Keywords
EEG
Neural correlates
Cognitive functions
Emotional responses
Rhythmic synchronization
Therapeutic potential
Technological innovation
EEG signals
Different frequency bands
How to Cite
Abstract
This comprehensive review investigates the intricate relationship between music and brain activity, focusing on insights gleaned from electroencephalography (EEG) studies. Music's impact on cognition, emotion, and behaviour is examined through the lens of EEG signals, elucidating the neural mechanisms underlying musical perception and processing. Rhythmic entrainment, emotional modulation, and cross-modal integration are explored as fundamental aspects of music-brain interactions. Clinical applications, therapeutic interventions, and future directions in music neuroscience are discussed, highlighting the potential for personalized interventions and cognitive enhancement. By synthesizing findings from diverse disciplines, this review underscores the transformative power in promoting health, cognitive vitality and well-being by music. Embracing interdisciplinary collaboration and technological innovation, we can continue to explore in depth of music's involvement on the brain and harness its therapeutic potential for the benefit of EEG and giving a brief information of EEG signal and its Technologies and process of signal acquiring.
References
1. Meng, Q., Tian, L., Liu, G., & Zhang, X. (2025). EEG-based cross-subject passive music pitch perception using deep learning models. Cognitive Neurodynamics, 19(1), 6.
2. Reddy, Srikireddy Dhanunjay, and Tharun Kumar Reddy Bollu. "Music Therapy based Stress Prediction using Homological Feature Analysis on EEG Signals." arXiv preprint arXiv:2502.18835 (2025).
3. Lin, Q., Cao, Y., Wu, Y., Qiu, X., Sun, B., & Lv, X. (2025). Protocol to study the neural mechanism of music therapy in alleviating depression using EEG-LFP signal recording. STAR protocols, 6(1), 103602.
4. Li, Y., Guo, S., Chen, J., Feng, L., Bian, H., Chen, Y., ... & Lu, J. (2025). Lifespan music training experience changes duration and transition rates of EEG microstates related to working memory. Brain-Apparatus Communication: A Journal of Bacomics, 4(1), 2465539.
5. Pasqualitto, F., Maidhof, C., Murtagh, D., Silva, D., Fernie, P., Panin, F., ... & Fachner, J. (2025). Music therapy modulates craving, inhibitory control, and emotional regulation: EEG, psychometric, and qualitative findings from a pilot RCT in a community outpatient service.
6. Khairunizam, W., Harsono, A. R. S., Choong, W. Y., & Mustafa, W. A. (2025, March). Analysis the Effect of Listening Music on the Human Brain by Using Electroencephalogram (EEG) Technique. In 2025 17th International Conference on Computer and Automation Engineering (ICCAE) (pp. 287-291). IEEE.
7. Garoufis, C., Glytsos, M., Chourdaki, I., Filntisis, P. P., & Maragos, P. (2025, April). Power in unity: Combining in-domain and out-of-domain pre-training strategies for eeg-based person identification. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-2). IEEE.
8. Reddy, V. S. S., & Paneerselvam, S. (2025, June). Music Recommendation System Based on EEG Signals: A Hybrid LSTM-GRU. In Fifth Congress on Intelligent Systems: CIS 2024, Volume 2 (Vol. 1276, p. 45). Springer Nature.
9. Khaleghimoghaddam, N., & Arzhangi, S. (2025). How can architectural acoustics reflect levels of stress and relaxation in indoor environments? An EEG-based experimental study. OBM Neurobiology, 9(3), 1-22.
10. Martin, L. C. C., Kumar, S. G., Ismail, A. S., & Jayaraj, R. (2025, April). EEG-Based Emotion Detection and AI-Generated Music: A Computational Approach to Personalized Emotion Modulation. In 2025 International Conference on Data Science and Business Systems (ICDSBS) (pp. 1-14). IEEE.
11. Lorenz, A., Mercier, M., Trébuchon, A., Bartolomei, F., Schön, D., & Morillon, B. (2025). Corollary discharge signals during production are domain general: An intracerebral EEG case study with a professional musician. Cortex, 186, 11-23.
12. Madhubala, B., Kumar, A. S., Tennin, K. L., Suganya, R. V., Kaur, G., Srinivasulu, A., ... & Irawati, I. D. (2025). Personalized Music Recommendation System for Athletes Using EEG Signals. In Coaching in Communication Research (pp. 141-168). IGI Global Scientific Publishing.
13. Han, Y., Ouyang, W., Wang, S., Liu, J., Song, W., & Du, H. (2025, June). Efficacy of Alpha Wave Music Combined with Mindfulness Intervention in Alleviating Mental Fatigue in College Students: Synergetic Evidence Based on EEG and Subjective Scales. In Proceedings of the 2025 6th International Conference on Education, Knowledge and Information Management (pp. 479-486).
14. Li, H., Chen, Y., Wang, Y., Ni, W., & Zhang, H. (2025). Foundation models for cross-domain eeg analysis application: A survey. arXiv preprint arXiv:2508.15716.
15. Li, J., Zhou, M., Zhang, J., Zhang, J., Zhang, L., Zeng, X., & Zhang, H. (2025). Personalized sleep-aiding music intervention for insomnia: A closed-loop neurofeedback approach. Sleep Medicine, 106693.
16. Kim, K. M. M., Kim, J., & McFerran, K. S. (2025, July). Exploring the Practicality of Portable EEG Equipment for Visualising Emotional Responses When Listening to Meaningful Songs: A Position Paper. In Voices: A World Forum for Music Therapy (Vol. 25, No. 2).
17. Niedermeyer, E., & da Silva, F. L. (2005). Electroencephalography: Basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins.
18. Gotman, J. (2008). EEG in the diagnosis, classification, and management of patients with epilepsy. Journal of Clinical Neurophysiology.
19. Malow, B. A., Kushwaha, R., Lin, X., Morton, R., & Aldrich, M. S. (1997). Interobserver agreement for sleep spindle scoring in normal subjects and patients with sleep-disordered breathing. Clinical Neurophysiology.
20. Hammond, D. C. (2005). Neurofeedback treatment of depression with the Roshi. Journal of Neurotherapy.
21. Lebedev, M. A., & Nicolelis, M. A. (2006). Brain-machine interfaces: past, present and future. Trends in Neurosciences.
22. Luck, S. J. (2014). An introduction to the event-related potential technique. MIT press.
23. Acharya, U. R., Sree, S. V., Swapna, G., Martis, R. J., & Suri, J. S. (2013). Automated EEG analysis of epilepsy: A review. Knowledge-Based Systems.
24. Lipa, A., & Ciel, A. I. (2025). Waveform Reconstruction Software Layer for EEG and EKG Devices.
25. Xia, B., Zhang, Q., Xie, H., & Li, J. (2012, June). A neurofeedback training paradigm for motor imagery based Brain-Computer Interface. In The 2012 International Joint Conference on Neural Networks (IJCNN) (pp. 1-4). IEEE.
26. Kim, Y. E., Schmidt, E. M., Migneco, R., Morton, B. G., Richardson, P., Scott, J., ... & Turnbull, D. (2010, August). Music emotion recognition: A state of the art review. In Proc. ismir (Vol. 86, pp. 937-952).
27. Han, D., Kong, Y., Han, J., & Wang, G. (2022). A survey of music emotion recognition. Frontiers of Computer Science, 16(6), 166335.
28. Hevner, K. (1936). Experimental studies of the elements of expression in music. The American journal of psychology, 48(2), 246-268.
29. Thayer, R. E. (1990). The biopsychology of mood and arousal. Oxford University Press.
30. Tellegen, A., Watson, D., & Clark, L. A. (1999). Further support for a hierarchical model of affect: Reply to Green and Salovey. Psychological Science, 10(4), 307-309.
31. Russell, J. A. (1980). A circumplex model of affect. Journal of personality and social psychology, 39(6), 1161.
32. Thammasan, N., Fukui, K. I., & Numao, M. (2016, July). Application of deep belief networks in eeg-based dynamic music-emotion recognition. In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 881-888). IEEE.
33. Buzsáki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science.
34. Steriade, M., Nunez, A., & Amzica, F. (1993). A novel slow (< 1 Hz) oscillation of neocortical neurons in vivo: depolarizing and hyperpolarizing components. Journal of Neuroscience.
35. Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain research reviews.
36. Barry, R. J., Clarke, A. R., Johnstone, S. J., Magee, C. A., & Rushby, J. A. (2007). EEG differences between eyes-closed and eyes-open resting conditions. Clinical Neurophysiology.
37. Pfurtscheller, G., & Lopes da Silva, F. H. (1999). Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical neurophysiology.
38. Uhlhaas, P. J., & Singer, W. (2010). Abnormal neural oscillations and synchrony in schizophrenia. Nature Reviews Neuroscience.
39. Rumsey, F. (2001). Bass lines: The nature and function of bass in music from the 17th century to present day. Routledge.
40. Roads, C. (1996). The computer music tutorial. MIT press.
41. De Boer, S. (1997). Music perception. Springer Science & Business Media.
42. Benade, A. H. (1990). Fundamentals of musical acoustics (Vol. 14). Courier Corporation.
43. Malmberg, C. F. (1917). Music perception. The American Journal of Psychology.
44. Moore, B. C. (2012). An introduction to the psychology of hearing. Brill.
45. Hammond, D. C. (2005). Neurofeedback with anxiety and affective disorders. Child and Adolescent Psychiatric Clinics.
46. Calcagno, S., Carnemolla, S., Kavasidis, I., Palazzo, S., Giordano, D., & Spampinato, C. (2025, April). Eeg-music emotion recognition: Challenge overview. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-3). IEEE.
47. Zhang, Y., Zhou, X., & Zhang, M. (2012). Temporary inhibitory tagging at previously attended locations: Evidence from event‐related potentials. Psychophysiology, 49(9), 1191-1199.
48. Barry, R. J., Clarke, A. R., Johnstone, S. J., Magee, C. A., & Rushby, J. A. (2007). EEG differences between eyes-closed and eyes-open resting conditions. Clinical Neurophysiology.
49. El Sayed, B. B., Basheer, M. A., Shalaby, M. S., El Habashy, H. R., & Elkholy, S. H. (2025). The power of music: impact on EEG signals. Psychological Research, 89(1), 42.
50. Das, N., & Chakraborty, M. (2025). Optimal multimodal feature combination and classifier selection for music-based EEG signal analysis. Computers in Biology and Medicine, 196, 110696.
51. Salimpoor, V. N., Benovoy, M., Larcher, K., Dagher, A., & Zatorre, R. J. (2011). Anatomically distinct dopamine release during anticipation and experience of peak emotion to music. Nature neuroscience.
52. Müllensiefen, D., Gingras, B., Musil, J., & Stewart, L. (2014). The musicality of non-musicians: an index for assessing musical sophistication in the general population.
53. Oostenveld, R., & Praamstra, P. (2001). The five percent electrode system for high-resolution EEG and ERP measurements. Clinical neurophysiology.
54. Schäfer, T., Sedlmeier, P., Städtler, C., & Huron, D. (2013). The psychological functions of music listening. Frontiers in psychology.
55. Sammler, D., Grigutsch, M., Fritz, T., & Koelsch, S. (2007). Music and emotion: Electrophysiological correlates of the processing of pleasant and unpleasant music. Psychophysiology.
56. Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods.
57. Makeig, S., Bell, A. J., Jung, T. P., & Sejnowski, T. J. (1996). Independent component analysis of electroencephalographic data. Advances in neural information processing systems.
58. Penny, W. D., Holmes, A. P., & Friston, K. J. (2003). Random effects analysis. Human brain mapping.
59. Cohen, M. X. (2014). Analyzing neural time series data: theory and practice
60. Sanei, S., & Chambers, J. A. (2013). EEG signal processing. John Wiley & Sons.
61. Subasi, A. (2007). EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications, 32(4), 1084-1093.
62. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
63. Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018). A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. Journal of neural engineering, 15(3), 031005.
64. Chen, J., Wang, Y., Liu, Y. L., Feng, R., Yuan, J., & Ling, Z. H. (2025, April). The ustc system for eeg-music emotion recognition challenge. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-2). IEEE.
65. Tas, S., Tanko, D., Tasci, I., Dogan, S., & Tuncer, T. (2025). TBP-XFE: A transformer-based explainable framework for EEG music genre classification with hemispheric and directed lobish analysis. Applied Acoustics, 239, 110855.
66. Sheykhivand, S., Mousavi, Z., Rezaii, T. Y., & Farzamnia, A. (2020). Recognizing emotions evoked by music using CNN-LSTM networks on EEG signals. IEEE access, 8, 139332-139345.
67. Dharmapriya, J., Dayarathne, L., Diasena, T., Arunathilake, S., Kodikara, N., & Wijesekera, P. (2021, January). Music emotion visualization through colour. In 2021 international conference on electronics, information, and communication (ICEIC) (pp. 1-6). IEEE.
68. Que, Y., & Hu, X. (2025). Enhancing Learners' Reading Comprehension With Preferred Background Music: An Eye‐Tracking, EEG, and Heart Rate Study. Reading Research Quarterly, 60(2), e70004.
69. Ahmadzadeh Nobari Azar, N., Cavus, N., Esmaili, P., Sekeroglu, B., & Aşır, S. (2024). Detecting emotions through EEG signals based on modified convolutional fuzzy neural network. Scientific Reports, 14(1), 10371.
70. Thoma, M. V., La Marca, R., Brönnimann, R., Finkel, L., Ehlert, U., & Nater, U. M. (2013). The effect of music on the human stress response. PloS one, 8(8), e70156.
71. Altenmüller, E., & Schlaug, G. (2013). Neurologic music therapy: The beneficial effects of music making on neurorehabilitation. Acoustical Science and Technology, 34(1), 5-12.
72. Zatorre, R. J., & Salimpoor, V. N. (2013). From perception to pleasure: music and its neural substrates. Proceedings of the National Academy of Sciences, 110(supplement_2), 10430-10437.
73. Kumar, S. (2017). Effect of popular music culture on university students. The Social ION, 6(2), 01-05.
74. Strauss, H., Vigl, J., Jacobsen, P. O., Bayer, M., Talamini, F., Vigl, W., ... & Zentner, M. (2024). The Emotion-to-Music Mapping Atlas (EMMA): A systematically organized online database of emotionally evocative music excerpts. Behavior Research Methods, 56(4), 3560-3577.
75. Alluri, V., Toiviainen, P., Jääskeläinen, I. P., Glerean, E., Sams, M., & Brattico, E. (2012). Large-scale brain networks emerge from dynamic processing of musical timbre, key and rhythm. NeuroImage.
76. Schaefer, R. S., Morcom, A. M., Roberts, N., & Overy, K. (2014). Moving to music: Effects of heard and imagined musical cues on movement-related brain activity. Frontiers in Human Neuroscience.
77. Groussard, M., La Joie, R., Rauchs, G., Landeau, B., Chételat, G., Viader, F., Desgranges, B., & Eustache, F. (2014). When music and long-term memory interact: Effects of musical expertise on functional and structural plasticity in the hippocampus.
78. Jiang, H., Chen, Y., Wu, D., & Yan, J. (2024). EEG-driven automatic generation of emotive music based on transformer. Frontiers in Neurorobotics, 18, 1437737.
79. Särkämö, T., Tervaniemi, M., Laitinen, S., Forsblom, A., Soinila, S., Mikkonen, M., & Hietanen, M. (2008). Music listening enhances cognitive recovery and mood after a middle cerebral artery stroke. Brain.
80. Lin, Y. P., Wang, C. H., Wu, T. L., Jeng, S. K., & Chen, J. H. (2009, April). EEG-based emotion recognition in music listening: A comparison of schemes for multiclass support vector machine. In 2009 IEEE international conference on acoustics, speech and signal processing (pp. 489-492). IEEE.
81. Thammasan, N., Fukui, K. I., & Numao, M. (2016, October). An investigation of annotation smoothing for eeg-based continuous music-emotion recognition. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 003323-003328). IEEE.
82. Liu, J., He, T., & Hu, Z. (2025). The Effect of Music on Resistance to Mental Fatigue: Evidence from the EEG Power Spectrum. Applied Psychophysiology and Biofeedback, 1-11.
83. Bhatti, A. M., Majid, M., Anwar, S. M., & Khan, B. (2016). Human emotion recognition and analysis in response to audio music using brain signals. Computers in Human Behavior, 65, 267-275.
84. Shahabi, H., & Moghimi, S. (2016). Toward automatic detection of brain responses to emotional music through analysis of EEG effective connectivity. Computers in Human Behavior, 58, 231-239.
85. Thammasan, N., Moriyama, K., Fukui, K. I., & Numao, M. (2016). Continuous music-emotion recognition based on electroencephalogram. IEICE TRANSACTIONS on Information and Systems, 99(4), 1234-1241.
86. Thammasan, N., Fukui, K. I., & Numao, M. (2017, February). Multimodal fusion of EEG and musical features in music-emotion recognition. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31, No. 1).
87. Hsu, J. L., Zhen, Y. L., Lin, T. C., & Chiu, Y. S. (2018). Affective content analysis of music emotion through EEG. Multimedia Systems, 24(2), 195-210.
88. Salama, E. S., El-Khoribi, R. A., Shoman, M. E., & Shalaby, M. A. W. (2018). EEG-based emotion recognition using 3D convolutional neural networks. Int. J. Adv. Comput. Sci. Appl, 9(8), 329-337.
89. Keelawat, P., Thammasan, N., Kijsirikul, B., & Numao, M. (2019, March). Subject-independent emotion recognition during music listening based on EEG using deep convolutional neural networks. In 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA) (pp. 21-26). IEEE.
90. Rahman, M. A., Hossain, M. F., Hossain, M., & Ahmmed, R. (2020). Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal. Egyptian Informatics Journal, 21(1), 23-35.
91. Paukner, P., Ripoll, M., Sabir, D., Erdoğan, D. O., Sacchetto, L., & Diepold, K. (2025, April). Classifying music-induced emotion using multi-modal ensembles of eeg and audio feature models. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
92. Avramidis, K., Garoufis, C., Zlatintsi, A., & Maragos, P. (2022, May). Enhancing affective representations of music-induced EEG through multimodal supervision and latent domain adaptation. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4588-4592). IEEE.
93. Hasanzadeh, F., Annabestani, M., & Moghimi, S. (2021). Continuous emotion recognition during music listening using EEG signals: a fuzzy parallel cascades model. Applied Soft Computing, 101, 107028.
94. Zainab, R., & Majid, M. (2021). Emotion recognition based on EEG signals in response to bilingual music tracks. Int. Arab J. Inf. Technol., 18(3), 286-296.
95. Naser, D. S., & Saha, G. (2021). Influence of music liking on EEG based emotion recognition. Biomedical Signal Processing and Control, 64, 102251.
96. Demir, F., Sobahi, N., Siuly, S., & Sengur, A. (2021). Exploring deep learning features for automatic classification of human emotion using EEG rhythms. IEEE Sensors Journal, 21(13), 14923-14930.
97. Demir, F., Sobahi, N., Siuly, S., & Sengur, A. (2021). Exploring deep learning features for automatic classification of human emotion using EEG rhythms. IEEE Sensors Journal, 21(13), 14923-14930.
98. Demir, F., Sobahi, N., Siuly, S., & Sengur, A. (2021). Exploring deep learning features for automatic classification of human emotion using EEG rhythms. IEEE Sensors Journal, 21(13), 14923-14930.
99. Hu, Z., Chen, L., Luo, Y., & Zhou, J. (2022). EEG-based emotion recognition using convolutional recurrent neural network with multi-head self-attention. Applied sciences, 12(21), 11255.
100. Gong, P., Wang, P., Zhou, Y., & Zhang, D. (2023). A spiking neural network with adaptive graph convolution and LSTM for EEG-based brain-computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 1440-1450.
101. Ahmed, H., Ali, Z., Narejo, S., Irfan, A., & Azeem, D. (2024, January). EEG-Based Human Emotion Recognition Using Deep Learning. In 2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC) (pp. 1-7). IEEE.
102. Ghosh, S., Mukherjee, R., & Gupta, B. (2025). Modern technologies for human brain monitoring. In Advancing Science and Innovation in Healthcare Research (pp. 611-628). Academic Press.
103. Cantiello, H. F., Porcari, C. Y., Albarracin, V. H., Murphy, D., Mecawi, A. S., Godino, A., & Cantero, M. D. R. (2025). Differences in Brain Microtubule Electrical Activity of the Hippocampus and Neocortex from the Adult Rat. bioRxiv, 2025-11.
104. Janata, P. (2009). The neural architecture of music-evoked autobiographical memories. Cerebral Cortex.
105. Gupta, A., Srivastava, C. K., Bhushan, B., & Behera, L. (2025). A comparative study of EEG microstate dynamics during happy and sad music videos. Frontiers in Human Neuroscience, 18, 1469468.
106. Schaefer, R. S., & Vlek, R. J. (2019). EEG-based neurofeedback training of alpha-band coherence enhances motor learning. Journal of Neural Engineering.
107. Altenmüller, E., Marco-Pallares, J., Münte, T. F., & Schneider, S. (2009). Neural reorganization underlies improvement in stroke-induced motor dysfunction by music-supported therapy. Annals of the New York Academy of Sciences.
108. Müller, V., & Lindenberger, U. (2011). Cardiac and respiratory patterns synchronize between persons during choir singing.
109. Murgia, M., Santoro, I., Tamburini, G., Prpic, V., Sors, F., Galmonte, A., & Agostini, T. (2016). Rhythmic auditory stimulation (RAS) and motor rehabilitation in Parkinson's disease: New frontiers in assessment and intervention protocols. European Journal of Physical and Rehabilitation Medicine.
110. Pasiali, V., & LaGasse, A. B. (2018). The use of music therapy to address the suffering in advanced cancer pain. Music Therapy Perspectives.
111. Hjorth, B. (1970). EEG analysis based on time domain properties. Electroencephalography and Clinical Neurophysiology.
112. Koelsch, S. (2014). Brain correlates of music-evoked emotions. Nature Reviews Neuroscience.
113. Huang, S., Jin, Z., Li, D., Han, J., & Tao, X. (2025, April). Multimodal fusion for eeg emotion recognition in music with a multi-task learning framework. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-2). IEEE.
114. Thaut, M. H., & Hoemberg, V. (2014). Handbook of neurologic music therapy. Oxford University Press.
115. Grahn, J. A., & Brett, M. (2007). Rhythm and beat perception in motor areas of the brain. Journal of Cognitive Neuroscience.
116. Münte, T. F., Altenmüller, E., & Jäncke, L. (2002). The musician's brain as a model of neuroplasticity. Nature Reviews Neuroscience.
117. Zhang J, Huang Y, Dong Y, Li J, Zhu L and Zhao M (2024) The effect of music tempo on movement flow. Front. Psychol. 15:1292516. doi: 10.3389/fpsyg.2024.1292516.
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