Multimodal Opera Performance Form Based on Human-Computer Interaction Technology
“Audience Engagement (AE)” describes how a stage performance affects the audience’s thoughts, provokes a bodily response, and spurs cognitive growth. With little audience involvement, theatre performing arts like opera typically have difficulty keeping audiences’ attention. The brain-computer intera...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2022/4003245 |
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| author | Lin Wu |
| author_facet | Lin Wu |
| author_sort | Lin Wu |
| collection | DOAJ |
| description | “Audience Engagement (AE)” describes how a stage performance affects the audience’s thoughts, provokes a bodily response, and spurs cognitive growth. With little audience involvement, theatre performing arts like opera typically have difficulty keeping audiences’ attention. The brain-computer interaction (BCI) technology could be used in opera performances to alter the audience’s emotional experience. Nevertheless, for such BCI systems to function, they must accurately identify their participants’ present emotional states. Although difficult to evaluate, audience participation is a vital sign of how well an opera performs. Practical methodological approaches for real-time perception and comprehension of audience emotions include psychological and physiological assessments. Hence, a multimodal emotional state detection technique (MESDT) for enhancing the AE in opera performance using BCI has been proposed. Three essential steps make up a conceptual MESDT architecture. An electroencephalogram (EEG) and other biological signs from the audience are first captured. Second, the acquired signals are processed, and the BCI tries to determine the user’s present psychological response. Third, an adaptive performance stimulus (APS) is triggered to enhance AE in opera performance, as determined by a rule base. To give the opera audience a high-quality viewing experience, the immersive theatre performance has been simulated. Fifty individuals have been used in the experimental assessment and performance studies. The findings demonstrated that the proposed technology had been able to accurately identify the decline in AE and that performing stimuli had a good impact on enhancing AE during an opera performance. It has been further shown that the suggested design improves the overall performance of AE by 5.8% when compared to a typical BCI design (one that uses EEG characteristics solely) for the proposed MESDT framework with BCI. |
| format | Article |
| id | doaj-art-6766b1093d694dc99129ff04c534d8d0 |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-6766b1093d694dc99129ff04c534d8d02025-02-03T05:57:31ZengWileyInternational Transactions on Electrical Energy Systems2050-70382022-01-01202210.1155/2022/4003245Multimodal Opera Performance Form Based on Human-Computer Interaction TechnologyLin Wu0College of Music Education“Audience Engagement (AE)” describes how a stage performance affects the audience’s thoughts, provokes a bodily response, and spurs cognitive growth. With little audience involvement, theatre performing arts like opera typically have difficulty keeping audiences’ attention. The brain-computer interaction (BCI) technology could be used in opera performances to alter the audience’s emotional experience. Nevertheless, for such BCI systems to function, they must accurately identify their participants’ present emotional states. Although difficult to evaluate, audience participation is a vital sign of how well an opera performs. Practical methodological approaches for real-time perception and comprehension of audience emotions include psychological and physiological assessments. Hence, a multimodal emotional state detection technique (MESDT) for enhancing the AE in opera performance using BCI has been proposed. Three essential steps make up a conceptual MESDT architecture. An electroencephalogram (EEG) and other biological signs from the audience are first captured. Second, the acquired signals are processed, and the BCI tries to determine the user’s present psychological response. Third, an adaptive performance stimulus (APS) is triggered to enhance AE in opera performance, as determined by a rule base. To give the opera audience a high-quality viewing experience, the immersive theatre performance has been simulated. Fifty individuals have been used in the experimental assessment and performance studies. The findings demonstrated that the proposed technology had been able to accurately identify the decline in AE and that performing stimuli had a good impact on enhancing AE during an opera performance. It has been further shown that the suggested design improves the overall performance of AE by 5.8% when compared to a typical BCI design (one that uses EEG characteristics solely) for the proposed MESDT framework with BCI.http://dx.doi.org/10.1155/2022/4003245 |
| spellingShingle | Lin Wu Multimodal Opera Performance Form Based on Human-Computer Interaction Technology International Transactions on Electrical Energy Systems |
| title | Multimodal Opera Performance Form Based on Human-Computer Interaction Technology |
| title_full | Multimodal Opera Performance Form Based on Human-Computer Interaction Technology |
| title_fullStr | Multimodal Opera Performance Form Based on Human-Computer Interaction Technology |
| title_full_unstemmed | Multimodal Opera Performance Form Based on Human-Computer Interaction Technology |
| title_short | Multimodal Opera Performance Form Based on Human-Computer Interaction Technology |
| title_sort | multimodal opera performance form based on human computer interaction technology |
| url | http://dx.doi.org/10.1155/2022/4003245 |
| work_keys_str_mv | AT linwu multimodaloperaperformanceformbasedonhumancomputerinteractiontechnology |