The Effect of Music on Resistance to Mental Fatigue：Evidence of EEG Power Spectrum

doi:10.21203/rs.3.rs-5289565/v1

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The Effect of Music on Resistance to Mental Fatigue：Evidence of EEG Power Spectrum

https://doi.org/10.21203/rs.3.rs-5289565/v1

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Objective: To evaluate the efficacy of music listening in alleviating mental fatigue among healthy participants and to explore the neural evidence by electroencephalography (EEG).

Methods: A total of 30 participants were recruited and randomly assigned to either the Music or Control groups. Mental fatigue was induced in both groups using a 30-minute Stroop task. Following this task, the Music group listened to music for 20 minutes, while the Control group sat quietly for the same duration. Measurements were taken at three time points: before the Stroop task, immediately after the Stroop task, and after the 20-minute intervention period. Visual Analog Scale (VAS) scores and 3-minute resting-state EEG signals were collected at each time point.

Results: The data indicated that music listening significantly reduced mental fatigue. VAS scores decreased more in the Music group than the Control group (P=0.031). The EEG iAPF showed a significant recovery in the Music group (P<0.0001). Delta power in the frontal region decreased significantly post-intervention in the Music group (P=0.011). Theta and alpha power also decreased significantly in the Music group across multiple brain regions (all Ps<0.0076), with no significant changes observed in beta power.

Conclusion: These findings highlight the potential of music as a non-invasive and enjoyable intervention for mitigating the effects of mental fatigue. Moreover, iAPF, theta, and alpha power can serve as reliable biomarkers for assessing mental fatigue and the restorative effects of interventions like music.

Music

Mental fatigue

Stroop task

iAPF

EEG power spectrum

Mental fatigue is described as a psychobiological state resulting from extended and/or demanding cognitive activities, often accompanied by sensations of "tiredness" and "lack of energy", which is a common phenomenon experienced in various domains of life, such as work, education, and daily activities (Rozand & Lepers, 2016). A study found that approximate 22% of the population reported experiencing fatigue, which manifests as an unpleasant mix of physical, cognitive, and emotional symptoms (Galland-Decker et al., 2019). Particularly, mental fatigue can disrupt daily routines, lowering productivity and efficiency in studies (Galland-Decker et al., 2019). Research has shown that mental fatigue can significantly decline cognitive functions (Lorist et al., 2000). Gaining a deeper understanding of the neurophysiological bases of mental fatigue is crucial for mitigating its detrimental impacts on healthy individuals.

The detection of mental fatigue can be confirmed through various subjective and objective indicators. Recent findings suggest that employing a visual analog scale (VAS) is the most effective method for measuring mental fatigue (Smith et al., 2019). However, incorporating electrophysiological markers alongside subjective assessments is considered vital for accurately characterizing mental fatigue. Electroencephalography (EEG) is a non-invasive neurophysiological technique that measures the electrical activity of the brain, providing valuable insights into cognitive processes and neural mechanisms. By analyzing the power spectrum of EEG signals, researchers can identify specific brainwave patterns associated with different cognitive states and evaluate the impact of external stimuli, such as music, on these patterns. Research utilizing EEG has demonstrated significant changes in brain oscillations due to mental fatigue. A recent meta-analysis has identified an increase in theta power as the most dependable biomarker for mental fatigue (Tran et al., 2020). Additionally, an increase in alpha power, which is associated with reduced attention, has also been observed in cases of mental fatigue (Arnau et al., 2017).

Music, as a non-invasive and accessible intervention, has been increasingly recognized for its potential to alleviate mental fatigue and enhance well-being. In recent years, researchers have become increasingly interested in investigating the impact of music on mental fatigue and its potential to enhance cognitive performance. The therapeutic effects of music are multifaceted, involving both psychological and physiological pathways.

Psychologically, studies have explored the relationship between music and cognitive functioning, highlighting the potential benefits of music in improving attention, memory, and overall cognitive performance (Chanda & Levitin, 2013; Thoma et al., 2013). As evidenced in a study by Jacquet et al.(2021), listening to music was efficient strategies to counteract the negative effects of mental fatigue. Similarly, Axelsen et al. (2020) found that music intervention was effective in reducing the negative effects of mental fatigue on cognitive processing, indicating a potential for music to enhance cognitive control. Further, the study by Guo et al. (2015), demonstrated that listening to relaxing music not only alleviated the mental fatigue associated with performing a continuous performance task but also maintained performance by influencing neurobehavioral responses and brain activity. Gao et al. (2023) further explored the compensatory effect of music on mental fatigue in steady-state visually evoked potential-based BCIs, finding effective relief in participants' mental fatigue, suggesting that background music can provide a practical solution for long-duration tasks. Physiologically, music has been shown to influence autonomic nervous system activity, with implications for heart rate variability and stress reduction. Previous studies have demonstrated the influence of music on brainwave patterns, showing that music can modulate neural oscillations in specific frequency bands (Kawashima et al., 2024; Thoma et al., 2013). Thoma et al. (2013) found that listening to music reduces physiological stress markers such as heart rate, blood pressure, and cortisol levels, leading to overall stress reduction. Overall, the evidence suggests that listening to music is an effective non-bioactive strategy to counteract mental fatigue and to maintain positive psychological states. These findings underscore the potential of music as a simple, accessible intervention for managing mental fatigue across various settings.

However, the specific mechanisms underlying the effect of music on mental fatigue remain relatively unexplored. By examining the EEG power spectrum, it could help us provide empirical evidence on the effect of music on mental fatigue and elucidate the underlying neural mechanisms. Alpha power in EEG has been linked to parasympathetic activity, suggesting that music may foster relaxation and mitigate mental fatigue through neurophysiological mechanisms(Kawashima et al., 2024). Additionally, studies have explored the impact of music on neural oscillations (Nolden et al., 2017; Wollman et al., 2020). Drozdenko et al. (2023) found that music significantly increased alpha and theta rhythm activity in different brain regions and decreased delta rhythm activity in the frontal and occipital lobes. Furthermore, a study by Nolden et al. (2017) indicated that music training enhanced frontal theta and alpha activities. These studies demonstrate that music significantly affects neural oscillations.

In this study, unlike the method used by Guo et al. (2015), music was not played during but rather after the mental fatigue induced by cognitively demanding activities, aiming to explore the neural effects of music on mental fatigue that had been induced. Previous findings have indicated that music might distract during cognitive tasks, potentially impairing performance and affecting brain oscillations (Kou et al., 2018). Therefore, in our approach to induce mental fatigue, it was crucial that participants remained undisturbed during the challenging tasks.

The primary aim of this study was to assess the effectiveness of music listening in alleviating mental fatigue in healthy participants and to investigate the neural evidence through EEG. We hypothesized that listening to music could be an effective strategy to combat mental fatigue. This potential recovery effect was expected to manifest in brain oscillations, with a significant decrease in theta and alpha power.

Participants

A total of thirty participants were randomly divided into two groups: the Music (n=15) and Control (n=15) groups. There were no statistical differences between the two groups in gender, age, height, body mass, and body mass index (BMI) (Table 1). They are all right-handed by self-report and have no special music experience. It was essential that participants did not have any neurological disorders, which could influence brain function. Additionally, all participants were required to have normal hearing capabilities, with no hearing impairments. Prior to the experiment, all participants provided informed consent. The protocol, including the selection criteria and the experimental conditions, was approved by this university ethical review board.

Table 1 Demographic data of participants

	Music (n=15)	Control (n=15)	t/x²	P value
Gender (male/female)	8/7	10/5	0.556	0.456
Age (years)	22.2±2.0	21.6±2.7	0.611	0.545
Height (cm)	167.0±7.8	169.9±9.0	0.925	0.362
Body mass(kg)	63.4±9.5	67.4±11.4	1.029	0.312
BMI (kg/m²)	22.6±1.7	23.1±1.8	0.796	0.432
Educations (years)	15.2±1.5	14.5±2.3	1.000	0.325

Experimental Design

In this study, a randomized controlled design was implemented, comprising two groups: the Music and Control groups. The experiment encompassed three testing sessions. The initial session served as the baseline assessment, followed by the induction of mental fatigue in the second session, and finally, the music intervention in the third session. During the baseline assessment, data collection included the Visual Analog Scale (VAS) and resting-state EEG. In the second session, mental fatigue was induced through a 30-minute Stroop task, followed by assessment using the VAS and EEG power spectrum analysis. Subsequently, in the third session, participants engaged in either a 20-minute music session or remained in silence after mental fatigue, depending on group assignment. The same evaluations of VAS and 3-min EEG collection were conducted following the intervention. The music chosen for the intervention aimed to provide a pleasant and engaging auditory experience. Participants in the Control group experienced silence after mental fatigue, without specific auditory stimuli. The experimental setup is illustrated in Fig. 1.

Experimental Procedure

Baseline Assessment

Firstly, a 3-min resting-state EEG signals with eyes closed were recorded and then the VAS was assessed, to test whether there were significant differences in degrees of mental fatigue and EEG power spectrum in baseline state.

Induction of Mental Fatigue Based on Stroop Task

In this study, the Stroop task served as the method to induce mental fatigue. The task involved displaying colored words on a computer screen using E-prime 3.0 software, prompting participants to respond based on the color of the word presented. Two conditions were included: congruent and incongruent. In the congruent condition, the color names matched the color displayed (e.g., the word "red" presented in red color). Conversely, in the incongruent condition, the color names did not correspond to the color displayed (e.g., the word "red" displayed in blue color). Following the protocol outlined by Niu et al. (2024), in this study the incongruent condition was utilized to induce mental fatigue. In this condition, participants were tasked with swiftly and accurately identifying the color of the word, disregarding the word itself. The Stroop task was continuously administered for duration of 30 minutes. Participants' fatigue levels were monitored using the VAS, while accuracy and reaction time during the Stroop task were recorded for analysis.

Music Intervention

During the music listening phase, participants were instructed to spend 20 minutes seated in front of a black screen while listening to enjoyable music. The selection of relaxing music was sourced from functional music curated by the China Institute of Sport Science, which had been utilized to assist Chinese athletes in alleviating mental fatigue during their preparation for the Olympic Games. The music was played through a power amplifier connected to a computer system. To maintain consistent volume levels, music software was employed to set the volume at 50 dB and ensure a tempo ranging from 65 to 80 beats per minute. All participants were exposed to the same set of eight instrumental folk music pieces following the fatigue-inducing task. Conversely, participants in the control group engaged in a 20-minute seated rest period.

Data Collection and Analysis

Behavioral Data Collection and Analysis

Reaction time and error rate were measured as behavioral variables via E-prime 3.0 (Psychology Software Tools, Pittsburgh, PA, USA).

Subjective Data Collection and Analysis

The VAS serves as a widely employed approach for assessing mental fatigue. It entails presenting individuals with a horizontal line, typically spanning 10 cm, with anchor points situated at each end denoting extreme states of mental fatigue (e.g., "Not at all fatigued" and "Extremely fatigued"). Participants are instructed to indicate a point on the line corresponding to their prevailing level of mental fatigue. Subsequently, the distance from the "Not at all fatigued" end to the participant's mark is quantified and utilized as a measure of mental fatigue severity. Various studies have employed the VAS as a subjective gauge of mental fatigue (Matthews & Desmond, 2002; Niu et al., 2024). Participants were mandated to complete the VAS scale within a minute. If the score on the scale surpasses 50 points, it indicates that individuals have fully attained the level of mental fatigue (Niu et al., 2024).

EEG Data Acquisition

The EEG data were captured using the ANT system (eego™mylab, ANT Neuro, Netherlands). Initially, participants underwent scalp cleaning to ensure impedance levels remained below 10 kΩ. Subsequently, a high-density EEG cap outfitted with 64 electrodes was meticulously positioned on the participant's head, adhering to the international 10-20 system for electrode placement. Care was taken to adjust the cap for proper alignment and snugness, facilitating optimal electrode-scalp contact. Prior to electrode attachment, conductive gel was administered to enhance electrical conductivity. The ANT system employed a reference electrode situated on the CPz, while a ground electrode was affixed to the forehead, positioned between AFz and Fz. EEG signals underwent amplification via a high-fidelity amplifier, featuring a sampling rate of 1000 Hz and a low-pass filter set at 100 Hz. These amplified signals were subsequently digitized and stored for subsequent offline analysis.

Throughout the data collection process, participants were instructed to maintain a comfortable seated position in a quiet environment with closed eyes, minimizing exposure to external stimuli. To mitigate the risk of artifacts, participants were urged to limit body and head movements.

EEG Data Preprocessing

The EEG data underwent preprocessing using the eeglab toolbox within a Matlab script. Initially, raw data was visually inspected to detect and eliminate evident artifacts, such as EMG activities or other sources of noise. Following artifact removal, the continuous EEG data were segmented into 5s epochs, allowing for enhancement of the signal-to-noise ratio by dividing the continuous data into smaller segments. Subsequently, the data was downsampled to 250 Hz and filtered within the frequency range of 0.1 to 30 Hz, with a notch filter implemented at 50 Hz to remove powerline interference. To further refine the dataset, independent component analysis (ICA) technique was applied to mitigate artifacts associated with eye blinks, muscle activity, or other sources of interference. Finally, average re-referencing was employed to enhance the overall quality of the data, ensuring consistency and reliability throughout subsequent analyses.

EEG Power Spectrum Analysis

EEG power spectrum analysis is a widely employed technique for investigating the frequency characteristics of EEG signals and elucidating the distribution of power across various frequency bands. Initially, the Fourier Transform is applied to each epoch, transforming the time-domain EEG signal into the frequency domain. Subsequently, after the EEG data has been converted into the frequency domain, power spectral density (PSD) estimates are computed using the Welch method (Welch, P. (1967). The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms.

The PSD provides insight into the distribution of power across distinct frequency bins. In the context of this study, specific frequency bands are delineated to explore particular brainwave activities. These bands typically encompass delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequencies. Furthermore, the study also involves the calculation of the EEG individual alpha peak frequency (iAPF) (Zhang et al., 2021), offering additional insights into individual alpha oscillatory patterns. On the basis of relevant published research and the topography of our own data (Zhang et al., 2021), in the present study iAPF identification was at parietal-occipital regions (O1, O2, Oz, P1, P2, P3, P4, and Pz), where the average value of was calculated.

Statistical Analysis

The SPSS 19.0 and GraphPad Prism 9.0 were used to analyze the data. The Shapiro–Wilk test indicated that all the data conformed to normality distribution. Statistical analyses of behavioral data, subjective data, EEG power spectrum, and iAPF were performed using a 2×3 mixed-design ANOVA. The between-subjects variable was condition (Music and Control), and the within-subject variable was the testing phase (baseline (Pre), after mental fatigue (Post), and after music intervention (Post-20). Degrees of freedom were corrected whenever necessary using the Greenhouse-Geisser epsilon correction factor. The post-hoc multiple comparisons was conducted by Bonferroni correction to the P value. The statistical tests in gender, age, height, body mass, BMI, and education between the two groups were performed by chi-square test or independent sample t-test. The significant level was set at P <0.05.

Subjective Feeling of Mental Fatigue

There was no interaction between test phase and group (F _{(2, 56)}= 2.283, P=0.114, η²_p=0.075). A significant test phase effect was shown on the VAS (F _{(1.709, 47.86)} = 64.15, P < 0.0001, η²_p = 0.696). However there was no significant group effect (F_{(1, 28)}=1.551, P=0.223, η²_p=0.029). Post-hoc analyses indicated an increase in the VAS between Pre and Post in the Music group (t=6.902, P <0.0001, d=1.7816) and in the Control group (t= 8.737, P<0.0001, d=2.255), followed by a decrease between Post and Post-20 in the Music group (t = 6.897, P=0.0007, d=1.275) and in the Control group (t = 3.181, P=0.02, d=0.821). However, the VAS remained higher at Post 20 than at Pre in the Music (t =2.607, P =0.062, d=0.673) and in the Control group (t =5.539, P =0.0002, d =1.430). Notably, at the Post-20 phase a significant group effect was reported, with a lower VAS in the Music than in the Control (t=2.744, P=0.031, d=1.001) (Fig. 2). These results suggested that our fatigue-inducing task cause mental fatigue in both groups, but it affected the Control group more than the Music group.

Behavioral Performance

Fig. 3 indicated that reaction time increased and ACC decreased for both the Control and Music groups during the Stroop task. However, there was no significant difference in reaction time and ACC between the two groups. These data suggest that the Stroop task induced the same degree of mental fatigue in both groups.

EEG Spectrum after Mental Fatigue at Rest

iAPF

A significant interaction between test phase and group was reported (F_{(2, 56)}= 8.287, P=0.0007, η²_p=0.228). There was also a significant main effect of test phase on the iAPF (F_{(2, 56)}= 29.65, P<0.0001, η²_p =0.514). Post-hoc analyses showed a decline in the iAPF between Pre and Post in the Music group (t = 6.324, P< 0.0001, d = 1.632) and in the Control group (t = 4.548, P< 0.0001, d = 1.174). This was followed by an increase between Post and Post-20 in the Music group (t = 5.815, P< 0.0001, d = 1.501), but no significant difference in the Control group (t = 0.1838, P> 0.999, d = 0.047). However, the iAPF remained lower at Post-20 than at Pre in the Control group (t = 4.364, P= 0.0002, d = 1.126). Additionally, at the Post-20 phase, the iAPF in the Music group was higher than in the Control group (t = 2.637, P = 0.029, d = 0.830) (Fig. 4).

Delta: There was a significant main effect of the test phase on delta power in the frontal region (F _{(1.66, 46.57)} = 7.749, P=0.002, η²_p=0.216). As shown in Fig. 5a, delta power increased after the Stroop task. Following 20 minutes of music listening, delta power in the frontal region significantly decreased at Post-20 compared to Post in the Music group (t=4.823, P=0.011, d=0.873) (Fig. 5a).

Theta: The test phases significantly affected theta activity in all brain regions (all Ps ≤ 0.003), regardless of whether subjects were in the Music or Control group. The most pronounced effect was observed in the parietal region (F_{(2, 56)} = 12.08, P< 0.0001, η²_p = 0.301). Post-hoc analyses indicated a significant increase in theta power in the frontal, central, parietal, and occipital regions between Pre and Post (all Ps < 0.0462, d > 0.63) in both Music and Control groups. A significant decrease in theta power was observed in these regions between Post and Post-20 (all Ps < 0.0076, d > 0.809) in the Music group, but not in the Control group. These data imply that listening to music after mental fatigue can reduce theta power in all regions (Fig. 5b).

Alpha: A two-factor ANOVA on brain oscillations revealed a significant test phase effect in the parietal (F_{(2, 56)} = 7.123, P=0.0018, η²_p = 0.202) and occipital (F_{(1.934, 54.15)} = 16.58, P<0.0001, η²_p =0.371) regions. Post-hoc analyses indicated a significant increase in alpha power in the parietal (t = 3.494, P= 0.0431, d = 0.638) and occipital (t = 4.220, P= 0.0251, d = 0.770) regions between Pre and Post in both groups. Similar changes were observed in the Control group in the parietal (t = 3.565, P = 0.0382, d = 0.65) and occipital (t = 5.015, P = 0.0085, d = 0.915) regions. After the intervention, a significant decrease in alpha power in the parietal (t = 4.733, P = 0.0041, d = 0.863) and occipital (t = 7.062, P = 0.0005, d = 1.289) regions was observed in the Music group between Post and Post-20, but no such changes were found in the Control group (Fig. 5c).

Beta: There were no significant effects of test phase and group on beta power in any brain region (Fig. 5d).

This study aimed to confirm the effect of listening to music on mental fatigue following a prolonged cognitively demanding task, using evidence from the EEG power spectrum. The results indicated a progressive decrease in subjective mental fatigue, accompanied by increases in delta, theta, and alpha rhythms power. Additionally, the results also showed that listening to music effectively counteracted the negative effects of mental fatigue, with associated changes in delta, theta, and alpha rhythms power.

In this study, both the music group and the control group exhibited signs of mental fatigue after completing a 30-minute Stroop task. The 30-minute Stroop task has been effectively utilized in prior research to induce mental fatigue (Niu et al., 2024; Veness et al., 2017). Subjective markers, such as the VAS for mental fatigue, were used in their study to confirm the presence of mental fatigue. In our study, the increase in subjective fatigue reported by participants, as measured by the VAS, confirmed the successful induction of mental fatigue. Additionally, objective indicators were observed; the ACC and RT in the Stroop task reflected changes in participants' executive abilities during task performance. ACC showed a gradual decline with the onset of mental fatigue, and a similar pattern was observed for RT, which gradually increased as mental fatigue developed. In conclusion, the increased subjective feelings of fatigue reported by participants, along with the observed decline in ACC and the prolonged RT in the Stroop task, all indicate the successful induction of mental fatigue in the participants.

Importantly, based on the scores from the VAS, listening to music reduces mental fatigue more effectively than the control group. A previous study found that intermittent odors during a continuous performance task helped reduce mental fatigue and maintain performance (Kato et al., 2012). The present study demonstrated a similar effect using relaxing music. Listening to relaxing music after a fatigue-inducing cognitive task may help individuals feel less mentally fatigued. The effects of relaxing music on alleviating mental fatigue may better explain why people tend to listen to music while working or driving in daily life.

EEG spectral changes have been recognized as a viable biomarker for predicting and alerting individuals to fatigue resulting from prolonged mental effort (Chai et al., 2017). Variations in the EEG power spectrum provide insights into the effects of music on mental fatigue. A review has shown that a marker for mental fatigue is present despite some inter-individual variability (Tran et al., 2020). This review suggested that an increase in theta activity, particularly in the frontal, central, and posterior brain regions, can serve as a robust biomarker for mental fatigue. An enhancement in alpha wave activity can be considered a secondary biomarker to account for individual variability, while smaller changes were observed in the delta and beta bands (Tran et al., 2020). In this study, analyses of brain oscillations revealed a significant increase in delta, theta, and alpha power in the frontal, central, posterior, and occipital regions during the resting state following mental fatigue induced by the Stroop task. Although the effects of mental fatigue are extensively documented in the literature, the underlying mechanisms remain elusive. Prolonged neural activity can lead to an increase in adenosine concentration, stemming from the breakdown of adenosine triphosphate (ATP) (Lovatt et al., 2012), which may account for the phenomenon of mental fatigue. Adenosine, by inhibiting neuronal excitability, can induce mental fatigue and reduce attentional capacity.

A study by Fan and colleagues (2015) observed increases in delta activity in the central region associated with mental fatigue. In contrast, our study demonstrated that the increase in delta activity occurred in the frontal region. This discrepancy in delta activity findings may be attributable to substantial ocular artifacts, which often share the same frequency band as delta activity, particularly in areas closer to the front of the scalp. Increased theta and alpha wave amplitudes (synchronization) have been consistently found to be associated with the onset of mental fatigue (Barwick et al., 2012; Craig et al., 2012). This phenomenon can be explained by the fact that synchronization reflects a more synchronized firing of neurons, which is typically seen in states of reduced cognitive demand and vigilance(Klimesch, 1999). Theta waves, primarily observed in the frontal and central regions, are often linked to drowsiness, meditation, and the transition from wakefulness to sleep. Alpha waves, predominantly found in the occipital region, are associated with relaxation and decreased attention to external stimuli (Sauseng et al., 2005). The process of desynchronization, on the other hand, involves a reduction in the amplitude of EEG waves and is generally linked to increased cortical activity or an alert, non-fatigued brain. Desynchronization indicates that neurons are firing more independently, which is often necessary for higher cognitive functions and active information processing (Pfurtscheller & Lopes da Silva, 1999). During tasks that require heightened attention and cognitive engagement, a decrease in theta and alpha wave amplitudes is commonly observed, signifying desynchronization and an active brain state. Conversely, during the resting state theta and alpha wave amplitudes commonly increase at the state of mental fatigue. This increase in theta and alpha wave amplitudes during mental fatigue reflects a shift towards a more synchronized neuronal activity pattern, which is often associated with a decrease in cognitive performance and vigilance. As mental fatigue sets in, the brain's ability to process information efficiently diminishes, leading to a more pronounced synchronization of neural oscillations. This phenomenon can be observed in various scenarios, such as prolonged periods of monotonous tasks or sustained mental effort without adequate breaks (Wascher et al., 2014). The divergent findings regarding EEG wave amplitude and mental fatigue highlight the complex nature of brain activity and its response to cognitive load. For instance, research by Wascher et al. (2014) demonstrated that prolonged cognitive tasks lead to increased theta and alpha synchronization, indicating mental fatigue and reduced attentional resources. Conversely, studies focusing on tasks requiring sustained attention and high cognitive demand, such as those by Wang et al. (2018) found that theta oscillations in frontal component increase and alpha oscillations decrease in parietal and occipital components reflecting the cognitive demands and attentional requirements as executing multiple tasks. These observations suggest that the relationship between EEG wave amplitude and mental state is dynamic and context-dependent. Factors such as the nature of the cognitive task, the duration of mental exertion, and individual differences in neural efficiency can all influence whether synchronization or desynchronization is observed. Further research is needed to delineate these factors and provide a more comprehensive understanding of the neural correlates of mental fatigue and cognitive engagement. Additionally, the findings of this study indicate that inducing mental fatigue in healthy subjects did not result in significant alterations in beta power across different brain regions. A review has also reported inconsistent changes in beta activity related to mental fatigue, suggesting that beta activity may be less reliable as a neural biomarker for mental fatigue.

Listening to music has been found to have a restorative effect on brainwave activity associated with mental fatigue. Our findings indicate that after 20-minute music listening session delta wave power showed a notable decrease, suggesting a reduction in mental fatigue. Additionally, both theta and alpha wave power decreased, indicating a recovery of cognitive function and alertness. This effect of music on EEG activity can be attributed to its ability to modulate neural oscillations and enhance relaxation. Music listening has been shown to activate various brain regions, including those involved in emotion regulation and cognitive processing (Koelsch, 2010). The reduction in delta wave power following music listening suggests that music may help alleviate the sluggish neural activity associated with mental fatigue, promoting a more alert state. Moreover, the decreases in theta and alpha wave power further support the role of music in restoring cognitive function. Theta waves are typically associated with drowsiness and light sleep, while alpha waves are linked to relaxation and reduced attention to external stimuli. The observed decreases in these waves suggest that music helps shift brain activity towards a more desynchronized state, which is conducive to higher cognitive performance and reduced fatigue (Lazar et al., 2005). These findings align with previous research demonstrating the beneficial effects of music on cognitive function and mental state. For example, a study by Thoma et al. (2013) found that music listening can reduce stress and enhance cognitive performance by modulating brain activity and inducing a state of relaxation. Additionally, another study by Jiang et al. (2013) reported that music intervention can improve attention and reduce fatigue in individuals performing repetitive tasks.

This study found that participants exhibited a significant decrease in individual iAPF after the onset of fatigue. Following a 20-minute music listening session, their iAPF significantly recovered, whereas the control group showed no significant changes. The recovery of iAPF in the music group suggests that music may play a role in mitigating the effects of mental fatigue and enhancing cognitive function. The iAPF is a measure of the frequency at which alpha power is maximal and is considered a marker of cognitive performance and mental state. A decline in iAPF is often associated with mental fatigue, reduced cognitive efficiency, and decreased vigilance (Grandy et al., 2013). The observed restoration of iAPF after music listening implies that music may help in re-establishing the optimal functioning of neural networks involved in attention and cognitive control. The beneficial effects of music on iAPF can be attributed to its ability to evoke emotional and cognitive responses that facilitate relaxation and reduce stress. Music has been shown to activate brain regions involved in emotional processing, such as the amygdala and the ventromedial prefrontal cortex, which can influence overall brain activity and improve cognitive performance (Koelsch, 2010). Additionally, music listening has been linked to increased dopamine release, which is associated with improved mood and motivation, further contributing to the recovery of cognitive function (Salimpoor et al., 2011). These findings align with previous research demonstrating the positive impact of music on brain function and cognitive performance. For example, a study by Thompson et al. (2001) found that listening to music enhanced spatial-temporal reasoning abilities, suggesting that music can improve certain aspects of cognitive function. Another study by Schäfer et al. (2013) reported that music listening can enhance mood and cognitive performance by inducing a positive emotional state and reducing stress.

To conclude, our study adds to the growing body of evidence supporting the positive impact of music on mental fatigue. The observed changes in delta, theta, and alpha wave power, along with the significant recovery of iAPF following music listening, indicate that music can effectively restore brainwave activity and reduce fatigue. These findings highlight the potential of music as a non-invasive and enjoyable intervention for mitigating the effects of mental fatigue. Moreover, iAPF, theta, and alpha wave power can serve as reliable biomarkers for assessing mental fatigue and the restorative effects of interventions like music.

Limitation of Study

One limitation of this study is that it only observed changes in EEG activity during the resting state and did not examine the effects of music on EEG activity during task performance. This approach was chosen to avoid confounding the effects of music with the neural activity associated with task execution. Observing EEG changes during task performance could introduce ambiguity, making it difficult to discern whether the observed changes in brainwaves are due to the effects of music or the cognitive demands of the task itself. Future research should consider investigating the differential impacts of music on EEG activity during both resting and task states to provide a clearer understanding of the specific contributions of music to neural dynamics and cognitive performance under conditions of mental fatigue.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Author Contribution

J.L. made substantial contributions to the design of the work. J.L. and T.T.H. were responsible for the acquisition, analysis, and interpretation of data. J.L. and Z.G.H. drafted the work.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Acknowledgements

The authors are grateful to all the participants in this study. We acknowledge Shuo Liu and Zhengdao Jia, who helped recruit the participants.

There were no conflicts of interest.

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The Effect of Music on Resistance to Mental Fatigue：Evidence of EEG Power Spectrum

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