IEEE Transactions on Neural Systems and Rehabilitation Engineering (Jan 2024)

Physiologic Network-Based Brain-Heart Interaction Quantification During Visual Emotional Elicitation

  • Zhipeng Cai,
  • Hongxiang Gao,
  • Min Wu,
  • Jianqing Li,
  • Chengyu Liu

DOI
https://doi.org/10.1109/TNSRE.2024.3424543
Journal volume & issue
Vol. 32
pp. 2482 – 2491

Abstract

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In recent years, there has been a surge in interest regarding the intricate physiological interplay between the brain and the heart, particularly during emotional processing. This has led to the development of various signal processing techniques aimed at investigating Brain-Heart Interactions (BHI), reflecting a growing appreciation for their bidirectional communication and influence on each other. Our study contributes to this burgeoning field by adopting a network physiology approach, employing time-delay stability as a quantifiable metric to discern and measure the coupling strength between the brain and the heart, specifically during visual emotional elicitation. We extract and transform features from EEG and ECG signals into a 1 Hz format, facilitating the calculation of BHI coupling strength through stability analysis on their maximal cross-correlation. Notably, our investigation sheds light on the critical role played by low-frequency components in EEG, particularly in the $\delta $ , $\theta $ , and $\alpha $ bands, as essential mediators of information transmission during the complex processing of emotion-related stimuli by the brain. Furthermore, our analysis highlights the pivotal involvement of frontal pole regions, emphasizing the significance of $\delta $ - $\theta $ coupling in mediating emotional responses. Additionally, we observe significant arousal-dependent changes in the $\theta $ frequency band across different emotional states, particularly evident in the prefrontal cortex. By offering novel insights into the synchronized dynamics of cortical and heartbeat activities during emotional elicitation, our research enriches the expanding knowledge base in the field of neurophysiology and emotion research.

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