Environmental modulation of musical emotion: frequency-specific analysis based on virtual reality and electroencephalography_Journal of Biomedical Engineering

Authors：

ZHOU Yanbin ^1,2 , HUANG Min ³ ,  JIANG Leqi ^1,2 , WANG Mingxun ³ , GAO Tingting ³ , CHEN Junming ³ , JIN Yanjun ^1,2

1. Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang 330063, P. R. China;
2. College of Information Engineering, Nanchang Hangkong University, Nanchang 330063, P. R. China;
3. School of Aviation Services and Music, Nanchang Hangkong University, Nanchang 330063, P. R. China;

Corresponding?author：

JIANG Leqi, Email: jdz_jlq@nchu.edu.cn

Keywords：

Musical emotion perception; Virtual reality; Electroencephalography signals; Multi-band feature analysis

DOI：

10.7507/1001-5515.202506053

Video：

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Abstract Full text Figures/Tables Video References Cited by

Musical emotion perception, as a key pathway to decoding the essence of human emotions, requires the analysis of its regulatory mechanisms for the development of precise neural modulation strategies. Although electroencephalography (EEG) signals can be used to capture the dynamic neural activities associated with music emotion processing, and virtual reality (VR) technology can offer immersive enhancement effects for emotion regulation, the interaction mechanism among the VR environment, music emotion and neural activity remains unclear. This study established a multimodal experimental paradigm of “VR environment-music stimulation-EEG response” and employed multi-band feature analysis to systematically elucidate the neural dynamics patterns of musical emotion perception during transitions between different virtual scenarios. The results demonstrated that the right temporal lobe exhibited significant electrophysiological changes when comparing real and virtual scenarios, while posterior brain regions were sensitive to differences in virtual environments. Furthermore, the environment exerted specific modulation on both low-frequency and high-frequency EEG activities, with the δ energy percentage demonstrating a context-dependent differentiation in music emotion perception. This study, through virtual scenario-modulated music emotion perception experiments, systematically reveals the frequency-band-specific modulation effects of environmental factors on music emotion, establishes the energy ratio of the δ band as a key biomarker for environment-emotion interaction, and provides an important theoretical basis and quantitative assessment methods for the development of immersive emotion regulation strategies and clinical psychological interventions.

Citation： ZHOU Yanbin, HUANG Min, JIANG Leqi, WANG Mingxun, GAO Tingting, CHEN Junming, JIN Yanjun. Environmental modulation of musical emotion: frequency-specific analysis based on virtual reality and electroencephalography. Journal of Biomedical Engineering, 2026, 43(1): 61-69, 78. doi: 10.7507/1001-5515.202506053 Copy

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2.	Hofbauer L M, Rodriguez F S. Emotional valence perception in music and subjective arousal: experimental validation of stimuli. International Journal of Psychology, 2023, 58(5): 465-475..
3.	宓洪挺, 呂曉, 李新科. 不同情緒特征的音樂聯合經顱磁刺激對意識障礙患者神經功能恢復的評估. 中國康復, 2023, 38(8): 483-485..
4.	Cui X, Wu Y, Wu J, et al. A review: music-emotion recognition and analysis based on EEG signals. Frontiers in Neuroinformatics, 2022, 16: 997282..
5.	Sammler D, Grigutsch M, Fritz T, et al. Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music. Psychophysiology, 2007, 44(2): 293-304..
6.	Poikonen H, Alluri V, Brattico E, et al. Event-related brain responses while listening to entire pieces of music. Neuroscience, 2016, 312: 58-73..
7.	Sakharov D S, Davydov V I, Pavlygina R A. Intercentral relations of the human EEG during listening to music. Human Physiology, 2005, 31: 392-397..
8.	鄒楊萌, 揭麗琳, 王銘勛, 等. 基于腦電和眼動信號的動態連續情緒識別方法. 生物醫學工程學雜志, 2025, 42(1): 32-41..
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14.	楊文陽, 徐可欣. 聯合虛擬現實環境和腦電信號的情緒識別研究進展. 生物醫學工程學雜志, 2024, 41(2): 389-397..
15.	Li J, Wu W, Jin Y, et al. Research on environmental comfort and cognitive performance based on EEG+VR+LEC evaluation method in underground space. Building and Environment, 2021, 198: 107886..
16.	Koelsch S. Towards a neural basis of music-evoked emotions. Trends in Cognitive Sciences, 2010, 14(3): 131-137..
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18.	Vieillard S, Peretz I, Gosselin N, et al. Happy, sad, scary and peaceful musical excerpts for research on emotions. Cognition and Emotion, 2007, 22(4): 720-752..
19.	Salimpoor V N, Benovoy M, Larcher K, et al. Anatomically distinct dopamine release during anticipation and experience of peak emotion to music. Nature neuroscience, 2011, 14(2): 257-262..
20.	Bar-Yoshafat Y. On the musically melancholic: temporality and affects in Western music history. Journal of Aesthetic Education, 2020, 54(4): 93-110..
21.	Ilie G, Thompson W F. A comparison of acoustic cues in music and speech for three dimensions of affect. Music Perception, 2006, 23(4): 319-330..
22.	Bradley M M, Lang P J. Measuring emotion: the self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 1994, 25(1): 49-59..
23.	Li J, Jin Y, Lu S, et al. Building environment information and human perceptual feedback collected through a combined virtual reality (VR) and electroencephalogram (EEG) method. Energy and Buildings, 2020, 224: 110259..
24.	Abromavi?ius V, Serackis A. Eye and EEG activity markers for visual comfort level of images. Biocybernetics and Biomedical Engineering, 2018, 38(4): 810-818..
25.	Abenna S, Nahid M, Bouyghf H, et al. EEG-based BCI: a novel improvement for EEG signals classification based on real-time preprocessing. Computers in Biology and Medicine, 2022, 148: 105931..
26.	Yan W, He B, Zhao J, et al. Frequency domain filtering method for SSVEP-EEG preprocessing. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, 31: 2079-2089..
27.	Bigdely-Shamlo N, Mullen T, Kothe C, et al. The PREP pipeline: standardized preprocessing for large-scale EEG analysis. Frontiers in Neuroinformatics, 2015, 9: 16..
28.	Picton T W, Bentin S, Berg P, et al. Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria. Psychophysiology, 2000, 37(2): 127-152..
29.	Pernet C R, Appelhoff S, Gorgolewski K J, et al. EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 2019, 6(1): 103..
30.	Widmann A, Schr?ger E, Maess B. Digital filter design for electrophysiological data-a practical approach. Journal of Neuroscience Methods, 2015, 250: 34-46..
31.	Li J, Zhang Z, He H. Hierarchical convolutional neural networks for EEG-based emotion recognition. Cognitive Computation, 2018, 10: 368-380..
32.	Oken B S, Salinsky M C, Elsas S M. Vigilance, alertness, or sustained attention: physiological basis and measurement. Clinical Neurophysiology, 2006, 117(9): 1885-1901..
33.	Zhang Y, Liu H, Zhang D, et al. EEG-based emotion recognition with emotion localization via hierarchical self-attention. IEEE Transactions on Affective Computing, 2023, 14(3): 2458-2469..
34.	Alarc?o S M, Fonseca M J. Emotions recognition using EEG signals: asurvey. IEEE Transaction on Affective Computing, 2019, 10(3): 374-393..
35.	張冠華, 余旻婧, 陳果, 等. 面向情緒識別的腦電特征研究綜述. 中國科學(信息科學), 2019, 49(9): 1097-1118..
36.	Stam C J, Nolte G, Daffertshofer A. Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human Brain Mapping, 2007, 28: 1178-1193..
37.	Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 2010, 52(3): 1059-1069..
38.	Ballerini A, Tondelli M, Talami F, et al. Amygdala subnuclear volumes in temporal lobe epilepsy with hippocampal sclerosis and in non-lesional patients. Brain Communications, 2022, 4(5): fcac225..
39.	Rolls E T. Hippocampal spatial view cells for memory and navigation, and their underlying connectivity in humans. Hippocampus, 2023, 33(5): 533-572..
40.	Maris E, Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods, 2007, 164(1): 177-190..
41.	Magosso E, Borra D. The strength of anticipated distractors shapes EEG alpha and theta oscillations in a working memory task. NeuroImage, 2024, 300: 120835..

1. Tan M, Zhou X, Shen L, et al. Music’s dual role in emotion regulation: network analysis of music use, emotion regulation self-efficacy, alexithymia, anxiety, and depression. Depression and Anxiety, 2024, 2024: 1790168..
2. Hofbauer L M, Rodriguez F S. Emotional valence perception in music and subjective arousal: experimental validation of stimuli. International Journal of Psychology, 2023, 58(5): 465-475..
3. 宓洪挺, 呂曉, 李新科. 不同情緒特征的音樂聯合經顱磁刺激對意識障礙患者神經功能恢復的評估. 中國康復, 2023, 38(8): 483-485..
4. Cui X, Wu Y, Wu J, et al. A review: music-emotion recognition and analysis based on EEG signals. Frontiers in Neuroinformatics, 2022, 16: 997282..
5. Sammler D, Grigutsch M, Fritz T, et al. Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music. Psychophysiology, 2007, 44(2): 293-304..
6. Poikonen H, Alluri V, Brattico E, et al. Event-related brain responses while listening to entire pieces of music. Neuroscience, 2016, 312: 58-73..
7. Sakharov D S, Davydov V I, Pavlygina R A. Intercentral relations of the human EEG during listening to music. Human Physiology, 2005, 31: 392-397..
8. 鄒楊萌, 揭麗琳, 王銘勛, 等. 基于腦電和眼動信號的動態連續情緒識別方法. 生物醫學工程學雜志, 2025, 42(1): 32-41..
9. 孫葉菁, 馮珍. 事件相關電位成分失匹配負波和P300在神經精神系統疾病中的臨床應用研究進展. 中國康復醫學雜志, 2024, 39(7): 1054-1059..
10. Hou J C, Song B, Chen A C N, et al. Review on neural correlates of emotion regulation and music: implications for emotion dysregulation. Frontiers in Psychology, 2017, 8: 501..
11. Honzel E, Murthi S, Brawn-Cinani B, et al. Virtual reality, music, and pain: developing the premise for an interdisciplinary approach to pain management. Pain, 2019, 160(9): 1909-1919..
12. Lin X, Mahmud S, Jones E, et al. Virtual reality-based musical therapy for mental health management//2020 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas: IEEE, 2020: 0948-0952..
13. Hao T, Pang J, Liu Q, et al. A systematic review and network meta-analysis of virtual reality, audiovisuals and music interventions for reducing dental anxiety related to tooth extraction. BMC Oral Health, 2023, 23(1): 684..
14. 楊文陽, 徐可欣. 聯合虛擬現實環境和腦電信號的情緒識別研究進展. 生物醫學工程學雜志, 2024, 41(2): 389-397..
15. Li J, Wu W, Jin Y, et al. Research on environmental comfort and cognitive performance based on EEG+VR+LEC evaluation method in underground space. Building and Environment, 2021, 198: 107886..
16. Koelsch S. Towards a neural basis of music-evoked emotions. Trends in Cognitive Sciences, 2010, 14(3): 131-137..
17. Eysenck S B, Eysenck H J, Barrett P. A revised version of the psychoticism scale. Personality and Individual Differences, 1985, 6(1): 21-29..
18. Vieillard S, Peretz I, Gosselin N, et al. Happy, sad, scary and peaceful musical excerpts for research on emotions. Cognition and Emotion, 2007, 22(4): 720-752..
19. Salimpoor V N, Benovoy M, Larcher K, et al. Anatomically distinct dopamine release during anticipation and experience of peak emotion to music. Nature neuroscience, 2011, 14(2): 257-262..
20. Bar-Yoshafat Y. On the musically melancholic: temporality and affects in Western music history. Journal of Aesthetic Education, 2020, 54(4): 93-110..
21. Ilie G, Thompson W F. A comparison of acoustic cues in music and speech for three dimensions of affect. Music Perception, 2006, 23(4): 319-330..
22. Bradley M M, Lang P J. Measuring emotion: the self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 1994, 25(1): 49-59..
23. Li J, Jin Y, Lu S, et al. Building environment information and human perceptual feedback collected through a combined virtual reality (VR) and electroencephalogram (EEG) method. Energy and Buildings, 2020, 224: 110259..
24. Abromavi?ius V, Serackis A. Eye and EEG activity markers for visual comfort level of images. Biocybernetics and Biomedical Engineering, 2018, 38(4): 810-818..
25. Abenna S, Nahid M, Bouyghf H, et al. EEG-based BCI: a novel improvement for EEG signals classification based on real-time preprocessing. Computers in Biology and Medicine, 2022, 148: 105931..
26. Yan W, He B, Zhao J, et al. Frequency domain filtering method for SSVEP-EEG preprocessing. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, 31: 2079-2089..
27. Bigdely-Shamlo N, Mullen T, Kothe C, et al. The PREP pipeline: standardized preprocessing for large-scale EEG analysis. Frontiers in Neuroinformatics, 2015, 9: 16..
28. Picton T W, Bentin S, Berg P, et al. Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria. Psychophysiology, 2000, 37(2): 127-152..
29. Pernet C R, Appelhoff S, Gorgolewski K J, et al. EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 2019, 6(1): 103..
30. Widmann A, Schr?ger E, Maess B. Digital filter design for electrophysiological data-a practical approach. Journal of Neuroscience Methods, 2015, 250: 34-46..
31. Li J, Zhang Z, He H. Hierarchical convolutional neural networks for EEG-based emotion recognition. Cognitive Computation, 2018, 10: 368-380..
32. Oken B S, Salinsky M C, Elsas S M. Vigilance, alertness, or sustained attention: physiological basis and measurement. Clinical Neurophysiology, 2006, 117(9): 1885-1901..
33. Zhang Y, Liu H, Zhang D, et al. EEG-based emotion recognition with emotion localization via hierarchical self-attention. IEEE Transactions on Affective Computing, 2023, 14(3): 2458-2469..
34. Alarc?o S M, Fonseca M J. Emotions recognition using EEG signals: asurvey. IEEE Transaction on Affective Computing, 2019, 10(3): 374-393..
35. 張冠華, 余旻婧, 陳果, 等. 面向情緒識別的腦電特征研究綜述. 中國科學(信息科學), 2019, 49(9): 1097-1118..
36. Stam C J, Nolte G, Daffertshofer A. Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human Brain Mapping, 2007, 28: 1178-1193..
37. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 2010, 52(3): 1059-1069..
38. Ballerini A, Tondelli M, Talami F, et al. Amygdala subnuclear volumes in temporal lobe epilepsy with hippocampal sclerosis and in non-lesional patients. Brain Communications, 2022, 4(5): fcac225..
39. Rolls E T. Hippocampal spatial view cells for memory and navigation, and their underlying connectivity in humans. Hippocampus, 2023, 33(5): 533-572..
40. Maris E, Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods, 2007, 164(1): 177-190..
41. Magosso E, Borra D. The strength of anticipated distractors shapes EEG alpha and theta oscillations in a working memory task. NeuroImage, 2024, 300: 120835..

Journal of Biomedical Engineering

Environmental modulation of musical emotion: frequency-specific analysis based on virtual reality and electroencephalography

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