Abstract
This paper measured the bio-signals and liking of the audience watching a musical show, and then conducted machine learning training using this data. Subsequently, the trained machine learning model was utilized to predict the audience’s liking for a musical show. As a result, it was possible to achieve a prediction accuracy of 74.38%. Through additional analysis, it was confirmed that the highest prediction accuracy could be achieved when predicting the audience’s liking for the musical show using support vector machine (SVM) and utilizing pupil and facial expression data.
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References
Affectiva (2024). https://www.affectiva.com/. Accessed 15 Mar 2024
Emotiv epoc x - 14 channel wireless EEG headset (2024). https://www.emotiv.com/epoc-x/. Accessed 15 Mar 2024
Empatica e4 wristband (2024). https://www.empatica.com/en-int/research/e4/. Accessed 15 Mar 2024
Alarcao, S.M., Fonseca, M.J.: Emotions recognition using EEG signals: a survey. IEEE Trans. Affect. Comput. 10(3), 374–393 (2017)
Alhagry, S., Fahmy, A.A., El-Khoribi, R.A.: Emotion recognition based on EEG using LSTM recurrent neural network. Int. J. Adv. Comput. Sci. Appl. 8(10) (2017)
Amjadzadeh, M., Ansari-Asl, K.: An innovative emotion assessment using physiological signals based on the combination mechanism. Sci. Iranica 24(6), 3157–3170 (2017)
Chen, W., Jaques, N., Taylor, S., Sano, A., Fedor, S., Picard, R.W.: Wavelet-based motion artifact removal for electrodermal activity. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6223–6226. IEEE (2015)
Choi, S.W., Lee, C., Lee, J.M., Park, J.H., Lee, I.B.: Fault detection and identification of nonlinear processes based on kernel PCA. Chemom. Intell. Lab. Syst. 75(1), 55–67 (2005)
Egger, M., Ley, M., Hanke, S.: Emotion recognition from physiological signal analysis: a review. Electron. Notes Theor. Comput. Sci. 343, 35–55 (2019)
Gupta, R., Falk, T.H., et al.: Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization. Neurocomputing 174, 875–884 (2016)
Kassner, M., Patera, W., Bulling, A.: Pupil: an open source platform for pervasive eye tracking and mobile gaze-based interaction. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, pp. 1151–1160 (2014)
Koelstra, S., et al.: DEAP: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2011)
Moon, S.E., Jang, S., Lee, J.S.: Evaluation of preference of multimedia content using deep neural networks for electroencephalography. In: 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX), pp. 1–6. IEEE (2018)
Savitzky, A., Golay, M.J.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627–1639 (1964)
Schittenkopf, C., Deco, G., Brauer, W.: Two strategies to avoid overfitting in feedforward networks. Neural Netw. 10(3), 505–516 (1997)
Xu, H., Plataniotis, K.N.: Affective states classification using EEG and semi-supervised deep learning approaches. In: 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6. IEEE (2016)
Zhuang, X., Rozgić, V., Crystal, M.: Compact unsupervised eeg response representation for emotion recognition. In: IEEE-EMBS international conference on Biomedical and Health Informatics (BHI). pp. 736–739. IEEE (2014)
Acknowledgment
This research was supported by Culture, Sports and Tourism R &D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture, Sports and Tourism in 2024 (Project Name: Real-time feedback visualization and multisensory performance technology development using performer-audience emotional state information, Project Number: RS-2023-00219678, Contribution Rate: 100%).
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Lee, CG., Kwon, O. (2024). Prediction of a Musical Show Liking Using Bio-signals of an Audience. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2024 Posters. HCII 2024. Communications in Computer and Information Science, vol 2116. Springer, Cham. https://doi.org/10.1007/978-3-031-61950-2_27
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DOI: https://doi.org/10.1007/978-3-031-61950-2_27
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