Early Investigations into Music-Induced Neural and Cardiovascular Responses.pdf

Objective: Explore the neural correlation to the heartbeat in response to music
Participants: 30 participants aged 18-22
Key Findings: Slow music accelerates heart rate recovery post-exercise

Background
The relationship between music and emotions has long been recognized in various cultures.
Early studies were motivated by the need to scientifically understand this relationship, focusing on how music affects the brain and the heart.
These studies sought to identify specific physiological responses triggered by musical stimuli.

1. Title: Early Investigations into Music-Induced
Neural and Cardiovascular Responses
Mr. Prasad Purnaye
Assistant Professor
School of Computer Engineering & Technology,
MIT World Peace University, Pune, India.
Sanket Salvi
Assistant Professor
School of Computer Engineering & Technology,
MIT World Peace University, Pune, India.
PAPER ID: 674
2nd International Conference on Artificial Intelligence:
Theory and Applications (AITA 2024)
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2. Abstract & Background
•Objective: Explore the neural correlation to the heartbeat in response to music
•Participants: 30 participants aged 18-22
•Key Findings: Slow music accelerates heart rate recovery post-exercise
Background
•The relationship between music and emotions has long been recognized in various cultures.
•Early studies were motivated by the need to scientifically understand this relationship, focusing on
how music affects the brain and the heart.
•These studies sought to identify specific physiological responses triggered by musical stimuli.
2nd International Conference on Artificial Intelligence:
Theory and Applications (AITA 2024)
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3. Introduction
Importance of Physical Health: Essential for overall
Health
Focus: Impact of Music Genre on heart rate recovery
post exercise
Goal: Uncover insights for personalized exercise
regimens and recovery strategies
2nd International Conference on Artificial Intelligence:
Theory and Applications (AITA 2024)
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4. Related Work
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Theory and Applications (AITA 2024)
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Neural Correlates of Music-Induced Emotions:
Several studies have utilized EEG-based methodologies to investigate
how music evokes emotions and the corresponding neural
mechanisms.
For instance, research by Sun et al. (2019) and Zhang et al. (2019)
employed deep learning techniques to analyze EEG signals and
recognize music-induced emotions, highlighting the complexity of
emotional processing in response to music.

5. Related Work
2nd International Conference on Artificial Intelligence:
Theory and Applications (AITA 2024)
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Post-Exercise Recovery:
✓The influence of music on recovery after exercise has
garnered attention, with findings indicating that music
can positively affect mood, motivation, and perceived
exertion.
✓Guzzo et al. (2015) reviewed the benefits of music on
exercise performance, although they noted challenges in
drawing definitive conclusions due to diverse study
designs and individual differences.

6. Related Work
2nd International Conference on Artificial Intelligence:
Theory and Applications (AITA 2024)
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Heart Rate Synchronization:
✓Research has shown that music can synchronize heart
rates
✓suggesting that physiological responses to music are
consistent and not solely influenced by the listener's
emotional state or motivation.

7. Related Work
2nd International Conference on Artificial Intelligence:
Theory and Applications (AITA 2024)
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Music-Based Meditation:
✓The efficacy of music in managing stress and anxiety has been
supported by meta-analyses, such as that by Hassed et al. (2010),
which found strong evidence for the benefits of mindfulness-based
stress reduction techniques that incorporate music.

8. Related Work
2nd International Conference on Artificial Intelligence:
Theory and Applications (AITA 2024)
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Cognitive Enhancement: Studies have indicated that
music may enhance cognitive function, particularly in
older adults, suggesting its potential role in cognitive health
maintenance.

9. Related Work
2nd International Conference on Artificial Intelligence:
Theory and Applications (AITA 2024)
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✓Motor Learning and Movement: Research has
demonstrated that different emotional music pieces can
influence motor learning and kinematics,
✓indicating that music can facilitate changes in emotional
and physiological states, potentially accelerating
relaxation and recovery processes after physical
exertion.

10. Related Work
Neural Correlates of
Music-Induced
Emotions: Several studies
have utilized EEG-based
methodologies to
investigate how music
evokes emotions and the
corresponding neural
mechanisms. For
instance, research by Sun
et al. (2019) and Zhang et
al. (2019) employed deep
learning techniques to
analyze EEG signals and
recognize music-induced
emotions, highlighting the
complexity of emotional
processing in response to
music2.
Post-Exercise Recovery:
The influence of music on
recovery after exercise
has garnered attention,
with findings indicating
that music can positively
affect mood, motivation,
and perceived exertion.
Guzzo et al. (2015)
reviewed the benefits of
music on exercise
performance, although
they noted challenges in
drawing definitive
conclusions due to
diverse study designs and
individual differences2.
Heart Rate
Synchronization:
Research has shown that
music can synchronize
heart rates, suggesting
that physiological
responses to music are
consistent and not solely
influenced by the
listener's emotional state
or motivation2.
Music-Based Meditation:
The efficacy of music in
managing stress and
anxiety has been
supported by meta-
analyses, such as that by
Hassed et al. (2010),
which found strong
evidence for the benefits
of mindfulness-based
stress reduction
techniques that
incorporate music2.
Cognitive Enhancement:
Studies have indicated
that music may enhance
cognitive function,
particularly in older
adults, suggesting its
potential role in cognitive
health maintenance2.
Motor Learning and
Movement: Research has
demonstrated that
different emotional music
pieces can influence
motor learning and
kinematics, indicating that
music can facilitate
changes in emotional and
physiological states,
potentially accelerating
relaxation and recovery
processes after physical
exertion2.
2nd International Conference on Artificial Intelligence:
Theory and Applications (AITA 2024)
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11. Proposed Work
• Methodology:
❑Multi-faceted approach combining EEG, pulse rate data, and machine learning
• Experiment Design:
❑Participants' heart rates and neural responses monitored during exercise and recovery
with/without music
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Theory and Applications (AITA 2024)
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12. Experimental Setup
EEG Data Collection: Using OpenBCI EEG headsets
Pulse Rate Monitoring: Using Arduino + Pulse sensor
Phases: Baseline, Intense Workout, Music-Enhanced Workout
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13. Experiment
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Theory and Applications (AITA 2024)
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14. Experimentation: Parameter Considerations
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15. Experimental Observations
•Participants: 30 (15 females, 15 males)
•Heart Rates: Resting average 75.6 bpm, post-exercise average 103.8 bpm
•Recovery Time: With music Average 247 seconds, without music 298 seconds
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Theory and Applications (AITA 2024)
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16. Result Analysis
•Machine Learning: K-Nearest Neighbors (KNN) algorithm
•Metrics: MSE, RMSE, MAE, R2 score
•Results: R2 score of 0.77 indicating a strong correlation
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Theory and Applications (AITA 2024)
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17. Conclusion
Findings: Slow music enhances parasympathetic activation, faster heart rate recovery
Implications: Potential for personalized music-based recovery strategies in exercise
science
2nd International Conference on Artificial Intelligence:
Theory and Applications (AITA 2024)
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18. Future Directions
Further Research:
Extend study to diverse age groups, different music genres, specific workout activity
Applications: Development of innovative interventions for promoting health and
well-being through music
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Theory and Applications (AITA 2024)
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19. References
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Theory and Applications (AITA 2024)
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20. Thank You !
Questions?
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Theory and Applications (AITA 2024)
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Early Investigations into Music-Induced Neural and
Cardiovascular Responses
Mr. Prasad Purnaye
Assistant Professor
School of Computer Engineering & Technology,
MIT World Peace University, Pune, India.
[email protected]
Sanket Salvi
Assistant Professor
School of Computer Engineering & Technology,
MIT World Peace University, Pune, India.
•PAPER ID: 674