基于生理网络脑心交互的视觉诱发情绪效价评估

doi:10.3969/j.issn.0258-8021.2025.01.002

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Abstract In the past decade, complex physiological interactions between brain and heart during emotional processing have become a research hotspot. This study employed network physiological methods to analyze the time-delay stability(TDS) quantization indices between electroencephalogram (EEG) and electrocardiogram (ECG) signals recorded during visually induced emotional stimuli in 414 data entries from the Dreamer database, exploring the brain-heart interaction under visual emotional stimulation. The research revealed asymmetric connectivity between the brain hemispheres during emotional processing, particularly highlighting the dominant role of the right hemisphere in these interactions. EEG analysis emphasized the critical role of low-frequency bands (δ, θ, α) in the transmission of emotional information, with δ-θ coupling in the frontal region being particularly crucial for emotional regulation. In high-valence emotional states, the %TDS value of δ-θ coupling (0.78±0.05) was significantly higher than in low-valence states (0.65±0.04). Additionally, the average link strength of brain-heart interactions in low-valence states reached its peak (0.68±0.06), while in high-valence states, it decreased to the lowest (0.59±0.03). These findings not only enhance our understanding of the synchronization mechanisms between the cortical brain and heart in emotional processing but also enrich the knowledge base in neurophysiology and emotional science.

Key words： brain-heart interaction electroencephalogram oscillations emotion elicitation network physiology time delay stability

Received: 16 July 2024

PACS:

R318

Corresponding Authors: *E-mail: chengyu@seu.edu.cn

About author:: ^#(Member, Chinese Society of Biomedical Engineering)

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	Cai Zhipeng
	Gao Hongxiang
	Li Jianqing
	Liu Chengyu

Cite this article:

Cai Zhipeng,Gao Hongxiang,Li Jianqing, et al. Visually Induced Emotional Valence Evaluation using Physiologic Network-Based Brain-Heart Interaction[J]. Chinese Journal of Biomedical Engineering, 2025, 44(1): 11-20.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2025.01.002 OR http://cjbme.csbme.org/EN/Y2025/V44/I1/11

[1] 刘爽,仝晶晶,郭冬月,等. 情绪影响工作记忆的研究现状与发展动向[J]. 中国生物医学工程学报, 2017, 36 (6): 724-732.
[2] 周如双,赵慧琳,林玮玥,等. 基于深浅特征融合的深度卷积残差网络的脑电情绪识别模型[J]. 中国生物医学工程学报, 2021, 40 (6): 641-652.
[3] 袁丹凤,杨祥云,李占江. 脑电信号在焦虑障碍疗效预测与治疗机制中的研究进展[J]. 四川精神卫生, 2024, 37 (3): 270-276.
[4] Mishra S, Srinivasan N, Tiwary U S. Cardiac-brain dynamics depend on context familiarity and their interaction predicts experience of emotional arousal[J]. Brain Sciences, 2022, 12 (6): 702.
[5] Tandle A, Jog N, Dharmadhikari A, et al. Estimation of valence of emotion from musically stimulated EEG using frontal theta asymmetry[C]// 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). Changsha: IEEE, 2016: 63-68.
[6] Yang Kai, Tong Li, Shu Jun, et al. High gamma band EEG closely related to emotion: evidence from functional network[J]. Frontiers in Human Neuroscience, 2020, 14: 89.
[7] 李永康,方安成,陈娅南,等. 基于心电信号图像特征及卷积神经网络的情绪识别研究[J]. 生物医学工程研究, 2024, 43 (1): 33-39.
[8] Valenza G, Greco A, Gentili C, et al. Combining electroencephalographic activity and instantaneous heart rate for assessing brain-heart dynamics during visual emotional elicitation in healthy subjects[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2016, 374 (2067): 20150176.
[9] Mather M, Thayer JF. How heart rate variability affects emotion regulation brain networks[J]. Current Opinion in Behavioral Sciences, 2018, 19: 98-104.
[10] Valderas MT, Bolea J, Laguna P, et al. Mutual information between heart rate variability and respiration for emotion characterization[J]. Physiological Measurement, 2019, 40 (8): 84001.
[11] 尚彦国,王轩,佟小光,等. 泛血管医学时代“脑心同治”手术与展望[J]. 中国现代神经疾病杂志, 2024, 24 (1): 8-16.
[12] Sabor N, Mohammed H, Li Zhe, et al. BHI-Net: Brain-heart interaction-based deep architectures for epileptic seizures and firing location detection[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 1576-1588.
[13] Sammler D, Grigutsch M, Fritz T, et al. Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music[J]. Psychophysiology, 2007, 44 (2): 293-304.
[14] Duggento A, Bianciardi M, Passamonti L, et al. Globally conditioned Granger causality in brain-brain and brain-heart interactions: a combined heart rate variability/ultra-high-field (7 T) functional magnetic resonance imaging study[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2016, 374 (2067): 20150185.
[15] Piper D, Ungureanu M, Strungaru R, et al. Time-variant connectivity analysis between epileptic EEG signals and between EEG-envelopes and HRV[C]//2013 E-Health and Bioengineering Conference (EHB). Romania: IEEE, 2013: 1-4.
[16] Lin Aijing, Liu KK, Bartsch RP, et al. Delay-correlation landscape reveals characteristic time delays of brain rhythms and heart interactions[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2016, 374 (2067): 20150182.
[17] Katsigiannis S, Ramzan N. DREAMER: a database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices[J]. IEEE Journal of Biomedical and Health Informatics, 2017, 22 (1): 98-107.
[18] Bashan A, Bartsch RP, Kantelhardt JW, et al. Network physiology reveals relations between network topology and physiological function[J]. Nature Communications, 2012, 3 (1): 702.
[19] Lindquist KA, Satpute AB, Wager TD, et al. The brain basis of positive and negative affect: evidence from a meta-analysis of the human neuroimaging literature[J]. Cerebral Cortex, 2016, 26 (5): 1910-1922.
[20] Schubring D, Schupp HT. Emotion and brain oscillations: high arousal is associated with decreases in alpha- and lower beta-band power[J]. Cerebral Cortex, 2021, 31 (3): 1597-1608.
[21] Hartikainen KM. Emotion-attention interaction in the right hemisphere[J]. Brain Sciences, 2021, 11 (8): 1006.
[22] Zhao Guozhen, Zhang Yulin, Ge Yan. Frontal EEG asymmetry and middle line power difference in discrete emotions[J]. Frontiers in Behavioral Neuroscience,2018,12:225.
[23] Adams NE, Teige C, Mollo G, et al. Theta/delta coupling across cortical laminae contributes to semantic cognition[J]. Journal of Neurophysiology, 2019, 121 (4): 1150-1161.
[24] Stankovski T, Ticcinelli V, Mcclintock PV, et al. Neural cross-frequency coupling functions[J]. Frontiers in Systems Neuroscience, 2017, 11: 33.
[25] Schutter DJ, Knyazev GG. Cross-frequency coupling of brain oscillations in studying motivation and emotion[J]. Motivation and Emotion, 2012, 36: 46-54.
[26] Candia-Rivera D, Catrambone V, Thayer JF, et al. Cardiac sympathetic-vagal activity initiates a functional brain-body response to emotional arousal[J]. Proceedings of the National Academy of Sciences, 2022, 119 (21): e2119599119.
[27] Klados MA, Frantzidis C, Vivas AB, et al. A framework combining delta event-related oscillations (EROs) and synchronisation effects (ERD/ERS) to study emotional processing[J]. Computational Intelligence and Neuroscience, 2009, 2009 (1): 549419.
[28] Knyazev GG, Slobodskoj-Plusnin JY, Bocharov AV. Event-related delta and theta synchronization during explicit and implicit emotion processing[J]. Neuroscience, 2009, 164 (4): 1588-1600.
[29] Aftanas LI, Golocheikine SA. Human anterior and frontal midline theta and lower alpha reflect emotionally positive state and internalized attention: high-resolution EEG investigation of meditation[J]. Neuroscience Letters, 2001, 310 (1): 57-60.