Neurovascular coupling analysis of working memory based on electroencephalography and functional near-infrared spectroscopy_Journal of Biomedical Engineering

Authors：

LIU Wenzheng ^1,2 , ZHANG Hao ⁴ , YANG Liu ^1,2 ,  GU Yue ^1,2,3

1. School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, P. R. China;
2. Key Laboratory of Computer Vision and System (Ministry of Education), Tianjin University of Technology, Tianjin 300384, P. R. China;
3. Engineering Research Center of Learning-Based Intelligent system (Ministry of Education), Tianjin University of Technology, Tianjin 300384, P. R. China;
4. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, P. R. China;

Corresponding author：

GU Yue, Email: guyue@email.tjut.edu.cn

Keywords：

Electroencephalography; Functional near-infrared spectroscopy; Working memory; Neurovascular coupling; Canonical correlation analysis

DOI：

10.7507/1001-5515.202108048

Video：

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Working memory is an important foundation for advanced cognitive function. The paper combines the spatiotemporal advantages of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to explore the neurovascular coupling mechanism of working memory. In the data analysis, the convolution matrix of time series of different trials in EEG data and hemodynamic response function (HRF) and the blood oxygen change matrix of fNIRS are extracted as the coupling characteristics. Then, canonical correlation analysis (CCA) is used to calculate the cross correlation between the two modal features. The results show that CCA algorithm can extract the similar change trend of related components between trials, and fNIRS activation of frontal pole region and dorsolateral prefrontal lobe are correlated with the delta, theta, and alpha rhythms of EEG data. This study reveals the mechanism of neurovascular coupling of working memory, and provides a new method for fusion of EEG data and fNIRS data.

Citation： LIU Wenzheng, ZHANG Hao, YANG Liu, GU Yue. Neurovascular coupling analysis of working memory based on electroencephalography and functional near-infrared spectroscopy. Journal of Biomedical Engineering, 2022, 39(2): 228-236, 247. doi: 10.7507/1001-5515.202108048 Copy

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21.	Blankertz B, Acqualagna L, Dähne S, et al. The Berlin brain-computer interface: progress beyond communication and control. Front Neurosci, 2016, 10: 530.
22.	Si X P, Xiang S X, Zhang L D, et al. Acupuncture with deqi modulates the hemodynamic response and functional connectivity of the prefrontal-motor cortical network. Front Neurosci, 2021, 15: 803.
23.	Correa N M, Eichele T, Adali T, et al. Multi-set canonical correlation analysis for the fusion of concurrent single trial ERP and functional MRI. Neuroimage, 2010, 50(4): 1438-1445.
24.	Liu Y, Lan Z, Cui J, et al. Inter-subject transfer learning for EEG-based mental fatigue recognition. Advanced Engineering Informatics, 2020, 46(20): 101157.
25.	Palomäki J, Kivikangas M, Alafuzoff A, et al. Brain oscillatory 4-35Hz EEG responses during an n-back task with complex visual stimuli. Neurosci Lett, 2012, 516(1): 141-145.
26.	黄海云, 蔡跃新, 冯学技, 等. 基于脑电信号的耳鸣患者静息态频谱图及注意力研究. 生物医学工程学杂志, 2021, 38(3): 492-497.
27.	Shin J, von Lühmann A, Kim D W, et al. Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset. Sci Data, 2018, 5(1): 180003.
28.	Li T, Luo Q, Gong H. Gender-specific hemodynamics in prefrontal cortex during a verbal working memory task by near-infrared spectroscopy. Behav Brain Res, 2010, 209(1): 148-153.

1. Baddeley A. Working memory: looking back and looking forward. Nat Rev Neurosci, 2003, 4(10): 829-839.
2. 陆晟, 罗志增, 史红斐, 等. 经颅直流电刺激干预工作记忆的脑功能网络研究. 中国生物医学工程学报, 2021, 40(2): 145-153.
3. 蔡梓良, 郭苗苗, 杨新生, 等. 基于最大分类器差异域对抗方法的跨被试脑电情绪识别研究. 生物医学工程学杂志, 2021, 38(3): 455-462.
4. 李继鹏, 李颖, 张东颖, 等. 基于脑电信号溯源分析的音乐类型对学习记忆影响的研究. 中国生物医学工程学报, 2019, 38(6): 679-686.
5. Martínez-Briones B J, Fernández-Harmony T, Garófalo G N, et al. Working memory in children with learning disorders: an EEG power spectrum analysis. Brain Sci, 2020, 10(11): 817.
6. 刘宝根, 周兢, 李菲菲. 脑功能成像的新方法-功能性近红外光谱技术(fNIRS). 心理科学, 2011, 34(4): 943-949.
7. Stute K, Hudl N, Stojan R, et al. Shedding light on the effects of moderate acute exercise on working memory performance in healthy older adults: an fNIRS study. Brain Sci, 2020, 10(11): 813.
8. Shirzadi S, Einalou Z, Dadgostar M. Investigation of functional connectivity during working memory task and hemispheric lateralization in left-and Right-Handers measured by fNIRS. Optik-International Journal for Light and Electron Optics, 2020, 221: 165347.
9. He B, Liu Z. Multimodal functional neuroimaging: integrating functional MRI and EEG/MEG. IEEE Rev Biomed Eng, 2008, 1(28): 23-40.
10. Wu C W, Tsai P J, Chen S C, et al. Indication of dynamic neurovascular coupling from inconsistency between EEG and fMRI indices across sleep–wake states. Sleep Biol Rhythms, 2019, 17(4): 423-431.
11. Eichele T, Specht K, Moosmann M, et al. Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI. Proc Natl Acad Sci U S A, 2005, 102(49): 17798-17803.
12. Fazli S, Mehnert J, Steinbrink J, et al. Enhanced performance by a hybrid NIRS-EEG brain computer interface. Neuroimage, 2012, 59(1): 519-529.
13. Jorge J, Zwaag W, Figueiredo P. EEG–fMRI integration for the study of human brain function. NeuroImage, 2014, 102: 24-34.
14. 邹凌, 严永, 杨彪, 等. 基于同步EEG-fMRI采集的情绪认知重评数据特征融合分析研究. 自动化学报, 2016, 42(5): 134-144.
15. 杨桂芬, 张云亭, 张权, 等. 正常人数字n-back工作记忆神经基础的fMRI研究. 临床放射学杂志, 2008, 27(1): 6-9.
16. Keles H O, Barbour R L, Omurtag A. Hemodynamic correlates of spontaneous neural activity measured by human whole-head resting state EEG+fNIRS. Neuroimage, 2016, 138: 76-87.
17. 张莉, 何传红, 何为. 典型相关分析去除脑电信号中眼电伪迹的研究. 计算机工程与应用, 2009, 45(31): 218-220.
18. Hwang H J, Lim J H, Kim D W, et al. Evaluation of various mental task combinations for near-infrared spectroscopy-based brain-computer interfaces. J Biomed Opt, 2014, 19(7): 77005.
19. Quian Q R, Garcia H. Single-trial event-related potentials with wavelet denoising. Clin Neurophysiol, 2003, 114(2): 376-390.
20. Kocsis L, Herman P, Eke A. The modified Beer-Lambert law revisited. Phys Med Biol, 2006, 51(5): N91-N98.
21. Blankertz B, Acqualagna L, Dähne S, et al. The Berlin brain-computer interface: progress beyond communication and control. Front Neurosci, 2016, 10: 530.
22. Si X P, Xiang S X, Zhang L D, et al. Acupuncture with deqi modulates the hemodynamic response and functional connectivity of the prefrontal-motor cortical network. Front Neurosci, 2021, 15: 803.
23. Correa N M, Eichele T, Adali T, et al. Multi-set canonical correlation analysis for the fusion of concurrent single trial ERP and functional MRI. Neuroimage, 2010, 50(4): 1438-1445.
24. Liu Y, Lan Z, Cui J, et al. Inter-subject transfer learning for EEG-based mental fatigue recognition. Advanced Engineering Informatics, 2020, 46(20): 101157.
25. Palomäki J, Kivikangas M, Alafuzoff A, et al. Brain oscillatory 4-35Hz EEG responses during an n-back task with complex visual stimuli. Neurosci Lett, 2012, 516(1): 141-145.
26. 黄海云, 蔡跃新, 冯学技, 等. 基于脑电信号的耳鸣患者静息态频谱图及注意力研究. 生物医学工程学杂志, 2021, 38(3): 492-497.
27. Shin J, von Lühmann A, Kim D W, et al. Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset. Sci Data, 2018, 5(1): 180003.
28. Li T, Luo Q, Gong H. Gender-specific hemodynamics in prefrontal cortex during a verbal working memory task by near-infrared spectroscopy. Behav Brain Res, 2010, 209(1): 148-153.

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