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The evaluation of the correlation between the activations of various organs has great importance. This work investigated the synchronization of the brain and heart responses to different auditory stimuli using complexity-based analysis. We selected three pieces of music based on the difference in the complexity of embedded noise (including white noise, brown noise, and pink noise) in them. We played these pieces of music for 11 subjects (7 M and 4 F) and computed the fractal dimension and sample entropy of EEG signals and R–R time series [as heart rate variability (HRV)]. We found strong correlations ($r=0.9999$ in the case of fractal dimension and $r=0.7862$ in the case of sample entropy) among the complexities of EEG signals and HRV. This finding demonstrates the synchronization of the brain and heart responses and auditory stimuli from the complexity perspective.

Decoding of the coupling among the brain and heart activations is an important research area in network physiology. We studied the coupling of brain and heart activations for 48 subjects who performed the NASA Revised Multi-Attribute Task Battery II under three different activity level conditions. During the experiment, the physical activity of subjects was manipulated by changing the speed of a stationary bike (including no movement, 50rpm, and 70rpm) or a treadmill (including no movement, 3km/h, and 5km/h), while their physiological signals were recorded. We analyzed the complex structure of electroencephalogram (EEG) and R-R signals using fractal theory and sample entropy. The results demonstrated that the alterations of the complex structures of EEG and R-R signals are strongly correlated, which indicates the coupling between brain and heart activations. This method of analysis can be applied to evaluate the coupling between different organs.

Since the brain controls heart activations, there should be a correlation between their activities in different conditions. This study investigates the correlation between heart and brain responses to olfactory stimulation. We employed fractal theory and sample entropy to evaluate the complexity of EEG signals and Heart Rate Variability (HRV) in the form of R–R time series. We applied four different pleasant odors with different molecular complexities to 13 participants and analyzed their EEG and ECG signals. The results demonstrated that the complexities of HRV and EEG signals are strongly correlated; a bigger alteration in the complexity of olfactory stimuli is mapped to a bigger alteration in the complexity of HRV and EEG signals. This investigation can be similarly done to examine the correlation between various organs and the brain by quantifying the complexity of their signals versus brain signals.

Understanding the correlation between the brain’s activity and the physiological responses of other organs under varying conditions is a crucial area of research that holds significant potential for advancing our knowledge of human physiology. In this study, we focused on investigating the interaction between the heart and brain by employing advanced complexity analysis techniques, specifically examining the fractal dimension and approximate entropy of electroencephalogram (EEG) and R-R interval time series. The analysis was conducted on data collected from 12 subjects who were observed under three distinct conditions: baseline (normal resting state) and two collaborative activities performed both with and without the presence of noise. Our findings revealed that the complexity patterns of EEG and R-R signals showed similar trends in alterations across all conditions, suggesting a strong coupling between the brain’s and heart’s responses. This observed coupling highlights the potential for a coordinated physiological interaction between these two critical systems. Furthermore, our approach, which successfully decoded the heart–brain correlation, offers a promising framework for extending this analysis to explore correlations between the brain and other organs, thereby contributing to a deeper understanding of the complex networks that underlie human health and adaptive physiological responses.