A comparative study of EEG microstate dynamics during happy and sad music videos

Abstract

EEG microstates offer a unique window into the dynamics of emotional experiences. This study delved into the emotional responses of happiness and sadness triggered by music videos, employing microstate analysis and eLoreta source-level investigation in the alpha band. The results of the microstate analysis showed that regardless of gender, participants during happy music video significantly upregulated class D microstate and downregulated class C microstate, leading to a significantly enhanced global explained variance (GEV), coverage, occurrence, duration, and global field power (GFP) for class D. Conversely, sad music video had the opposite effect. The eLoreta study revealed that during the happy state, there was enhanced CSD in the central parietal regions across both genders and diminished functional connectivity in the precuneus for female participants compared to the sad state. Class D and class C microstates are linked to attention and mind-wandering, respectively. The findings suggest that (1) increased class D and CSD activity could explain heightened attentiveness observed during happy music, and (2) increased class C activity and functional connectivity could explain enhanced mind wandering observed during sad music. Additionally, female participants exhibited significantly higher mean occurrence than males, and the sad state showed significantly higher mean occurrence than the happy state.

Full text

TYPE Original Research
PUBLISHED 06 February 2025
DOI 10.3389/fnhum.2024.1469468
OPEN ACCESS
EDITED BY
Changming Wang,
Capital Medical University, China
REVIEWED BY
Jianghai Ruan,
The Aliated Hospital of Southwest Medical
University, China
Zhikai Yu,
Capital Medical University, China
*CORRESPONDENCE
Laxmidhar Behera
lbehera@iitk.ac.in
†PRESENT ADDRESS
Ashish Gupta,
Brainwave Science, Inc., Southborough, MA,
United States
RECEIVED 23 July 2024
ACCEPTED 23 December 2024
PUBLISHED 06 February 2025
CITATION
Gupta A, Srivastava CK, Bhushan B and
Behera L (2025) A comparative study of EEG
microstate dynamics during happy and sad
music videos.
Front. Hum. Neurosci. 18:1469468.
doi: 10.3389/fnhum.2024.1469468
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©2025 Gupta, Srivastava, Bhushan and
Behera. This is an open-access article
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which does not comply with these terms.
A comparative study of EEG
microstate dynamics during
happy and sad music videos
Ashish Gupta1†, Chandan Kumar Srivastava2, Braj Bhushan3and
Laxmidhar Behera1,4*
1Department of Electrical Engineering, Indian Institute of Technology, Kanpur, India, 2Department of
Humanities and Social Sciences, Indian Institute of Technology, Bombay, India, 3Department of
Humanities and Social Sciences, Indian Institute of Technology, Kanpur, India, 4School of Computing
and Electrical Engineering, Indian Institute of Technology, Mandi, India
EEG microstates oer a unique window into the dynamics of emotional
experiences. This study delved into the emotional responses of happiness and
sadness triggered by music videos, employing microstate analysis and eLoreta
source-level investigation in the alpha band. The results of the microstate
analysis showed that regardless of gender, participants during happy music
video significantly upregulated class D microstate and downregulated class
C microstate, leading to a significantly enhanced global explained variance
(GEV), coverage, occurrence, duration, and global field power (GFP) for class
D. Conversely, sad music video had the opposite eect. The eLoreta study
revealed that during the happy state, there was enhanced CSD in the central
parietal regions across both genders and diminished functional connectivity in
the precuneus for female participants compared to the sad state. Class D and
class C microstates are linked to attention and mind-wandering, respectively.
The findings suggest that (1) increased class D and CSD activity could explain
heightened attentiveness observed during happy music, and (2) increased
class C activity and functional connectivity could explain enhanced mind
wandering observed during sad music. Additionally, female participants exhibited
significantly higher mean occurrence than males, and the sad state showed
significantly higher mean occurrence than the happy state.
KEYWORDS
EEG microstate, emotion, music, attention, mind wandering
1 Introduction
MUSIC is evolutionary linked to human brains (Cross and Morley, 2008) in as much as
humans can readily recognize basic emotions such as happiness and sadness (Brattico et al.,
2011). Apart from improving one’s mood, music has been utilized to achieve various self-
regulatory objectives. Listening to happy music is linked to improved cognitive functions
such as attention (Gupta et al., 2018;Putkinen et al., 2017) and spatial-temporal abilities,
sometimes referred to as the “Mozart effect” (Gupta et al., 2018;Putkinen et al., 2017;
Rauscher et al., 1995;Wilson and Brown, 1997). Conversely, sad music aids in emotional
processing and introspection, offering comfort and fostering emotional resilience during
challenging times (Van den Tol et al., 2016;Van den Tol and Edwards, 2013).
However, music research faces challenges, including the lack of a scientifically
standardized approach to music administration, the reduction of music’s effects to
superficial aesthetic or mood-related features, and limited understanding of the brain’s
dynamic during music listening. Addressing these challenges requires precise analyses
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to fully explore music’s impact on cognitive domains such as
attention and intelligence. This research has the potential to
transform approaches to mental health, education, and cognitive
rehabilitation, promoting wellbeing through accessible, non-
invasive methods.
Attention is a fundamental cognitive function that enables us
to selectively focus on specific stimuli, tasks, or thoughts while
filtering out irrelevant information (Callan et al., 2023). Research
indicates that attention is shaped by prior context (Mugruza-
Vassallo et al., 2021) and the emotional significance of stimuli or
events (Bröckelmann et al., 2011). The early auditory processing,
in turn, is modulated by attention (Karns and Knight, 2009).
Studies show that even brief exposure to happy music can
activate brain regions linked to memory, attention, and IQ, while
also minimizing unnecessary brain activity, leading to optimized
cognitive efficiency. Similarly, sad music aids in the achievement
of various self-regulation goals in the domains of cognition, social,
memory retrieval, friend, distraction, mood enhancement, and re-
experience affect (Van den Tol and Edwards, 2013;Van den Tol
et al., 2016) ultimately leading to better emotional and memory
processing, especially during difficult situations (Gupta et al., 2023).
Taruffi et al. (2017), who specifically explored the impact of
happy and sad music on mind wandering and meta-awareness,
found that happy music significantly enhanced meta-awareness
compared to sad music, whereas sad music led to a significant
increase in mind wandering compared to happy music. However,
the mind-wandering experience while listening to sad music is
distinct from that of ordinary sadness and is uniquely characterized
by the melancholic yet pleasurable nature of sad music (Gupta et al.,
2023;Taruffi and Koelsch, 2014;Sachs et al., 2015).
The brain associations of basic emotions of happiness and
sadness in music have been explored in only a limited number
of studies. One of the initial studies (Khalfa et al., 2005), using
functional magnetic resonance imaging (fMRI), found that sad
music stimulated the left medial frontal gyrus and the adjacent
superior frontal gyrus, more as compared to happy music. These
brain regions are linked to emotional experiences, self-reflection,
and self-evaluation (Jacobsen et al., 2006;Kornysheva et al., 2010).
fMRI maps brain activity by detecting blood flow changes tied to
neural activity. It provides high spatial resolution, helping identify
brain regions involved in cognition and emotion, although its
temporal resolution is limited (Varvatsoulias, 2013).
fMRI studies have also shown that compared to neutral
composition, happy music activates several brain regions such
as the superior frontal gyrus, anterior cingulate cortex, posterior
cingulate gyrus, parahippocampal gyrus, medial frontal gyrus,
and precuneus (Mitterschiffthaler et al., 2007), while sad music
activates brain regions such as the hippocampus/amygdala,
posterior cingulate gyrus, medial frontal gyrus, and cerebellum
(Mitterschiffthaler et al., 2007). However, there is need to
investigate brain activity particularly during basic primary emotion
of happiness and sadness evoked by music using EEG especially in
connection to cognitive and emotion processing.
Understanding how the brain processes information has led
to extensive research on large-scale resting-state brain networks,
focusing on their spatial structure and temporal dynamics. A
key method in this research is the analysis of EEG microstates,
which represent snapshots of the brain’s global neuronal activity.
It represent episodes of synchronized electrical activity in the
brain that last for tens of milliseconds (Michel and Koenig, 2018)
and illustrate how specific spatial and temporal configurations of
neuronal activity align with mental processes or the resting state of
the brain (Michel and Koenig, 2018;Lehmann and Michel, 2011).
Further investigations have found consistent and specific
spatio-temporal brain microstates across independent studies
(Khanna et al., 2015;Michel and Koenig, 2018), making them
potential markers of neural traits (Schiller et al., 2020). These
functional microstates are usually identified as four prototypical
microstates termed class A, class B, class C, and class D and are
known for auditory processing, visual processing, default mode
network (DMN), and attention respectively (Khanna et al., 2015;
Michel and Koenig, 2018;Koenig et al., 2002). Studies have shown
that disruptions in cognitive processes related to psychiatric and
neurological disorders are linked to changes in the temporal
dynamics of these microstates (Soni et al., 2019;Michel and Koenig,
2018).
Microstate analysis has been used in a wide range of studies,
including resting state of the brain (Schiller et al., 2020),
neuropsychiatric diseases (Nishida et al., 2013), sleepiness (Cantero
et al., 1999), gender differences (Tomescu et al., 2018), and tasks-
based brain activities (Seitzman et al., 2017;Hu et al., 2023).
Unlike emotional states, which change gradually over time,
EEG signals are unsteady and change rapidly, resulting in highly
variable extracted features. As a result, Chen et al. (2021) argue that
analyzing EEG microstates can offer deeper insight into emotional
research than traditional EEG analysis and better capture the
spatial-temporal characteristics of spontaneous brain activity under
varying emotional states. Indeed, microstate analysis has been used
successfully in emotional research (Prete et al., 2022;Chen et al.,
2021;Coll et al., 2019) and has the potential to improve emotion
classification (Chen et al., 2021;Shen et al., 2020). Studies reveal
that the four EEG microstates are proficient in capturing the
dynamic features of emotions (Prete et al., 2022;Hu et al., 2023).
A recent review has shown it as an effective tool for
investigating socio-affective states (Schiller et al., 2024) and
emotional processing (Schiller et al., 2024), providing a dynamic
whole-brain representation of distinct emotions (Liu et al., 2023).
Specifically, research investigating the impact of music on the brain
microstate shows improved microstates related to speech, vision,
and attention processing (Jiang and Zheng, 2024) in participants
who are trained in music as compared to untrained participants.
Microstate analysis has also advanced our understanding of the
neural mechanisms underlying the effectiveness of music therapy
for tinnitus (Zhu and Gong, 2023). Furthermore, happy music can
modify brain microstates, leading to positive effects on cognitive
reappraisal (Hua and Li, 2023).
In this current investigation, we used the widely recognized
DEAP database (Koelstra et al., 2011), specifically designed for
emotion analysis using physiological signals. A recent microstate
analysis of the DEAP dataset highlighted the effectiveness of
alpha band microstates in accounting for variances across all
EEG time frames, surpassing other frequency bands (Shen et al.,
2020). Remarkably, the microstate topologies within the alpha band
closely resembled the four maps previously identified more than
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those in other bands (Shen et al., 2020). Additionally, the concept
of microstates was first applied to alpha oscillations in the 1987
(Lehmann et al., 1987), and recent studies have confirmed that
alpha-band activity is the prominent driver of microstates (Milz
et al., 2017). Several other studies have also shown the alpha band
to play vital roles in cognitive functions during music listening (Wu
et al., 2012;Flores-Gutiérrez et al., 2009;Gupta et al., 2018,2023).
Therefore, in our current analysis of the DEAP dataset, we focused
our investigation on the alpha band. This is also in line with the
earlier microstate studies (Gu et al., 2022;Das et al., 2024).
A prior investigation using the DEAP database identified four
as the optimal cluster number (Hu et al., 2023). In our current
analysis, we used the same DEAP dataset. Consequently, we
selected four microstates for our study. Four microstates are the
most consistent observed and studied across different research
studies and provide clear neurophysiological interpretations linked
to various human cognitive functions. This is in line with earlier
studies (Al Zoubi et al., 2019;da Cruz et al., 2020;Koenig et al.,
2002).
In prior EEG, studies delving into neural signatures for
emotions, particularly in the alpha band, have demonstrated an
increase in EEG power, in the central-parietal regions during
passive listening to music (Markovic et al., 2017;Jäncke et al., 2015).
This phenomenon is linked to heightened attentiveness (Markovic
et al., 2017;Jäncke et al., 2015) with the results also indicating
a positive correlation with valence (Koelstra et al., 2011). Studies
focusing on internal tasks such as self-referential process (Knyazev,
2013), meditation (Aftanas and Golocheikine, 2001), and music
listening (Markovic et al., 2017;Jäncke et al., 2015) had shown
that alpha band oscillations (power) to be directly proportional
to cortical activity within the task relevant area.The examination
of functional connectivity in brain networks revealed heightened
connectivity, particularly in the alpha band, during music listening
(Wu et al., 2012;Flores-Gutiérrez et al., 2009;Gupta et al., 2023).
Gender is an important factor to consider while studying the
brain’s response to basic emotions (Stevens and Hamann, 2012),
and in general, females had a greater brain activity than males
(Goshvarpour and Goshvarpour, 2019). In this study, we also aim
to investigate the role of gender differences in processing musical
stimuli while also accounting for valence as contributing factor.
Thus, the current study investigates the brain microstates
underlying basic emotions of happiness and sadness in the alpha
band for male and female participants. As discussed earlier, Taruffi
et al. (2017) found that relatively happy music significantly boosts
meta-awareness more than sad music, while sad music increases
mind wandering more than happy music. These findings remained
consistent across multiple experiments investigating the effects
of happy (Gupta et al., 2018;Putkinen et al., 2017) and sad
music (Gupta et al., 2023;Taruffi and Koelsch, 2014;Sachs et al.,
2015). The DMN has been identified as the primary network
involved in mind-wandering (Mason et al., 2007;Kucyi et al., 2013).
Consequently, we hypothesize that sad music would influence the
class C microstate, associated with DMN activity, while happy
music would affect the class D microstate, linked to attention.
We performed source reconstruction analysis through eLoreata
to further investigate the brain regions underpinning emotional
experience and expect enhanced brain activity during listening to
happy music compared to sad one.
2 Methods
2.1 Procedure and EEG data
The study utilized the DEAP dataset (Koelstra et al., 2011),
which is an open-source EEG dataset consisting of recordings
from 32 participants (17 male) with mean age of 27.18 (SD =
4.44) listening to 40 musical videos, each lasting 1 min. Data
were recorded at two locations: Participants 1–22 in Twente and
23–32 in Geneva. The DEAP database utilized music-video clips
to evoke emotional responses in subjects. Before commencing
the emotional experiment, a 2-min baseline recording was taken.
During this time, subjects were instructed to relax, while a fixation
cross was displayed. Subsequently, 40 videos were presented across
40 trials. The musical clip presentations were randomized for
each participant. Each trial began with the display of the trial
number for 2 s, indicating the subject’s progress, followed by a 5-
s fixation cross. Then, the music video was shown for 1 min, after
which the subject completed a self-assessment. A brief break was
provided after the 20th trial, during which volunteers were offered
non-caffeinated and non-alcoholic beverages and cookies, and the
examiner checked the signal quality and electrode placement. The
second half of the experiment was then conducted. Participants
rated their experience on valence, arousal, dominance, liking, and
familiarity scales from 1 to 9. EEG was recorded from 32 channels
based on the standard 10–20 system of electrode placement, with
a sampling frequency of 512 Hz. Further details can be found in
Koelstra et al. (2011).
The musical stimuli were selected based on ratings for arousal,
valence, dominance, and the Genova emotion scale (Koelstra et al.,
2011). In the current study, we only selected stimuli which had
significantly expressed the respective emotions of happiness and
sadness, based upon the Genova scale rating. We identified one
music video, with the ID number 11, that received significant
ratings for happiness, and another one, with the ID number 30, that
was significantly rated for sadness (see Supplementary Figures S1,
S2). Therefore, these specific videos were chosen for the current
investigation. Other music videos did not exhibit significant
expressions of happiness or sadness.
Koelstra et al.’s work concentrated on traditional EEG power
analysis of scalp potentials, primarily examining valence and
arousal within a dimensional framework. The present study
expands this investigation by incorporating (1) global neural
activity assessment through microstate analysis, (2) source-level
analysis using eLoreta, and (3) evaluation of music videos based on
discrete emotion theory.
2.2 EEG pre-processing
The EEG data were down-sampled to 256 Hz and visually
checked for artifacts. Bad electrodes were marked and interpolated.
The EEG data were re-referenced to average reference in line with
earlier studies (Gupta et al., 2023;Goshvarpour and Goshvarpour,
2019;Koelstra et al., 2011;Hu et al., 2023). To further remove eye
and muscle movement artifacts, independent component analysis
(ICA) and SASICA were employed after rank adjustment. The
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EEGLAB toolbox was utilized for implementing ICA and SASICA,
which have proven effectiveness in eliminating artifacts associated
with eyes and muscle movements (Sburlea et al., 2021;Khosravani
et al., 2019). EEG data were filtered between 8 and 13 Hz to
obtain the alpha band. We analyzed the EEG data under four
conditions: (1) Female during listening to happy music (FH), (2)
Female during listening to sad music (FS), (3) Male during listening
to happy music (MH), and (4) Male during the listening of sad
music (MS).
2.3 Microstate analysis
A spatial k-means cluster analysis, as implemented in the
EEGLAB toolbox (Poulsen et al., 2018), was applied separately
for FH, FS, MH, and MS conditions. The cluster analysis was
performed using maps at the local maxima of the global field
power (GFP), which represents the time points with the highest
signal to noise. The polarity of the maps was not considered.
Microstate cluster analysis was performed on the concatenated
EEG data of the participants under each condition. We extracted
four microstates for each condition (Bréchet et al., 2020;da Cruz
et al., 2020;Tait et al., 2020). Koenig et al. (1999) categorized four
microstate maps of the brain into classes A, B, C, and D based
upon the topological orientation of the map. Specifically, microstate
map A displays a left-right orientation, map B exhibits a right-left
orientation, map C demonstrates an anterior-posterior orientation,
and map D reveals a fronto-central maximum. Subsequent studies
have consistently maintained this labeling convention (Michel and
Koenig, 2018) (Supplementary Figures S3–S5). We categorized the
acquired microstates in our study as classes A, B, C, and D
based on their topographical orientation, as outlined by Koenig
et al. (1999), in line with earlier studies (Hu et al., 2023;
Pal et al., 2021;Liu et al., 2021;Pascual-Marqui et al., 2014).
Furthermore, we calculated the spatial correlation among the
four microstates of the brain under the four conditions. After
identifying the maps for each condition, the maps were fitted back
to the EEG data of each participant under each condition. Each
time frame was assigned to templates that best fit the data in
terms of spatial correlation. This process resulted in a microstate
sequence for each participant, which was then used to calculate
the microstate parameters specific to each participant for each
condition.
(1) GEV: It is a parameter that measures how well the chosen
template maps describe the entire dataset.
(2) Coverage: Coverage of microstates indicates the percentage of
the specified microstates in the total recorded time.
(3) Occurrence: Frequency of occurrence measures the average
number of times the microstate occurs per second.
(4) GFP: Global field power is a measure of the strength of the
electric field generated by the brain at any instant of time.
(5) Transition probability: Transition probability between
different microstates is the likelihood of transitioning from the
current microstate to another state.
(6) Duration: It refers to the average length of time a specific
microstate remains dominant.
2.4 EEG source analysis
eLoreta is a source localization method that uses a weighted
minimal norm inverse technique to perform three-dimensional
source localization (Pascual-Marqui et al., 2011). It offers exact
localization (zero localization error) using a discrete, distributed,
and linear approach and was used to analyze the EEG data in this
study. EEG current source density (CSD) refers to the estimation
of the electrical current flow within the cortex, based on scalp
EEG recordings. CSD provides a measure of the intensity and
distribution of active neural sources by calculating the spatial
second derivative of the EEG potential. CSD was computed
at 6,239 voxels, with a sampling resolution of 5 mm, using
eLoreta software.The study used functional connectivity as a
tool to investigate how brain regions synchronize to accomplish
tasks. Lag phase synchronization was selected as the metric of
interest as it measures non-linear functional connectivity while
accounting for factors such as power fluctuations, instantaneous
zero lagged components, and volume conduction. This choice
was made to ensure resistance to non-physiological artifacts
and enhance the validity of the findings (Pascual-Marqui
et al., 2011). Brain connectivity between all pairs of the
standard 68 regions of interest (ROI) defined by the Desikan-
Killiany atlas was computed at the source level using eLoreta
(Supplementary Table S1).
2.5 Statistical analysis
To analyze the data, we utilized a two-tailed t-test with a
significance level (α) of 0.05 for comparing mean values and
subjective questionnaires. To examine the influence of gender,
microstates, and stimulus type on parameters such as GEV,
occurrence, GFP, duration, and coverage, a three-way analysis
of variance (ANOVA) was conducted using SPSS software. In
this analysis, gender was treated as a between-subjects factor,
meaning it varied across different participants, allowing us to
assess if there are differences in these parameters between male
and female participants. Meanwhile, microstates and stimulus
type were included as within-subjects factors as each participant
experienced different microstates and stimulus conditions. This
three-way ANOVA enabled us to determine not only the main
effects of each factor (gender, microstates, and stimulus type) on
the parameters but also any interaction effects between them,
showing how combinations of these factors may influence the
outcomes in complex ways. To account for multiple testing
across microstates and stimuli, we applied false discovery rate
(FDR) correction.
eLoreta source-level data analysis at the each voxel presents
issues with multiple testing. To address this, eLoreta uses the
non-parametric SnPM method, performing 5,000 randomizations
to establish accurate probability thresholds, correcting for
multiple comparisons without relying on normal distribution
assumptions. SnPM is implemented in the eLoreta statistical
package (Holmes et al., 1996). SnPM has been widely validated,
enhancing reliability in EEG source analysis (Pascual-Marqui et al.,
1999).
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3 Results
3.1 Specific microstate maps for each state
We obtained four microstates maps explaining together 69.97,
69.46, 68.27, and 70.30 percent GEV for MH, MS, FH, and FS states,
respectively.
Figure 1 shows the four microstate maps under each condition
categorized as per standard convention into the four Class A,
B, C, and D based upon the highest spatial correlation and
visual inspection (Hu et al., 2023;Pascual-Marqui et al., 2014).
Figure 1 shows high spatial correlations among the different
conditions for the corresponding microstate category A-D (p<
0.0001). This confirms that the respective microstates across the
four conditions for each class are consistently aligned among
themselves, representing the same microstate type.
3.2 Microstate parameters
The microstate maps were fitted back into the EEG data of
the participants under each condition to obtain several parameters
such as GEV, coverage, occurrence, duration, and inter-microstate
transition probability.
(1) GEV analysis: We administered a three-way ANOVA
with gender as in between factor, stimulus, and microstate
as within factor. The results show no significant three-way
interaction. We obtained a significant two-way interaction between
stimulus and microstate with a Greenhouse-Geisser correction
(F1.522,45.662 =13.438, p<0.001). To examine the simple
effect of microstates, a one-way repeated measures ANOVA was
conducted. Findings revealed a significant effect of microstates on
GEV for happy stimulus with a Greenhouse-Geisser correction
(F2.177,67.492 =15.440, p<0.001). We did not obtain any significant
effect of microstate for sad stimulus through one-way repeated
measure ANOVA. Further post-hoc pairwise comparison with FDR
correction revealed that regardless of gender, class D state to be
significantly higher than class C (t =5.0036, df =31, p<0.0001,
effect size =0.9201), class B (t =5.6165, df =31, p<0.0001, effect
size =0.7294), and class A (t =3.1354, df= 31, p<0.005, effect
size =0.3290) during the happy stimulus. We also found class C
state to be significantly reduced GEV than class A (t = −2.2977, df
=31, p<0.05, effect size = −0.8899) as shown in Figure 2A. We
obtained microstate class C and class D during the sad stimulus to
be significantly higher and lower than the class C (t =3.1266, df =
31, p<0.005, effect size =0.5527) and class D (t = −4.9658, df =
31, p<0.001, effect size = −0.8778), respectively, during the happy
stimulus as shown in Figure 2B. We also observed microstate class
B during the sad stimulus to be significantly higher than the class
B (t =2.3415, df =31, p<0.05, effect size =0.4139) during the
happy stimulus.
(2) Coverage analysis: A three-way ANOVA with gender
as in between factor, stimulus, and microstate as within factor
was administered. The results show no significant three-way
interaction. We obtained a significant two-way interaction between
stimulus and microstate with a Greenhouse-Geisser correction
(F1.441,43.225 = 12.609, p=0.001). Further one-way repeated
measure ANOVA was conducted to examine the simple effect of
microstates. Findings revealed a significant effect of microstates on
coverage for happy stimulus with a Greenhouse-Geisser correction
(F2.125,65.886 =12.974, p<0.001).
We also obtained a significant two-way interaction between
gender and microstate with a Greenhouse-Geisser correction
(F1.441,43.225 =12.609, p=0.001). Further one-way repeated
measure ANOVA was done to investigate the simple effect of
microstates. Findings revealed a significant effect of microstates
on coverage for happy stimulus for female participants with a
Hyunh-Feldt correction(F2.751,38.508 =14.767, p<001).
Post-hoc pairwise comparison with FDR correction revealed
that regardless of gender, class D state to be significantly higher
than class C (t =5.3447, df =31, p<0.001, effect size =0.9448)
and class B (t =5.2380, df =31, p<0.001, effect size =0.9260)
during the happy stimulus. We also found class C state to be
significantly reduced coverage than class A (t = −3.5234, df= 31,
p<0.005, effect size = −0.6229) as shown in Figure 2C. We
obtained microstate class C and class D during the sad stimulus to
be significantly higher and lower than the class C (t =3.6066, df =
31, p<0.005, effect size =0.6376) and the class D (t = −4.0892,
df= 31, p≤0.001, effect size = −0.7229), respectively, during the
happy stimulus as shown in Figure 2D.
Pairwise comparison further showed that regardless of
stimulus, class D was significantly higher than class A (t =3.6063,
df =14, p≤0.001, effect size =0.9311), class B (t =5.2938, df
=14, p≤0.001, effect size =1.3669), and class C (t =4.9116,
df =14, p≤0.001, effect size =1.2682) for female participants
as shown in Figure 2D. We obtained class B to be significantly
reduced that class A (t = −4.5118, df =31, p≤0.001, effect size
= −1.1650). Two sample t-test further showed that Class D during
female participants was significantly enhanced compared to class D
(t = −4.0892, df =31, p≤0.001, effect size = −0.7229) during
male participants as shown in Figure 2E.
(3) Occurrence analysis: A three-way ANOVA with gender
as in between factor, stimulus, and microstate as within factor
was administered. The results show no significant three-way
interaction. We obtained a significant two-way interaction between
stimulus and microstate with a Greenhouse-Geisser correction
(F1.748,52.451 =13.9, p<0.001).To examine the simple effect of
microstates, a one-way repeated measures ANOVA was conducted.
Findings revealed a significant effect of microstates on frequency
of occurrence for happy stimulus with (F3,93 =16.089, p<
0.001). Further post-hoc pairwise comparison with FDR correction
revealed class D state to be significantly higher than class C (t =
5.2046, df =31, p<0.001, effect size =0.9201) and class B (t =
4.1262, df =31, p<0.001, effect size =0.7294) during the happy
stimulus. We also found the class C state to be significantly reduced
than class A (t = −5.0341, df =31, p<0.001, effect size = −0.8899)
and class B (t = −3.2324, df =31, p<0.05, effect size = −0.5714),
as shown in Figure 3A. We also obtained microstate class C and
class D during the sad stimulus to be significantly higher and lower
than the class C (t =4.9191, df =31, p<0.001, effect size =0.8696)
and class D (t = −2.4689, df =31, p<0.05, effect size = −0.4365),
respectively, during the happy stimulus as shown in Figure 3B.
(4) GFP analysis: A three-way ANOVA with gender as
in between factor, stimulus, and microstate as within factor
was administered. The results show no significant three-way
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FIGURE 1
Microstate maps. Four EEG microstates under FS, FH, MS, and MH conditions. Spatial correlation between the corresponding microstate class across
conditions.
FIGURE 2
Microstate parameters. (A) Relative GEV of microstates during Happy stimulus across gender. (B) Relative GEV in each microstate during Happy and
Sad stimulus across gender. (C) Relative coverage of microstates during Happy stimulus across gender. (D) Relative coverage in each microstate
during Happy and Sad stimulus across gender. (E) Relative coverage of microstates during female participants across stimulus. (F) Relative coverage
of microstates for female and male participants across stimulus (**FDR corrected, p<0.05, error bars =1 SD).
interaction. We obtained a significant two-way interaction between
stimulus and microstate with a Huynh-Feldt correction (F2.674,80.216
=0.431, p<0.05). One-way repeated measure ANOVA was done
to investigate the simple effect of microstates. Findings revealed a
significant effect of microstates on GFP for happy stimulus with
(F3,93 =5.163, p<0.010). Further post-hoc pairwise comparison
with FDR correction revealed class D state to be significantly higher
than class B (t =3.3277, df =31, p<0.05, effect size =0.5883) and
class A (t =2.5716, df =31, p<0.05, effect size =0.4546) during
the happy stimulus (Figure 3C). We also obtained microstate class
A and class D during the sad stimulus to be significantly lower than
the class A (-t =2.7627, df =31, p<0.05, effect size = −0.4884)
and class D (t = −5.0481, df =31, p<0.001, effect size = −0.8924),
respectively, during the happy stimulus as shown in Figure 3D.
(5) Duration Analysis: A three-way ANOVA with gender
as in between factor, stimulus, and microstate as within factor
was administered. The results show no significant three-way
interaction. We obtained a significant two-way interaction between
stimulus and microstate with a Greenhouse-Geisser correction
(F1.638,49.135 =8.384, p =0.001). To examine the simple effect of
microstates, a one-way repeated measures ANOVA was conducted.
The findings revealed a significant effect of microstates on
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FIGURE 3
Microstate parameters. (A) Relative frequency of occurrence of microstates during Happy stimulus across gender. (B) Relative frequency of
occurrence in each microstate during Happy and Sad stimulus across gender. (C) Relative GFP of microstates during Happy stimulus across gender.
(D) Relative GFP in each microstate during Happy and Sad stimulus across gender. (E) Relative duration of microstates during Happy stimulus across
gender. (F) Relative duration in each microstate during Happy and Sad stimulus across gender (**FDR corrected, p<0.05; error bars =1 SD).
duration for happy stimulus with a Greenhouse-Geisser correction
(F2.130,66.040 =8.746, p<0.001). Further post-hoc pairwise
comparison with FDR correction revealed class D state to be
significantly higher than class C (t =4.4648, df =31, p<0.001,
effect size =0.7893) and class B (t =5.2560, df =31, p<0.001,
effect size =0.9291) during the happy stimulus. We also found the
class C state to be significantly reduced than class A (t = −2.4875,
df =31, p<0.05, effect size = −0.4397), as shown in Figure 3E. We
also obtained microstate class C and class D during the sad stimulus
to be significantly higher and lower than the class C (t =2.4471, df
=31, p<0.05, effect size =0.4326) and class D (t = −4.0303, df =
31, p<0.005, effect size = −0.7125), respectively, during the happy
stimulus as shown in Figure 3F.
(7) Transition probability: Transition probability between the
class C and class D microstates for all the four conditions was
further analyzed. A three-way ANOVA with gender as in between
factor, stimulus, and microstate as within factor was administered.
The results show no significant three-way interaction. We obtained
a significant two-way interaction between stimulus and microstate
for transition probability (F11,330 =6.637, p<0.001). Further
post-hoc pairwise comparison with FDR correction revealed a
significant enhancement for class C to class D transition compared
to class D to class C (t =5.4284, df =31, p<0.0001,
effect size =0.9596) during happy stimulus (Figures 4A,B).
We also found a significant class D to class C transition
during the sad stimulus compared to the happy stimulus (t =
3.7808, df =31, p<0.0001, effect size =0.6684), as shown
in Figure 4C.
(8) Mean occurrence analysis: We applied mixed ANOVA to
study the effect of gender and musical stimulus on the mean
frequency of occurrence and did not find a significant interaction
effect between gender and musical stimulus. However, the main
effect of gender (F1,30 =5.924, p =0.021) and the stimulus
(F1,30 =4.155, p=0.05) was statistically significant as shown
in Figures 4D,E.
(9) Mean GFP: We applied mixed ANOVA to study the effect
of gender and musical stimulus on the mean GFP and found a
significant interaction effect between gender and musical stimulus
with (F1,30 =4.786, p=0.037). Further post-hoc analysis revealed
male during happy stimulus had significantly enhanced mean GFP
than during sad stimulus (t =3.3941, df =17, p<0.005, effect size
=0.8232) as shown in Figure 4F.Figure 5 presents a word cloud
depicting the brain microstate features of classes C and D during
happy and sad music listening.
3.3 CSD and functional connectivity
analysis
We calculated CSD for 6,239 voxels of the brain and found
significant differences across stimulus conditions, as shown in
Figure 6.Figure 6A shows several brain regions (67) to have
significant higher brain activity across the MH condition as
compared to the MS condition, with t-values ranging from 4.236
to 4.712 at p<0.05 and effect size ranging from 1.0274 to
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FIGURE 4
Microstate parameters. (A) The relative transition probability between class C and class D while listening to happy music. (B) The relative transition
probability between class C and class D while listening to sad music. (C) The relative transition probability from class C to class D and from class D to
class C while listening to happy and sad music listening. (D) Relative mean occurrence of microstates for female and male participants. (E) Relative
mean occurrence of microstates while listening to happy and sad music listening. (F) Relative mean GFP of microstates while listening to happy and
sad music listening for male and female participants (**FDR corrected, p<0.05; error bars =1 SD).
FIGURE 5
Word cloud of microstate features during happy and sad music listening. (A) Class C brain microstate features during happy music. (B) Class C brain
microstate features during sad music. (C) Class D brain microstate features during happy music. (D) Class D brain microstate features during Sad
music. Font size of the words is proportional to value of the Microstate features.
1.11428. They were cingulate gyrus, posterior cingulate, precuneus,
and paracentral lobule. One hundred and fourteen regions were
found to have significantly higher brain activity during the FH
state compared to FS, as shown in Figure 6B. Most of the regions
were located upon cingulate gyrus, precuneus, posterior cingulate,
parahippocampal gyrus, and cuneus, with a t-value ranging from
3.817 to 5.25 at p<0.05 and effect size ranging from 0.9855
to 1.3555. We performed lagged phase coherence analysis across
68 ×68 brain regions defined by the standard Desikan-Killiany
atlas (Supplementary Table S1). The result shows that a connection
between precuneus and superior temporal gyrus has significantly
reduced connectivity (t =5.88, df =14, p<0.05, effect size =
1.5182) during the FH state as compared to the FS state, as shown
in Figure 6C.
3.4 Arousal and valence analysis
We performed arousal and valence analysis for the two stimuli
for both male and female participants. The mean arousal ratings
of the participants are shown in Figure 6D. A mixed ANOVA with
gender as between factor and musical stimulus as within factor
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FIGURE 6
Microstate maps. (A) Brain regions depicting the CSD change in the alpha band under MH and MS state (p<0.05). (B) Brain regions depicting the
CSD change in the alpha band under FH and FS state at p<0.05. (C) Brain regions depicting the phase coherence changes in the alpha band under
FH and FS state (p<0.05). (D) Mean subjective rating for arousal by the participants under dierent conditions. (E) Mean subjective rating for valence
by the participants under dierent conditions (**FDR corrected, p<0.05; error bars =1 SD).
was administered. The interaction effect or main effect was not
significant. We applied a mixed ANOVA gender as between factor
and musical stimulus as within factor for valence and found a
significant interaction effect between gender and musical stimulus
(F1,30 =14.468, p<0.001). The mean valence ratings of the
participants are shown in Figure 6E. Further post-hoc analysis with
FDR correction shows that FH was significantly higher than FS (t =
13.6954, df =14, p<0.001, effect size =3.5361), MS (t =9.3009,
df =30, p<0.001, effect size =3.29), and MH (t =2.4329, df =
30, p<0.05, effect size =0.86). MH was also significantly higher
than FS (t =8.8686, df =30, p<0.001, effect size =3.14) and MS
(t =6.6193, df =16, p<0.001, effect size =1.6548). We also found
that FS was significantly different than MS (t =2.5890, df =30, p<
0.05, effect size =0.92).
4 Discussion
Happy music enhances cognitive functions such as attention
and spatial skills, while sad music aids emotional processing
and resilience. However, limitations in scientific standardization
and real-time brain analysis have restricted our understanding of
music’s effects. This study seeks to address these gaps through
microstate analysis. Specifically, we examined the alpha band
microstates that underlie the basic emotions of happiness and
sadness in male and female participants evoked through music
videos. We analyzed several features of microstates, including
GEV, coverage, occurrence, duration, and transition probability,
for FH, FS, MH, and MS states. We also conducted EEG source-
level analysis of the brain using eLoreta and compared CSD and
functional connectivity during these conditions.
Earlier studies examining cognitive enhancement, such as
increased alertness from listening to happy music, commonly
attribute the effect to heightened arousal and mood, known as the
Arousal and Mood hypothesis (Thompson et al., 2001;Husain et al.,
2002). Further research reveals that happy music directly activates
centers associated with attention (Fernandez et al., 2019;Putkinen
et al., 2017) and intelligence (Gupta et al., 2018;Jaušovec and Habe,
2003). Similarly, sadness evoked by sad music is linked to empathy
(Vuoskoski and Eerola, 2012;Huron and Vuoskoski, 2020) and
autobiographical memories (Taruffi and Koelsch, 2014;Gupta et al.,
2023) as the most prominent factor underpinning sadness.While
cumulative effects during the music listening on cognitive and
emotional processes have been explored, the dynamic aspect of
these effects is often overlooked. This investigation advances the
study by examining the dynamic nature of music’s impact during
the course of listening through microstate analysis.
In our initial analysis, we aim to identify the four microstates
for FH, FS, MH, and MS conditions. We obtain four microstates
optimally explaining (GEV) for the four states separately as shown
in Figure 1. The results indicate that the topography of these four
microstates are similar to the classical four microstates identified
in previous studies (Pascual-Marqui et al., 2014;Gu et al., 2022)
including an earlier microstate study involving the current DEAP
dataset (Hu et al., 2023) (Supplementary Figures S3–S5) and have
an overall high spatial correlation among the corresponding class
A-D microstates for all the conditions.
We further proceeded to examine various parameters,
including GEV, coverage, occurrence, GFP, duration, and transition
probability. Findings show that happy music video is linked with
a significantly higher presence (GEV), increased total amount
of time (coverage), higher frequency (occurrence), greater brain
activity (GFP), and longer average life span (duration) of the class
D microstate compared to sad music video. Conversely, sad music
video is associated with a significantly higher presence, increased
total amount of time, higher frequency, and longer duration of the
class C microstate compared to happy music video (Figures 2,3).
Moreover, the analysis indicates that microstate class D is notably
heightened, whereas class C is downregulated during happy
music videos, in contrast to other microstates. Further analysis
was conducted on the transition probability between microstates
class C and class D for both happy and sad stimuli. The results
indicated that happy music upregulated class D microstate and
downregulated class C microstate as shown in Figures 4A,B, while
sad music had the opposite effect as shown in Figure 4C. The
findings are consistent across both genders.
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Microstates class C and class D are linked to enhanced DMN
and attention, respectively, as observed in earlier studies (Khanna
et al., 2015;Michel and Koenig, 2018;Koenig et al., 2002). The
DMN has been associated with mind-wandering (Mason et al.,
2007;Kucyi et al., 2013), and an enhanced DMN activity during sad
music listening has been linked to an increased mind-wandering
(Taruffi et al., 2017). Thus, in line with earlier findings (Taruffi et al.,
2017;Gupta et al., 2018;Putkinen et al., 2017;Gupta et al., 2023;
Husain et al., 2002), the analysis of microstate parameters reveals
that regardless of gender: (1) the attentiveness is significantly
larger during happy music video than during sad music video,
(2) conversely, mind-wandering is significantly higher during sad
music video than during happy music video, and (3) during
happy music video, the brain exhibits increased attentiveness and
a decreased mind wondering. Figure 5 illustrates a word cloud of
brain microstate features for classes C and D during happy and
sad music listening, showcasing musical stimuli-based differences
as revealed by EEG analysis.
Traditional EEG analyses, such as power and phase
connectivity, typically portray an apparent continuous activation
of brain regions throughout the duration of music listening.
Microstate analysis reveals that functional brain states, such
as enhanced attention, are not continuously present during
music listening; instead, they manifest as brief episodes lasting
tens of milliseconds. These microstates represent fundamental
instantiations of human neurological tasks, and further analysis
elucidates the duration, frequency, potential, and prominence of
these microstates during music listening.
Furthermore, our findings revealed that the GFP for class
A microstate was significantly higher during the happy state
compared to the sad state, regardless of gender (Figure 3D). This
suggests that the happy state is characterized by enhanced auditory
processing as class A microstate is linked with the auditory network
(Khanna et al., 2015;Michel and Koenig, 2018;Koenig et al., 2002;
Tarailis et al., 2023). This finding supports the notion of increased
awareness of music during happy music listening, which has been
reported in a previous study (Taruffi et al., 2017). Furthermore,
regardless of gender, the GEV for the class B microstate was
significantly higher during the sad state compared to the happy
state (Figure 2B). The class B microstate is associated with visual
processing, self-visualization, autobiographical memory, and scene
visualization (Tarailis et al., 2023). This observation likely supports
enhanced spontaneous self-referential processes and thoughts
that are enriched with images during the sad music listening
as reported in previous study (Taruffi et al., 2017). However,
further research is necessary to gain a more comprehensive
understanding of both these relationships and delve deeper into
their implications.
The results also revealed that regardless of musical stimuli,
female participants had significantly larger average life span for
the class D microstate compared to other microstates of female
participants and to the class D microstate of male participants
(Figures 2E,F).
We also analyzed mean value of parameters of GEV, coverage,
occurrence, GFP, and duration. The results indicated that regardless
of musical stimuli, the mean frequency of occurrence of microstates
was significantly higher in female participants compared to male
participants (Figure 4D). The finding is in support of earlier studies
(Whittle et al., 2011;Al-Fahad and Yeasin, 2019) that showed that
males tend to exhibit a greater likelihood of remaining in a specific
state, while females tend to exhibit a greater likelihood of being in
transient states. Similarly, we observed that the mean frequency of
occurrence of microstates was significantly higher during sad music
listening compared to listening to happy music regardless of gender
(Figure 4E). Mean GFP analysis shows brain during happy music
listening has higher electrical activity than the brain during sad
music listening (Figure 4F) for the male participants.
EEG microstate analysis reveals that happy and sad music
evoke distinct patterns in GEV, duration, GFP, and occurrence
across genders. However, gender notably impacts parameters such
as coverage, mean occurrence, and mean GFP indicating that
both emotional content and gender significantly influence neural
responses to music.
CSD reflects the mean brain activity during the musical videos.
CSD analysis revealed that the happy state was characterized
by enhanced activity in the central-parietal regions compared
to the sad state across gender (Figures 6A,B). The higher CSD
activity observed during the happy state compared to the sad
state aligns with previous research that has found a positive
correlation between the valence of the music stimulus and
brain activity (Koelstra et al., 2011). Additionally, studies have
shown that an increased activity in the central-parietal regions
during music listening is associated with enhanced attentiveness
(Markovic et al., 2017;Jäncke et al., 2015;Gupta et al., 2023).
Thus, findings suggest an enhanced attention during happy state
compared to sad state. The current CSD analysis finding is
in line with the results of the microstate analysis and likely
reflect the predominant characteristics of the brain state during
the whole period of happy music video compared to sad
music video.
According to the Lagged Phase Synchronization analysis, the
brain exhibited greater connectivity between the precuneus and
superior temporal gyrus (STG) during the sad state compared
to the happy state for female participants (Figure 6C). Enhanced
functional connectivity between STG and precuneus represents
the increased connection between the auditory cortex and DMN.
The finding is consistent with a previous study (Taruffi et al.,
2017) which found increased centrality in the DMN regions
(including the precuneus) during sad music listening compared
to happy music listening. The author suggested that increased
centrality leads to enhanced DMN activity and thus increased mind
wandering during sad music listening. Therefore, the enhanced
brain connectivity observed between the precuneus and STG
during the sad state could be related to increased DMN activity
during sad music video. The result is in line with the findings of the
microstate analysis. However, further research is needed to confirm
this relationship.
EEG source localization studies for musical stimuli are
infrequent, with the majority relying on neuroimaging techniques
such as fMRI and PET. Consistent findings indicate that compared
to unpleasant music, pleasant music activates specific regions,
including the subcallosal cingulate, inferior frontal gyrus, anterior
insula, parietal operculum, and ventral striatum. Conversely, the
unpleasant scrambled condition shows heightened activity in the
amygdala, hippocampus, and temporal poles (Mitterschiffthaler
et al., 2007;Blood et al., 1999;Trost et al., 2012). However,
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FIGURE 7
Schematic model. (A) Brain during happy music is marked by enhanced attention (blue color). (B) Brain during sad music is marked by enhanced
mind wandering (yellow color). (C) Potential neural pathways that enhances attention and mind wandering during happy and sad music listening,
respectively.
eLoreta-based source analysis faces challenges in spatial resolution,
particularly for deeper emotion processing regions such as the
amygdala, insula, and hippocampus. While our CSD analysis
aligns with previous EEG findings (Markovic et al., 2017;Jäncke
et al., 2015;Koelstra et al., 2011), further research is essential
to establish correlations between fMRI and EEG studies. Future
investigations concurrently utilizing both modalities promise
deeper insights. Moreover, we utilized a 32-channel setup for
source localization analysis. Studies with a greater number of
electrodes would be invaluable in gaining deeper and more
precise insights into the underlying neural sources during music
video listening.
Behavioral analysis shows that there was no difference in the
arousal rating of the participants in any conditions (Figure 6D).
Valance analysis indicates that the FH state had a significantly
higher positive state followed by the MH state compared to FS
and MS states (Figure 6E). FS state participants had particularly the
highest sad experience compared to other states.
Music conveys emotions that are perceived by listeners, giving
rise to two contrasting viewpoints: the “cognitivist” position,
asserting that music expresses emotions perceived by the listener,
and the “emotivist” position, suggesting that music also elicits
emotions (Juslin et al., 2001). In a seminal work, Gabrielsson
(2020) proposed various relationships between perception and
induction, including positive, negative, no systematic relationship,
and no relation. While some researchers assume a positive
relationship, it is not universally applicable. It is crucial to
distinguish between perceiving an emotion in music and actually
experiencing an emotional response to it. Statistically equal
ratings of felt and expressed emotion occur when music is
liked, as opposed to disliked by participants (Schubert, 2010,
2013). The current investigation shows a high rating for liking
for both music videos (Supplementary Figure S6), suggesting a
smaller gap between felt and expressed emotion. However, future
studies that distinguish and comparatively analyze both aspects
are crucial.
Key implications of EEG microstate analysis on brain
cognitive functions during music listening include: (1) Whole-
Brain Integration: Microstate analysis reveals that cognition
emerges from coordinated brain networks rather than isolated
regions, supporting the global workspace theory (Baars et al.,
2021) that cognitive processes result from integrated brain activity
across distributed areas. (2) Temporal Dynamics: Microstate
analysis introduces a temporal aspect, showing that cognitive
functions unfold through brief, shifting brain states. This aligns
with dynamic cognition theories, which suggest that mental
processes depend on transient network configurations, enhancing
models of sequential processing. (3) Simultaneous Function
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Integration: By linking distinct microstates to different cognitive
functions, microstate analysis shows that multiple functions
operate simultaneously, supporting the view that experiences are
shaped by various interacting processes for cohesive perception
and response.
In summary (Figure 7), our results demonstrate that regardless
of gender, the microstate characteristics of the brain during happy
and sad music listening are unique and distinct from each other.
Specifically, happy music listening is associated with enhanced
class D, indicating increased attentiveness, while sad music
listening is associated with higher class C, indicating increased
mind-wandering. The results are in line with an earlier study
investigating the effect of happy and sad music on mind wandering
and meta awareness (Taruffi et al., 2017). However, gender
significantly affects coverage, mean GFP, and mean occurrence,
indicating that both emotion and gender shape neural responses
to music. We also found a significantly enhanced mean occurrence
of microstates during sad music listening compared to happy one.
The results of the eLoreta analysis also support the results of the
microstate analysis.
5 Limitations and future directions
Although our study provides insight into the relationship
between music-induced emotions and brain microstates, several
limitations warrant further research. First, it remains unclear how
the varying levels of happiness and sadness in music impact
microstates, particularly classes C and D. Real-time subjective
assessments of attention and mind-wandering could provide a
more nuanced understanding. Expanding this research with a wider
range of happy and sad music stimuli and different durations may
enhance its generalizability. The current microstate investigation
was alpha band specific analysis future comparative investigations
across all frequency bands could provide further insights and
expand the analysis. Further studies exploring the relationship
between microstate dynamics and individual psychological traits,
such as depression, empathy, and cognition, would provide
deeper insights and enhance our understanding. Furthermore,
the mind wandering induced by sad music could differ for
individuals with conditions such as depression or PTSD, and
caution is advised to use sad music therapeutically, as it may
be counterproductive in these populations (van den Tol, 2016).
Future studies should utilize a dense montage system with 64
or more electrodes to improve microstate and source localization
analysis, particularly for identifying the neural sources underlying
microstates. Additionally, the unique ability of happy music to
foster attention and downregulate mind wandering could be
utilized in the healthcare system.
Data availability statement
Publicly available datasets were analyzed in this study. This data
can be found here: http://www.eecs.qmul.ac.uk/mmv/datasets/
deap/.
Ethics statement
Ethical approval was not required for the study involving
humans in accordance with the local legislation and institutional
requirements. Written informed consent to participate in this study
was not required from the participants or the participants’ legal
guardians/next of kin in accordance with the national legislation
and the institutional requirements.
Author contributions
AG: Conceptualization, Data curation, Formal analysis,
Investigation, Methodology, Software, Validation, Visualization,
Writing – original draft, Writing – review & editing. CS:
Data curation, Formal analysis, Writing – original draft.
BB: Conceptualization, Project administration, Resources,
Supervision, Visualization, Writing – review & editing. LB:
Conceptualization, Funding acquisition, Project administration,
Resources, Supervision, Visualization, Writing – review & editing.
Funding
The author(s) declare financial support was received for the
research, authorship, and/or publication of this article. The current
work is partly supported by the research grant by Brainwave
Science, Inc., Southborough, USA (IITM/BS-USA/LB/451) and
Indian Knowledge Systems Division of Ministry of Education,
Govt of India (AICTE/IKS/RFPI/2021-22/01). Brainwave Science,
Inc. was not involved in the study design, collection, analysis,
interpretation of data, the writing of this article, or the decision to
submit it for publication.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fnhum.
2024.1469468/full#supplementary-material
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Gupta et al. 10.3389/fnhum.2024.1469468
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