300 potentials[J]. JOURNAL OF MECHANICAL ENGINEERING, 2020, 37(3): 427-433, 441. doi: 10.7507/1001-5515.201903042
Citation: Mengfan LI, Guang YANG. Influence of the concrete and abstract graphs on N200 and P300 potentials[J]. JOURNAL OF MECHANICAL ENGINEERING, 2020, 37(3): 427-433, 441. doi: 10.7507/1001-5515.201903042

具体与抽象图形对 N200 和 P300 电位的影响研究

doi: 10.7507/1001-5515.201903042
详细信息
    通讯作者:

    李梦凡,Email:mfli@hebut.edu.cn

Influence of the concrete and abstract graphs on N200 and P300 potentials

More Information
  • 摘要: 增大事件相关电位的幅值是提高 P300 等经典事件相关电位范式下的脑-机接口系统辨识意图准确率的重要方法之一,此类脑-机接口系统常以符号或者被控对象作为视觉刺激,但是何种视觉刺激能够获得更明显的事件相关电位仍然未知。本文设计方形、箭头和机器人附加箭头这三种视觉刺激,分析图片的具体程度对 N200 和 P300 电位的影响,并采用支持向量机辨识该诱发电位来对比不同刺激下的脑-机接口性能。结果显示,与方形相比,机器人附加箭头和箭头都在额叶诱发出幅值更大的 N200 电位(P = 1.6 × 10−3P = 4.2 × 10−2)和潜伏期更长的 P300 电位(P = 2.2 × 10−3P = 1.9 × 10−2)。机器人附加箭头将方形和箭头的 N200 电位幅值数值分别从 3.12 μV 和 5.19 μV 提升至 7.21 μV(P = 1.6 × 10−3P = 8.9 × 10−2),单次准确率从 59.95% 和 61.67% 提升至 74.45%(P = 2.1 × 10−2P = 1.6 × 10−2),单次信息传输率从 35.00 bits/min 和 35.98 bits/min 提升至 56.71 bits/min(P = 2.6 × 10−2P = 1.6 × 10−2)。本研究表明图形的具体性会影响 N200 电位和 P300 电位,箭头虽然能够表征图片的含义并诱发电位,但是机器人附加箭头所包含的信息与人的经验相关度更大,有助于获得更高的电位幅值。该研究可为脑-机接口的视觉刺激界面优化设计提供新的思路。

     

  • 图  视觉诱发界面

    Figure  1.  Visual interface

    图  三种刺激下的波形图

    Figure  2.  The ERP under three conditions

    图  三种刺激下的脑地形图

    Figure  3.  The topographies of ERPs under three conditions

    图  三种条件下的准确率与信息传输率

    Figure  4.  Accuracies and ITRs under three conditions

    表  1  三种刺激条件下 ERP 的幅值和潜伏期

    Table  1.   The amplitude and latency of ERP under three stimulus conditions

    刺激条件 Fz Oz
    N200 P300 N200 P300
    幅值/μV 潜伏期/ms 幅值/μV 潜伏期/ms 幅值/μV 潜伏期/ms 幅值/μV 潜伏期/ms
    方形 −3.21 281 2.45 402 −1.95 154 2.44 271
    箭头 −5.19 266 4.78 428 −2.66 181 3.45 283
    机器人附加箭头 −7.21 259 5.12 443 −2.76 146 3.69 241
    下载: 导出CSV

    表  2  三种刺激条件下准确率和信息传输率

    Table  2.   Accuracies and information transfer rates under three stimulus conditions

    刺激条件 准确率 信息传输率
    一次 三次 一次/(bit·min−1 三次/(bit·min−1
    方形 59.95% 85.15% 35.00 26.60
    箭头 61.67% 90.51% 35.98 30.53
    机器人附加箭头 74.45% 96.85% 56.71 35.76
    下载: 导出CSV
  • [1] 王行愚, 金晶, 张宇, 等. 脑控: 基于脑-机接口的人机融合控制. 自动化学报, 2013, 39(3): 208-221.
    [2] Li Wenyu, Feng Duan, Sheng Shili, et al. A human-vehicle collaborative simulated driving system based on hybrid brain-computer interfaces and computer vision. IEEE Trans Cogn Dev Sys, 2018, 10(3): 810-822. doi: 10.1109/TCDS.2017.2766258
    [3] Allison B Z, Wolpaw E W, Wolpaw J R. Brain-computer interface systems: progress and prospects. Expert Rev Med Devices, 2007, 4(4): 463-474. doi: 10.1586/17434440.4.4.463
    [4] Qu Jun, Wang Fei, Xia Zhenping, et al. A novel three-dimensional P300 speller based on stereo visual stimuli. IEEE Trans Hum-Mach Syst, 2018, 48(4): 392-399. doi: 10.1109/THMS.2018.2799525
    [5] 王欣, 靳静娜, 李松, 等. 厌恶与悲伤情境图片诱发负性情绪的脑电机制差异探索. 生物医学工程学杂志, 2015, 32(6): 1165-1172. doi: 10.7507/1001-5515.20150207
    [6] Omedes J, Schwarz A, Müller-Putz G R, et al. Factors that affect error potentials during a grasping task: toward a hybrid natural movement decoding BCI. J Neural Eng, 2018, 15(4): 046023. doi: 10.1088/1741-2552/aac1a1
    [7] 魏景汉, 罗跃嘉. 事件相关电位原理与技术. 北京: 科学出版社, 2010.
    [8] Sutton S, Braren M, Zubin J. Evoked potential correlates of stimulus uncertainty. Los Angeles: American Psychological Association, 1964: 1-8.
    [9] Patel S H, Azzam P N. Characterization of N200 and P300: selected studies of the event-related potential. Int J Med Sci, 2005, 2(4): 147-154.
    [10] 范晓丽, 赵朝义, 罗虹, 等. 基于 2-back 任务下 ERP 特征的脑力疲劳客观评价研究. 生物医学工程学杂志, 2018, 35(6): 837-844.
    [11] Palankar M, De Laurentis K J, Alqasemi R, et al. Control of a 9-DoF wheelchair-mounted robotic arm system using a P300 brain computer interface: initial experiments// IEEE International Conference on Robotics and Biomimetics. Guilin: IEEE, 2009: 348-353.
    [12] Iturrate I, Antelis J M, Kubler A, et al. A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation. IEEE Trans Robot, 2009, 25(3): 614-627. doi: 10.1109/TRO.2009.2020347
    [13] Li Mengfan, Li Wei, Niu Linwei, et al. An event-related potential-based adaptive model for telepresence control of humanoid robot motion in an environment with cluttered obstacles. IEEE Trans Ind Electron, 2017, 64(2): 1696-1705. doi: 10.1109/TIE.2016.2538740
    [14] Jin Jing, Allison B Z, Wang Xingyu, et al. A combined brain-computer interface based on P300 potentials and motion-onset visual evoked potentials. J Neurosci Methods, 2012, 205(2): 265-276. doi: 10.1016/j.jneumeth.2012.01.004
    [15] Gonsalvez C J, Barry R J, Rushby J A, et al. Target-to-target interval, intensity, and P300 from an auditory single-stimulus task. Psychophysiology, 2007, 44(2): 245-250. doi: 10.1111/j.1469-8986.2007.00495.x
    [16] Allison B Z, Pineda J A. Effects of SOA and flash pattern manipulations on ERPs, performance, and preference: implications for a BCI system. Int J Psychophysiol, 2006, 59(2): 127-140. doi: 10.1016/j.ijpsycho.2005.02.007
    [17] 马忠伟, 高上凯. 基于 P300 的脑-机接口: 视觉刺激强度对性能的影响. 清华大学学报:自然科学版, 2008, 48(3): 415-418.
    [18] Allison B Z, Pineda J A. ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system. IEEE Trans Neural Syst Rehabil Eng, 2003, 11(2): 110-113. doi: 10.1109/TNSRE.2003.814448
    [19] Zhang Dan, Song Huaying, Xu Rui, et al. Toward a minimally invasive brain-computer interface using a single subdural channel: a visual speller study. Neuroimage, 2013, 71(5): 30-41.
    [20] Holz E M, Botrel L, Kaufmann T, et al. Long-term Independent brain-computer interface home use improves quality of life of a patient in the locked-in state: a case study. Arch Phys Med Rehabil, 2015, 96(3 Suppl): S16-S26.
    [21] Jin Jing, Sellers E W, Zhou Sijie, et al. A P300 brain-computer interface based on a modification of the mismatch negativity paradigm. Int J Neural Syst, 2015, 25(3): 595-599.
    [22] Kosonogov V, Martinez-Selva J, Carrillo-Verdejo, et al. Effects of social and affective content on exogenous attention as revealed by event-related potentials. Cogn Emot, 2019, 33(4): 683-695. doi: 10.1080/02699931.2018.1486287
    [23] 李玥. 基于图像信息的简单图形与复杂视觉场景认知过程研究. 昆明: 云南大学, 2013.
    [24] Bradley M M, Hamby S, Löw A, et al. Brain potentials in perception: picture complexity and emotional arousal. Psychophysiology, 2007, 44(3): 364-373. doi: 10.1111/j.1469-8986.2007.00520.x
    [25] Li Mengfan, Li Wei, Zhou Huihui. Increasing N200 potentials via visual stimulus depicting humanoid robot behavior. Int J Neural Syst, 2016, 26(1): 1-16.
    [26] Zhang Xukun, Zhang Zhenhao, Zhang Zhijun, et al. The role of the motion cue in the dynamic gaze-cueing effect: A study of the lateralized ERPs. Neuropsychologia, 2019, 124: 151-160. doi: 10.1016/j.neuropsychologia.2018.12.016
    [27] Hirai M, Fukushima H, Hiraki K. An event-related potentials study of biological motion perception in humans. Neurosci Lett, 2003, 344(1): 41-44. doi: 10.1016/S0304-3940(03)00413-0
    [28] Zarka D, Cevallos C, Petieau M, et al. Neural rhythmic symphony of human walking observation: Upside-down and Uncoordinated condition on cortical theta, alpha, beta and gamma oscillations. Front Syst Neurosci, 2014, 8: 1-19.
    [29] Hietanen J K, Leppänen J M, Nummenmaa L, et al. Visuospatial attention shifts by gaze and arrow cues: an ERP study. Brain Res, 2008, 1215(2): 123-136.
    [30] Beaucousin V, Cassotti M, Simon G, et al. ERP evidence of a meaningfulness impact on visual global/local processing: When meaning captures attention. Neuropsychologia, 2011, 49(5): 1258-1266. doi: 10.1016/j.neuropsychologia.2011.01.039
    [31] Gunter T C, Bach P. Communicating hands: ERPs elicited by meaningful symbolic hand postures. Neurosci Lett, 2004, 372(1/2): 52-56.
    [32] Potter MC. Short-term conceptual memory for pictures. J Exp Psychol, 1976, 2(5): 509-522.
    [33] Proverbio A M, Riva F. RP and N400 ERP components reflect semantic violations in visual processing of human actions. Neurosci Lett, 2009, 459(3): 142-146. doi: 10.1016/j.neulet.2009.05.012
    [34] Yin Erwei, Zeyl T, Saab R, et al. An auditory-tactile visual saccade-independent P300 brain-computer interface. Int J Neural Syst, 2016, 26(1): 1650001. doi: 10.1142/S0129065716500015
    [35] Li J, Ji H, Cao L, et al. Evaluation and application of a hybrid brain computer interface for real wheelchair parallel control with multi-degree of freedom. Int J Neural Syst, 2014, 24(4): 1450014. doi: 10.1142/S0129065714500142
    [36] Erdogan SB, Ozsarfati E, Dilek B, et al. Classification of motor imagery and execution signals with population-level feature sets: implications for probe design in fNIRS based BCI. J Neural Eng, 2019, 16(2): 026029. doi: 10.1088/1741-2552/aafdca
    [37] Rakotomamonjy A, Guigue V. BCI competition III: dataset II-ensemble of SVMs for BCI P300 speller. IEEE Trans Biomed Eng, 2008, 55(3): 1147-1154. doi: 10.1109/TBME.2008.915728
    [38] Sereshkeh A, Trott R, Bricout A, et al. Online EEG classification of covert speech for brain-computer interfacing. Int J Neural Syst, 2017, 27(8): 1750033. doi: 10.1142/S0129065717500332
    [39] Wittevrongel B, Van Wolputte E, Van Hulle M M. Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding. Sci Rep, 2017, 7(1): 15037. doi: 10.1038/s41598-017-15373-x
    [40] Li Wei, Li Mengfan, Zhou Huihui, et al. A dual stimuli approach combined with convolutional neural network to improve information transfer rate of event-related potential-based brain-computer interface. Int J Neural Syst, 2018, 28(10): 1850034. doi: 10.1142/S012906571850034X
    [41] 王金甲, 杨成杰, 胡备. P300 脑机接口控制智能小车系统的设计与实现. 生物医学工程学杂志, 2013, 30(2): 223-228.
    [42] Bechtold L, Bellebaum C, Egan S A, et al. The role of experience for abstract concepts: Expertise modulates the electrophysiological correlates of mathematical word processing. Brain Lang, 2019, 188: 1-10. doi: 10.1016/j.bandl.2018.10.002
    [43] Martin-Loeches M, Sommer W, Hinojosa J A. ERP components reflecting stimulus identification: contrasting the recognition potential and the early repetition effect (N250r). Int J Psychophysiol, 2005, 55(1): 113-125. doi: 10.1016/j.ijpsycho.2004.06.007
    [44] Merriënboer J J G V, Sweller J. Cognitive load theory in health professional education: design principles and strategies. Med Educ, 2010, 44(1): 85-93. doi: 10.1111/j.1365-2923.2009.03498.x
    [45] Hollender N, Hofmann C, Deneke M, et al. Integrating cognitive load theory and concepts of human-computer interaction. Comput Hum Behav, 2010, 26(6): 1278-1288. doi: 10.1016/j.chb.2010.05.031
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  • 收稿日期:  2019-03-31
  • 修回日期:  2020-04-11
  • 发布日期:  2020-03-17

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