Music possesses a unique ability to influence various musical as well as non-music domains, including intelligence (Rauscher et al., 1993; Rideout and Laubach, 1996), attention (Putkinen et al., 2017; Markovic et al., 2017; Jäncke et al., 2015), emotion (Van den Tol et al., 2016), and the Default Mode Network (DMN) (Trost et al., 2012; Wilkins et al., 2014). The DMN is a neural system primarily associated with internally focused cognitive processes, including daydreaming, mind-wandering, self-referential thinking, reminiscing about the past, and planning for the future (Yeshurun et al., 2021). The broad impact of music has led to positive effects on cognitive, motor, emotional, and social functioning in both healthy individuals and those with aging or neurological conditions (Särkämö, 2018). Cognitive functions such as attention and emotion regulation are essential processes for normal human functioning, and music has been used as a supplementary tool to enhance these functions (Rauscher et al., 1993; Rideout and Laubach, 1996; Putkinen et al., 2017; Markovic et al., 2017; Jäncke et al., 2015).

Studies have shown that even brief exposure to music can enhance the spatiotemporal performance (Rauscher et al., 1995; Wilson and Brown, 1997; Rauscher et al., 1993; Rideout and Laubach, 1996) of individuals, often referred to as the Mozart effect. Furthermore, research suggests that musical stimuli (Mammarella et al., 2007) capable of inducing a moderate arousal and pleasant mood in individuals can lead to significant improvements in several cognitive performance (Schellenberg and Hallam, 2005; Schellenberg et al., 2007). Pleasant music stimulates brain regions associated with memory, attention, cognition, and IQ (Verrusio et al., 2015). Recent research (Gupta et al., 2018) suggests that music affects the cognitive system, enhancing brain efficiency through three distinct mechanisms. First, it activates specific regions of the brain in the prefrontal and occipital lobes, which are responsible for IQ and attention. Additionally, music reduces unwanted brain activities, effectively minimizing interference and optimizing cognitive processes.

People generally gravitate toward happy music (Van den Tol et al., 2016) and strive to avoid sadness in their lives. However, paradoxically, they exhibit a strong inclination toward sad music (Taruffi and Koelsch, 2014), particularly during adverse moments' ranging from everyday struggles to relationship difficulties and profound experiences such as the loss of a loved one (Hanser et al., 2016). Research has provided evidence that sad music can evoke a pleasurable experience characterized by a sense of solace (Van den Tol et al., 2016) and profound beauty (Sachs et al., 2015). This feeling is different from real-life sadness (Gupta et al., 2023; Taruffi and Koelsch, 2014; Sachs et al., 2015). The positive effects of listening to sad music on managing difficult circumstances have been extensively studied and well-documented (Van den Tol et al., 2016; Van den Tol and Edwards, 2013; Hanser et al., 2016).

Sad music is frequently sought after by healthy adolescents and young adults as a means of seeking solace (Van den Tol et al., 2016), consolation (Ter Bogt et al., 2017), comfort (Taruffi and Koelsch, 2014), and coping with their emotions (Van den Tol et al., 2016). Listening to sad music during challenging situations consistently serves various self-regulation goals in the cognitive, social, memory retrieval, distraction, mood enhancement, and affect re-experience domains (Van den Tol et al., 2016; Van den Tol and Edwards, 2013). Furthermore, a recent study (Gupta et al., 2023) has shown that listening to sad music after recalling a personal sad event is associated with improved emotion and memory processing, as well as improved alertness. Findings suggest that sad music can have a profound impact on our emotional and cognitive experiences, facilitating the processing and regulation of emotions in challenging situations.

A comparative study (Taruffi et al., 2017) found that happy music had a notable positive impact on meta-awareness, while sad music exhibited a considerable rise in mind-wandering when contrasted with happy music. This is further supported by the heightened centrality observed within the core nodes of the DMN during sad music listening compared to happy music (Taruffi et al., 2017). The DMN has been recognized as the key network associated with mind-wandering (Mason et al., 2007; Kucyi et al., 2013; Andrews-Hanna et al., 2010a,b; Christoff et al., 2009). Numerous other studies have consistently linked the DMN activity to music listening (Trost et al., 2012; Wilkins et al., 2014; Janata, 2009; Ford et al., 2011; Brattico et al., 2011). It should be noted that the experience of wandering of the mind while listening to sad music is different from ordinary wandering of the mind and is characterized by a unique blend of melancholy and pleasure associated with sad music (Gupta et al., 2023; Taruffi and Koelsch, 2014; Sachs et al., 2015) and comprises of spontaneous, self-referential thoughts, emotions, and cognitive processes (Taruffi et al., 2017).

However, the field of music research is confronted with several obstacles, including the lack of a consistent scientific method for delivering musical interventions, the tendency to reduce its effects to surface-level emotional or esthetic experiences, and an incomplete understanding of how the brain functions while engaging with music. To address these issues, detailed and comprehensive studies are essential to reveal the deeper impact of music on cognitive abilities such as attention and emotion regulation. This line of research has the potential to reshape strategies in mental healthcare, educational methodologies, and cognitive therapy, paving the way for innovative and non-intrusive tools to enhance quality of life.

The application of EEG microstates, which represent distinct and non-overlapping topographies (Khanna et al., 2015; Koenig et al., 2002) in recorded electrical signals, has become increasingly popular in the field of electrical neuroimaging. EEG microstates, representing brief instances of coordinated electrical activity in the brain enduring tens of milliseconds, are considered quasi-stable functional states (Michel and Koenig, 2018). One notable advantage of the microstate method is the reliability and comparability of the topographies obtained across different studies (Khanna et al., 2015; Michel and Koenig, 2018), regardless of the number of electrodes used (Zhang et al., 2021), instructions given to participants (such as open or closed eyes) (Zanesco et al., 2021), or the frequency range analyzed (Férat et al., 2022). Importantly, these microstates have demonstrated the potential to function as biomarkers (Schiller et al., 2020) for neuropsychiatric disorders (Soni et al., 2019; Michel and Koenig, 2018), including mood and anxiety disorders, as well as Alzheimer's disease (Al Zoubi et al., 2019; Tait et al., 2020). Recently, it has been applied across a diverse array of studies, encompassing brain resting states (Schiller et al., 2020), neuropsychiatric disorders (Nishida et al., 2013; Soni et al., 2019; Terpou et al., 2022), sleepiness (Cantero et al., 1999), and task-based brain activities (Seitzman et al., 2017; Hu et al., 2023; Gu et al., 2022; Jiang et al., 2024).

Research has consistently identified specific spatiotemporal brain microstates in independent studies, commonly categorized into four distinct classes, A, B, C, and D, based on their unique topological orientations. Map A is characterized by a left-right orientation, Map B by a right-left orientation, Map C by an anterior-posterior orientation, and Map D by a fronto-central maximum. This labeling convention has been widely adopted in various studies (Michel and Koenig, 2018; Hu et al., 2023; Pal et al., 2021; Liu et al., 2021; Pascual-Marqui et al., 2014). Each microstate is associated with specific functions, namely auditory information processing, visual information processing, DMN, and attention (Khanna et al., 2015; Michel and Koenig, 2018; Koenig et al., 2002). A recent review (Tarailis et al., 2023) on the functionality of EEG microstates has additionally associated class A with arousal. The author finds that in addition to visual processing by class B microstate, it plays a key role in scene visualization and self-representation within those scenes (Bréchet et al., 2019). It is frequently observed during tasks involving autobiographical memory (Bréchet et al., 2019). Furthermore, microstate B exhibits a stronger propensity to transition to microstate C (Bréchet et al., 2019), which is linked to the self-experience system. The review further finds that class C relates to mind-wandering specifically to self-reflection and self-referential processes (Bréchet et al., 2019; Custo et al., 2017), while class D is linked to executive functioning, including processes such as working memory and attention (Bréchet et al., 2019; Kim et al., 2021).

Emotional states tend to evolve gradually, whereas EEG signals fluctuate rapidly, leading to significant variability in the features derived from them. Consequently, Chen et al. (2021) propose that examining EEG microstates provides a more nuanced understanding of emotions than conventional EEG analyses. Emotional research has benefited from the successful utilization of microstate analysis (Prete et al., 2022; Chen et al., 2021; Coll et al., 2019), which has the potential to enhance emotion classification (Chen et al., 2021; Shen et al., 2020). The research findings indicate that the four microstates successfully capture the dynamic attributes of emotions (Prete et al., 2022; Hu et al., 2023). However, research investigating the microstates' underpinnings of basic emotions in music (especially audio) is very limited. In addition, to ensure consistency and allow precise neurophysiological interpretations in our current investigation, we chose four microstates that have shown reliability in previous research studies (Prete et al., 2022; Hu et al., 2023; Khanna et al., 2015; Michel and Koenig, 2018; Koenig et al., 2002).

Although microstate topographies are believed to be unrelated to oscillatory activity (Férat et al., 2022) and various approaches (Zulliger et al., 2022), the alpha bands have been identified as the primary driving force behind microstates (Milz et al., 2017). These alpha oscillations can also affect the number of peaks in the global field power (GFP). The periodic nature of EEG microstates is associated with the alpha band rotating phase observed during periods of rest (von Wegner et al., 2021). Multiple studies have demonstrated that the alpha band microstates outperform those of other frequency bands in classifying conditions such as eyes open or eyes closed (Férat et al., 2022), as well as emotions (Shen et al., 2020). A recent EEG microstate study highlighted the efficacy of the alpha band (8–13 Hz) in examining the impact of happy and sad music on the brain (Gupta et al., 2025). Based on this, our investigation focused specifically on the alpha band.

In summary, this study investigates the brain's microstates associated with the fundamental emotions of happiness and sadness within the alpha band. It also seeks to uncover the neural mechanisms underlying the observed cognitive and emotional enhancements during music listening.

As previously discussed, music is known for its ability to influence both emotional states and cognitive functions. Research suggests that listening to happy music can enhance cognitive abilities such as intelligence and attention (Gupta et al., 2018), while sad music often serves as an effective tool for emotional regulation and coping in challenging situations, as well as for improving attention (Gupta et al., 2023).

To achieve the study's objectives, we conducted a comparative microstate analysis across three conditions—baseline (BL), music (MUS), and post-music (PMS)—for each case while participants listened to happy and sad musical stimuli. We hypothesize that happy music will predominantly affect class D microstates, signifying enhanced attention during the experience of pleasant music. In contrast, sad music is expected to influence both class C and class D microstates, which are associated with self-referential processing (DMN) and attention, respectively.

Music has been recognized for its ability to influence emotions and cognitive processes. Happy music has been found to boost intelligence (Rauscher et al., 1993; Rideout and Laubach, 1996) and attention (Putkinen et al., 2017; Markovic et al., 2017; Jäncke et al., 2015), while sad music has been used to regulate emotions and cope with challenging situations (Van den Tol et al., 2016; Van den Tol and Edwards, 2013; Hanser et al., 2016). However, the specific brain microstates associated with these effects have not been fully understood. This study aimed to comparatively investigate the underlying microstates that contribute to the observed effects when listening to happy and sad Indian raga, in relation to BL and PMS conditions.

The first experiment investigated the effect of happy music listening. The subjective assessment shows that the musical stimulus successfully induced a moderate degree of happiness in the participants (Figure 5d). Furthermore, we obtained four microstates that explained the variance (GEV) of 75.5, 77.4, and 74.43 during BL, MUS, and PMS conditions, respectively. These findings are illustrated in Figures 2a, 3. The results revealed that the spatial distribution of these four microstates resembled those of the four classical microstates (Wang et al., 2021; Hu et al., 2023; Pascual-Marqui et al., 2014; Gu et al., 2022), including earlier microstate studies involving musical stimulus (Hu et al., 2023; Gupta et al., 2025).

The analysis of GEV and GFP during the course of happy music listening indicates that class D microstate exhibits significantly higher presence and increased electrical activity than all other microstates (Figures 1b, c). Furthermore, the analysis of GEV and coverage for correlation between class C and class D microstates demonstrates a robust negative association, consistent with previous research (Braboszcz and Delorme, 2011) (Figures 2d, e). It is worth noting that class C microstate is associated with mind-wandering, especially self-referential thoughts and processes, while class D microstate is linked to attention, respectively (Khanna et al., 2015; Michel and Koenig, 2018; Koenig et al., 2002; Tarailis et al., 2023). This suggests that listening to happy music is linked with heightened attention, and when attention is heightened (class D), mind-wandering (including self-referential thoughts and processes) tends to be diminished during the course of listening to happy music. Additionally, we observed a strong positive correlation between the two microstates for GFP, as shown in Figure 2f. This suggests that, while the electrical activity of both microstates exhibits a positive correlation during the course of listening to happy music, there is a negative correlation between their relative presence. The findings align with previous research involving Raga Darbari, which indicated enhanced attention and decreased interference from unwanted noise during the music listening experience (Gupta et al., 2018).

Furthermore, a comparative microstate analysis was conducted across three conditions: BL, MUS, and PSM. The analysis of GEV revealed a significantly greater presence of the class D microstate in the MUS condition than the other conditions (Figure 4c). Both the MUS and PSM conditions also revealed a significantly reduced presence of class C microstate than the BL condition (Figure 4b). The findings show that a happy music listening state is characterized by enhanced attention and diminished mind-wandering. This is further supported by the lower presence of the class B microstate during the MUS condition as compared to BL condition (Figure 4a). Class B microstate has been linked to scenes and self-visualization (Bréchet et al., 2019; Tarailis et al., 2023), thereby indicating that mind-wandering might be reduced upon listening to happy music. However, further studies are needed to ascertain it. The results obtained in our study align with previous research on music (Gupta et al., 2018, 2025). GFP analysis revealed that the music listening condition (MUS) exhibited enhanced electrical activity than the baseline condition for the classes A and D microstates (Figures 5a, b). Increased GFP for class D indicates a more activated attention state, consistent with the above findings. On the other hand, the increase in GFP for class A suggests that auditory processing is enhanced during music listening than the baseline condition. These results align with a previous study that demonstrated an increased level of music awareness during the listening of happy music (Taruffi et al., 2017).

The second experiment focused on the effect of sad music listening. Participants' subjective mood assessments revealed an increased sad state during sad music listening (Figure 10a). Although sadness was experienced in both the SAR state and sad MUS state, the self-regulatory questionnaire indicated a qualitative variation in the nature of this sadness. The questionnaire revealed that the sad musical excerpts facilitated the achievement of various self-regulatory goals, such as re-experiencing past emotions, enhancing mood, and evoking memories (Figure 10b). Additionally, the results indicated that listening to sad music post adverse experience resulted in an overall positive experience (Figure 10c), aligning with findings from previous studies (Van den Tol et al., 2016; Van den Tol and Edwards, 2013; Hanser et al., 2016).

We further obtained four microstates that explained variance (GEV) of 77.95, 77.77, and 76.98 during BL, MUS, and PMS conditions, respectively. These results are depicted in Figures 6A, 7. The results revealed that the spatial distribution of these four microstates resembled the classical four microstates (Wang et al., 2021; Hu et al., 2023; Pascual-Marqui et al., 2014; Gu et al., 2022), including earlier microstate studies involving musical stimulus (Hu et al., 2023; Gupta et al., 2025). During the course of sad music listening, analyses of GEV and GFP indicate that class A microstate exhibits significantly lower presence and decreased electrical activity than all other microstates (Figures 6b, c).

Additionally, we conducted a comparative microstate analysis across three conditions: BL, MUS, and PSM. The GEV analysis showed that the presence of class C microstate during the PMS condition was significantly higher than the BL condition, as shown in Figure 8a. Additionally, the presence of a class D microstate was significantly higher during the MUS condition than the BL and PMS states (Figure 8b).

It is worth noting that the phenomenon of mind-wandering during sad music listening differs from ordinary mind-wandering, and is distinguished by its melancholic yet pleasurable nature (Gupta et al., 2023; Taruffi and Koelsch, 2014; Sachs et al., 2015). It involves the emergence of spontaneous, self-referential thoughts, emotions, and cognitive processes (Gupta et al., 2023; Taruffi and Koelsch, 2014; Sachs et al., 2015). This is also in alignment with the results obtained in the subjective assessment. Thus, the enhanced presence of class C and class D microstates as a result of listening to sad music signifies an enhanced process of mind-wandering, especially self-referential and attention, respectively. These findings are consistent with previous studies (Gupta et al., 2023; Van den Tol and Edwards, 2013; Van den Tol et al., 2016).

Furthermore, the increased presence of class B microstate during sad music listening might indicate the involvement of scene and self-visualization with self-referential thoughts and memories during sad music listening. This aligns with earlier research (Bréchet et al., 2019; Gupta et al., 2023; Van den Tol and Edwards, 2013; Van den Tol et al., 2016); however, more investigations are needed to ascertain the fact.

The duration and GFP analysis show that regardless of the microstate class, the mean duration and GFP of microstates were higher during the MUS condition than the BL and PMS states, as shown in Figures 8c, d. This suggests that during sad music listening, there is a tendency for the brain microstates to persist for a longer duration with enhanced electrical activity.

Furthermore, the occurrence analysis showed that the frequency of occurrence for microstates was lower than other states during the MUS state, suggesting that the music state had a lower occurrence rate for microstates but with longer duration and larger GFP (Figures 9a–d).

Moreover, analyses of GEV and coverage for correlation between class C and class D microstates during the course of listening to sad music reveal a moderate negative correlation. It is important to note that this relationship explains only a small amount of variance in the data, as indicated by low R-squared values (0.3025 and 0.3249), as shown in Figures 6d, e. This suggests that there are other factors and parameters that contribute to the unexplained variance in the data. Future investigations should explore these additional factors and parameters. However, different relationships between class C and class D microstates (for GEV and coverage) during happy and sad music are consistent with earlier studies and likely highlight the differences in the nature of mind-wandering (self-referential) process (class C microstates) between them (Taruffi et al., 2017). We also observed a strong positive correlation between the two microstates (class C and class D) in terms of GFP, as shown in Figure 6f. This suggests that the electrical activity of both microstates is enhanced during the course of listening to sad music and is in line with the happy music analysis.

In summary (Figure 11), the present study underscores the impact of happy and sad music on various mental processes, particularly in modulating brain microstates. The key findings indicate that listening to music leads to longer microstate duration and improved attention. Furthermore, happy music specifically reduces mind-wandering, fostering sustained focus, whereas sad music enhances self-referential processing, aiding in self-regulation during emotionally challenging situations.

This study's findings pave the way for personalized music therapy, cognitive training, and mental health interventions for conditions such as ADHD, depression, and anxiety. Music's impact on attention and self-regulation can enhance workplace productivity, education, and rehabilitation. Additionally, AI-driven adaptive music systems could tailor recommendations based on cognitive states. These insights have broad applications in healthcare, technology, and performance enhancement.

While this study offers valuable insights, several limitations warrant further exploration. First, it focused on specific music genres, and incorporating a wider range of musical styles could deepen our understanding of music-induced brain dynamics. Additionally, the study was limited to male participants, underscoring the need for future research to examine potential gender differences. Furthermore, it did not consider how varying intensities of happiness and sadness influence microstates, particularly classes C and D. The lack of real-time subjective assessments of attention and mind-wandering also restricts insights into moment-to-moment cognitive fluctuations during music listening.

Future research utilizing a dense montage system with 64+ electrodes and source localization analysis could provide a more precise understanding of the neural mechanisms underlying microstate changes, particularly in differentiating self-referential processes during sad music listening. Age can be a crucial factor, as it may influence both neural processing and microstate dynamics. Thus, the current findings need to be validated across different age groups. Furthermore, Studies 1 and 2 were conducted on separate sample groups. Future research using the same sample group for both musical stimuli would allow for a more detailed comparative analysis of microstate features specific to happy and sad music listening. Addressing these gaps will contribute to a more comprehensive understanding of music's effects on brain function and its therapeutic applications.