Influence of the concrete and abstract graphs on N200 and P300 potentials_Journal of Biomedical Engineering

Authors：

 LI Mengfan ^1,2 , YANG Guang ^1,2

1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China;
2. Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China;

Corresponding author：

LI Mengfan, Email: mfli@hebut.edu.cn

Keywords：

concrete graphs; abstract graphs; visual stimulus; N200; P300

DOI：

10.7507/1001-5515.201903042

Video：

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Increasing the amplitude of event-related potential is one of the key methods to improve the accuracy of the potential-based brain-computer interface, e.g., P300-based brain-computer interface. The brain-computer interface systems often use symbols or controlled objects as vision stimuli, but what visual stimuli can induce more obvious event-related potential is still unknown. This paper designed three kinds of visual stimuli, i.e., a square, an arrow, and a robot attached with an arrow, to analyze the influence of concreteness degree of the graph on the N200 and P300 potentials, and applied a support vector machine to compare the performance of the brain-computer interface under different stimuli. The results showed that, compared with the square, the robot attached with arrow and the arrow both induced larger N200 potential (P = 1.6 × 10⁻³, P = 4.2 × 10⁻²) and longer P300 potential (P = 2.2 × 10⁻³, P = 1.9 × 10⁻²) in the frontal area, but the amplitude under the arrow condition is smaller than the one under the robot attached with arrow condition. The robot attached with arrow increased the N200 potential amplitude of the square and arrow from 3.12 μV and 5.19 μV to 7.21 μV (P = 1.6 × 10⁻³, P = 8.9 × 10⁻²), and improved the accuracy rate from 59.95%, 61.67% to 74.45% (P = 2.1 × 10⁻², P = 1.6 × 10⁻²), and the information transfer rate from 35.00 bits/min, 35.98 bits/min to 56.71 bits/min (P = 2.6 × 10⁻², P = 1.6 × 10⁻²). This study shows that the concreteness of graphics could affect the N200 potential and the P300 potential. The abstract symbol could represent the meaning and evoke potentials, but the information contained in the concrete robot attached with an arrow is more correlated with the human experience, which is helpful to improve the amplitude. The results may provide new sight in modifying the stimulus interface of the brain-computer interface.

Citation： LI Mengfan, YANG Guang. Influence of the concrete and abstract graphs on N200 and P300 potentials. Journal of Biomedical Engineering, 2020, 37(3): 427-433, 441. doi: 10.7507/1001-5515.201903042 Copy

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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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