Although researchers have widely explored the relationship between EEG and heart rate variability (HRV), the results are not always consistent mainly due to the variety of tasks. In particular, several factors, such as mental fatigue and sleepiness, can affect the alpha power, which makes it difficult to obtain a direct relationship between alpha and heart rate activities. This study investigates the brain–heart interplay that is consistently observed in various mental states: listening to music and resting. To eliminate the indirect effects of mental states on alpha power, subjective fatigue and sleepiness in the resting condition and their emotional valence and arousal in the music condition were measured. A partial correlation analysis in the music condition, which excluded the indirect effects of emotional valence and arousal level, showed a positive correlation between the power of the occipital alpha2 component (10-12 Hz) and nHF, a measure of parasympathetic activity. In a similar vein, a partial correlation analysis in the resting condition, excluding subjective fatigue and sleepiness effects, showed a positive correlation between the occipital alpha2 component and nHF. These results indicate a brain–heart interplay that is frequently observed in various subjective states and that still exists after eliminating the effects of other variables.
Article
The Relationship Between Alpha Power and Heart Rate Variability Commonly Seen in Various Mental States
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The homeostatic control of the internal body state and its influence on brain functions are known to shape behavior and cognition1, 2. Therefore, a functional assessment of the brain–heart interplay can provide insight into human physiological and behavioral dynamics. EEG alpha power is known to reflect changes in alertness. In particular, relaxed or drowsy mental states are associated with an increase in alpha power. For example, an alpha power increase can be observed in a meditative state, indicating a state of relaxed alertness3, 4. According to Shaw5, alpha power increase is associated with inner-directed attention, while alpha power decrease is associated with outer-directed attention.
The direct relationships between human EEG alpha waves and heart rate variability (HRV) are often studied using a variety of tasks, making them difficult to obtain. For example, reduced alpha power is associated with increased sympathetic activity due to lower attentional levels6, mental fatigue7, and rather decreased parasympathetic activity due to increased emotional arousal8. Therefore, several factors can influence alpha power, making it difficult to establish a direct relationship between alpha and heart rate activities.
This study aims to examine the brain–heart interplay that is consistently observed across various mental states. Based on the fact that emotional stimuli affect both EEG and HRV9, 10, this study examines the relationship between EEG and heart rate while listening to emotional music. In addition, based on the relationship between EEG and HRV during the resting state in the same subjects, the brain–heart interplay commonly observed across various mental states (i.e., listening to music and resting) are examined. In particular, subjective arousal and emotional valence when listening to music, as well asl fatigue and drowsiness during rest, are measured. By parsing out these effects in brain–heart interplay, this study determines common brain–cardiac interactions across various mental states. In addition, alpha waves are divided into alpha1 and alpha2, which have been associated with various physiological states11 and mental functions12. This approach allowed us to more thoroughly examined the common brain–heart interplay across various biological states.
Resting state
Subjective change in fatigue and sleepiness
Figure 1 shows the VAS scores for fatigue and sleepiness. VAS scores of subjective fatigue after the task tended to be higher than those before the task (t (14) = 2.00, p = .066, d = 0.51). No difference was observed in VAS scores of sleepiness before and after the task (t (14) = 0.22, p = .826, d = 0.06). Therefore, the mental state changed over experimentation.
EEG
Figure 2 shows the power spectrum density of the EEG for resting (Figure 2A), showing clear alpha peaks. The alpha1 (8–10 Hz) and alpha2 (10–12 Hz) power for pre- and post-task conditions, respectively, were calculated. Paired t-test showed no significant difference in the alpha1 and alpha2 power between pre- and post-tasks (alpha1, Figure 3A: t (14) = 1.02, p = .327, d = 0.26; alpha2, Figure 3B: t (14) = 1.70, p = .111, d = 0.44).
HRV
Figure 4 shows the HRV between pre- and post-task conditions. No difference was observed between both conditions (t (14) = 0.21, p = .833, d = 0.06).
Music exposure
Subjective arousal and valence for music stimuli
Figure 5 shows the subjective ratings of arousal and valence for each song. A one-way ANOVA shows no arousal or valence rating differences among songs (arousal: F (11, 154) = 1.19, p = .296, 
= 0.08; valence: F (11, 154) = 1.45, p = .158, = 0.09).
EEG
Figure 2B shows the power spectrum density of the EEG for music exposure, showing clear alpha peaks for each music type. During the music exposure, alpha1 and alpha2 were modulated by the music (alpha1, Figure 6 top: F (11, 154) = 38.41, p < .001, = .73; alpha2, Figure 6 bottom: F (11, 154) = 39.21, p < .001, = .74). Thus, our music stimuli evoked various EEG responses.
HRV
Figure 7 shows the HRV for various music types. We discovered that different music styles affected HRV (F (11, 154) = 2.31, p = .012, = .14).
Graphical modeling
This study aims to find the common brain–heart interplay across various biological states. Figure 8 shows the results of graphical modeling during music exposure. The results show a positive partial correlation between emotional arousal and nHF component while parsing out the effects of alpha1 power and emotional arousal ratings. For the alpha2 component, there is a positive partial correlation between emotional arousal ratings and nHF (index of parasympathetic activity).
The partial correlation during the resting state was further evaluated. Differences between pre- and post-task measurements were used in the analysis. Figure 9 shows the results of graphical modeling for resting data. A positive partial correlation between nHF and sleepiness and a negative partial correlation with alpha1 and mental fatigue ratings were observed. For the alpha2 component, there is a negative partial correlation with mental fatigue ratings while parsing out the effects of nHF and sleepiness. Furthermore, a positive partial correlation between nHF and sleepiness ratings was observed. Critically, a positive partial correlation between alpha2 and nHF in the listening data was observed again.
This study examines the common brain–heart interplay in various biological states. A positive partial correlation was observed between arousal ratings and the alpha2 components when listening to music. This finding is consistent with recent studies showing that emotional stimuli increase alpha power (Aftanas et al., 2002; Uusberg et al., 2013). For example, Uusberg et al. (2013) reported the increased alpha power to emotionally aversive stimuli (high arousal negative stimuli from IAPS: Lang et al., 2005). This relationship between alpha and emotional arousal suggests the inhibitory role of alpha in the affective domain. However, according to another line of studies, emotional stimulus processing is more closely related to decreased alpha power (e.g., Balconi & Mazza, 2009; Schubring & Schupp, 2021). In particular, a negative correlation between arousal and alpha is often reported (De Cesarei & Codispoti, 2011). According to Uusberg et al. (2013), the discrepancy in these results is due to methodological differences: while participants in the study of De Cesarei and Codispoti (2011) simply observed emotional stimuli, they made evaluations about emotional contents in the study of Uusberg et al. (2013) as well as the present study. Thus, the existence of goals (i.e., evaluations) competing with focusing on emotional items may modulate the involvement of alpha waves. Importantly, a positive correlation between alpha and emotional arousal was observed by parsing out the other components (i.e., HRV). We believe this approach would provide further insight into the direction of modulation of alpha power in emotional processing.
In terms of the resting state between pre- and post-task conditions, a negative partial correlation between the alpha2 component and fatigue ratings was observed. This observation is consistent with the findings showing that after mental fatigue-inducing tasks, alpha power decreases (Ishii et al., 2013; Shigihara et al., 2013; Tanaka et al., 2012). Mental fatigue is a psychological state of low alertness (Boksem & Tops, 2008). Although alpha1 has been reported to be more sensitive to mental fatigue compared to alpha2 (Li et al., 2017; Sun et al., 2014), the results of this study are generally consistent with those of studies that have shown mental fatigue to modulate EEG activities.
Critically, a positive partial correlation between the alpha2 and nHF components in listening and resting conditions was observed. This relationship would be the common brain–heart interplay in various biological states. Both higher alpha and parasympathetic activities are attributed to relaxed subjective states (Duschek et al., 2015; Triggiani et al., 2016). In addition, increases in alpha power have been discussed as a functional inhibition of cortical activation (Clayton et al., 2018; Jensen & Mazaheri, 2010). For example, increased occipital alpha power has been reported in ipsilateral to the visually attended location (Kelly et al., 2006; Foxe & Snyder, 2011). Similar changes in alpha power have been observed in working memory tasks (Bonnefond & Jensen, 2012; Jensen et al., 2012). These results suggest that alpha oscillations reflect inhibition in the sensory area, which can suppress external inputs (Clayton et al., 2018; Jensen & Mazaheri, 2010). The results suggest that in both biological states (i.e., listening and resting), the more relaxed the participants are (as reflected in larger nHF), the more alpha power they have, which leads to less external processing.
This positive relationship between parasympathetic activity and alpha power is similar to the relationship between alpha waves and HRV during NREM sleep, also known as quiet sleep. According to Ehrhart et al. (2000), during NREM sleep, an increase in alpha power was associated with a decrease in LF/HF ratio (parasympathetic activity dominant), while a decrease in alpha power was associated to a high LF/HF ratio (sympathetic activity dominant) in REM sleep. According to these observations, Ehrhart et al. (2000) suggest that alpha waves could reflect sleep-maintaining processes. Alpha oscillations during sleep would play some functional role even in the arousal state. One possible neural network to support this interplay is the default mode network (DMN), which decreases its activity for externally oriented tasks (Raichle et al., 2001; Raichle, 2015). The DMN has been reported to be related to stimulus-independent thought or mind wandering, reflecting enhanced watchfulness toward the environment (e.g., Fox et al., 2015; Gilbert et al., 2007). In addition, the DMN is known to be influenced by cardiac and respiratory activities (Tong et al., 2013) and to show overlap with spatial patterns of alpha waves (Knyazev et al., 2011). Deep sleep can decrease functional connectivity within the DMN (Horovitz et al., 2009). Based on these empirical findings, Jerath and Crawford (2015) suggest that the DMN should be an indispensable neural network for consciousness and may underlie higher processing. In this context, the results of this study regarding the positive relationship between parasympathetic and alpha activities, commonly observed in listening to music and resting, would reflect this functional role of the DMN in maintaining a consciousness state or internally oriented cognition. Further research is needed to examine neural mechanisms underlying the interplay between the heart and brain.
This study has some limitations: It has been suggested that there are several alpha components distributed in the human brain (Barzegaran et al., 2017; Takahashi & Kitazawa, 2017). However, these multiple alpha components and their relationship with HRV are unclear. In addition, this study does not address the direction of interaction between alpha oscillations and HRV. Recently, Candia–Rivera et al. (2022) proposed the bidirectional model of cortical and parasympathetic–sympathetic activities. The bidirectional interplay between alpha power and HRV should be investigated.
In conclusion, a direct heart–brain interplay can be observed by excluding the indirect effects of subjective state on EEG and HRV activities, which shows a positive partial correlation between the alpha2 and nHF components. This direct relationship between alpha power and HRV suggests that more relaxed participants (as reflected in larger nHF) show less external processing (as reflected in larger alpha power) in both biological states.
Participants
Seventeen healthy volunteers participated in the study; all were male. One participant was removed because there were excessive noise in his EEG data, and another was removed because he did not properly understand the instruction (i.e., he inappropriately rated his mental state on the visual analog scales (VAS)). Finally, 15 participants, aged 20 to 23 years, were included in the analyses. The study was approved by the ethics and safety committee of the University of Tokyo (approval number: 21–34). A written informed consent was obtained from all participants, and all study methods were conducted according to the relevant guidelines and regulations.
Stimuli
Twelve 3-minute music pieces (stimuli) that could evoke emotional responses were selected. Eight music files from the MER database were created44 and assigned to a coordinate on the arousal–valence emotion plane, containing two songs for each quadrant. The remaining four pieces of music were then selected, that is, “Canon in D major,” “Hi friend!,” “Romance,” and “Persian 6/8” from Naji et al.45 because they were less frequently heard and assumed to evoke no memories.
Experimental procedure
Before and after the music sessions, EEG (electroencephalogram) and ECG (electrocardiogram) were recorded during a 3-minute eye-closed rest. After each 3-minute session, the participants were required to rate their fatigue and sleepiness using VAS based on the methods of Ishii et al.7 and Lee et al.46.
In the music condition, the overall experiment was divided into 12 blocks. Each block began with a 3-minute music presentation during which EEG and ECG were recorded while participants closed their eyes. After listening to the music, the participants completed their arousal and valence ratings using the self-assessment manikin (SAM).
Psychophysiological measures
EEG
EEG activity was digitally recorded using a StarStim (Neuroelectrics, Barcelona, Spain). Eight-channel dry electrodes were placed in a Neuroelectrics cap in positions F3, F4, C3, C4, P3, P4, O1, and O2 of the 10–20 International Electrode Placement System. EEG was sampled at 500 Hz and analyzed using MNE-Python47 (v0.24.1).
An offline bandpass filter from 2 to 40 Hz was applied. The data were re-referenced to all electrodes. The continuous EEG data was then segmented into 2-second segments with no overlap. Epochs that exceeded a voltage threshold of 150 μV (absolute) were excluded from further analyses. A fast Fourier transform was then used to calculate the EEG power spectra with a frequency resolution of 0.5 Hz using the Welch method. The frequency band was divided into alpha1 (8–10 Hz) and alpha2 (10–12 Hz). The P3/4 and O1/2 electrodes were used for analysis because the 8–12 Hz signals are prominent in the parieto-occipital region48.
ECG
ECG activity was digitally recorded from a StarStim (Neuroelectrics) at 500 Hz with an ECG cable extension attached to the subject’s right wrist and left ankle. ECG analyses were conducted using pyHRV49 (v0.4.0). After the automatic detection of the R peaks, the inter-beat interval between R peaks was calculated. Then, a spectral analysis was performed using a Welch method to quantify the HRV in the LF (0.04–0.15 Hz) and HF (0.15–0.4 Hz). We calculated normalized LF and HF power, defined as the relative power of each component in proportion to total power minus VLF50: normalized LF as the LF power in normalized units LF/(total power − VLF) × 100 and normalized HF as the HF power in normalized units HF/(total power − VLF) × 100.
Data analysis
The target variables served as the nodes of the undirected graph created for this analysis, and the partial correlations corresponding to each edge were then calculated. The edge with the lowest absolute value of partial correlation was repeatedly eliminated, stopping when the Akaike information criterion increased. In the final undirected graph, variables that not connected by edges are independent when other variables are fixed, and vice versa, even when the influence of other variables is excluded.
Acknowledgments:This research was supported by a JSPS Grant-in-Aid for Early-Career Scientists (JP20K14274) to T.K. The authors would like to thank Enago (www.enago.jp) for the English language review.
Author contributions:
H.S. performed experiments and H.S. and T.K. analyzed data, and H.S., T.K., and K.A. designed experiments, defined, and validated data analysis methods, and wrote the paper.
Data availability statement:
The data that support the findings of this study are available on request from the corresponding author.
Additional Information:
No potential conflict of interest was reported by the authors.
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No competing interests reported.