Journal of the Architectural Institute of Korea (대한건축학회논문집)

Architectural Institute of Korea (대한건축학회)
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EEG-Based Emotional Lighting Control for Improving User Concentration in Spatial Design

공간 사용자의 주의집중도 향상을 위한 EEG 기반 감성조명 제어

  • Received : 2022.10.11
  • Accepted : 2023.02.09
  • Published : 2023.03.30
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Abstract

An intelligent system that takes into consideration both rational and emotional needs has the potential to create an optimized environment for specific conditions and enhance the understanding of the characteristics of diverse users. In this study, a sustainable lighting process is proposed that employs emotional lighting to sustain the user's focused state by analyzing real-time biometric data acquired through EEG and considering the correlation between illumination and concentration(ENG, engagement). Additionally, the user-centered emotional lighting process, which integrates EEG and lighting sensor technology, was analyzed and compared to the prevailing lighting standards by means of experiments. This research is significant as it offers a new analytical approach to sustainable design research that is customized to the requirements of architectural space users.

Keywords

Acknowledgement

이 연구는 2022년도 한국연구재단 연구비 지원에 의한 결과의 일부임. 과제번호:2022R1A2C3011796

References

  1. Alexandrova, S., Tatlock, Z., & Cakmak, M. (2015). RoboFlow: A flow-based visual programming language for mobile manipulation tasks. 2015 IEEE International Conference on Robotics and Automation (ICRA), 5537-5544.
  2. Borisov, V., Syskov, A.., & Kublanov, V. (2019). Functional state assessment of an athlete by means of the brain-computer interface multimodal metrics. World Congress on Medical Physics and Biomedical Engineering 2018, 68(3), 71-75.
  3. Browarska, N., Kawala-Sterniuk, A., Zygarlicki, J., Podpora, M., Pelc, M., Martinek, R., & Gorzelanczyk, E. (2021). Comparison of smoothing filters' influence on quality of data recorded with the Emotiv EPOC Flex brain-computer interface headset during audio stimulation. Brain Sciences, 11(1), 98.
  4. Carrillo, I., Meza-Kubo, V., Moran, A.L., Galindo, G., & Garcia-Canseco, E. (2015). Processing EEG signals towards the construction of a user experience assessment method. AmIHEALTH 2015: Ambient Intelligence for Health, 9456, 281-292.
  5. Chae, J. (2014). Changes in EEG according to attention and concentration training programs with performed difference tasks. PNF and Movement, 12(2), 97-106. https://doi.org/10.21598/JKPNFA.2014.12.2.97
  6. Chai, J., Ge, Y., Liu, Y., Li, W., Zhou, L., Yao, L., & Sun, X. (2014). Application of frontal EEG asymmetry to user experience research. Engineering Psychology and Cognitive Ergonomics, 8532, 234-243. https://doi.org/10.1007/978-3-319-07515-0_24
  7. Chen, Z., & Lin, L. (2019) Emotional experience evaluation method of interaction task based on EEG technology. IOP Conference Series: Materials Science and Engineering, 573, 12-22.
  8. Choi, T., & Lee, G. (2017). Development of cookware product design converging with eco-friendly food culture contents. Journal of the Korea Convergence Society, 8(7), 167-173. https://doi.org/10.15207/JKCS.2017.8.7.167
  9. Csapo, G. (2019). Placing event-action-based visual programming in the process of computer science education. Acta Polytechnica Hungarica, 16(2), 35-57.
  10. Das, P., Khasnobish, A., & Tibarewala, D.N. (2016). Emotion recognition employing ECG and GSR signals as markers of ANS. 2016 Conference on Advances in Signal Processing (CASP), 37-42.
  11. Fouad, I. (2021) A robust and reliable online P300-based BCI system using Emotiv EPOC+ headset. Journal of Medical Engineering & Technology, 45(2), 94-114. https://doi.org/10.1080/03091902.2020.1853840
  12. Ekanayake, H. (2010). P300 and Emotiv EPOC: Does Emotiv EPOC capture real EEG?. Web publication http://neurofeedback.visaduma.info/emotivresearch.htm, 133.
  13. Emotiv. (n.d.). Retrieved September 30, 2022, from https://www.emotiv.com/
  14. EmotivBCI Toolbox. (n.d.). Retrieved September 30, 2022, from https://emotiv.gitbook.io/emotivbci-node-red-toolbox
  15. EmotivPRO v3.0. (n.d.). Performance Metrics. Retrieved September 30, 2022, from https://www.emotiv.com/knowledge-base/performance-metrics
  16. Holman, M., & Adebesin, F. (2019). Taking the subjectivity out of UX evaluation with Emotiv EPOC+. Association for Computing Machinery, 30, 1-10.
  17. Kang, S., Ji, S., & Jun, H. (2019). Proposal of IoT illumination control emotional-lighting based on EEG using Visual Programming Language(VPL). Proceeding of Annual Conference of the Architectural Institute of Korea, 2019, 39(1), 96-99.
  18. Kim, D., Ahn, S., & Park, S. (2013). Whang, M. Interactive emotional lighting system using physiological signals. IEEE Transactions on Consumer Electronics, 59(4), 765-771. https://doi.org/10.1109/TCE.2013.6689687
  19. Kim, M., Cheon, S., & Kang, Y. (2019). Use of Electroencephalography (EEG) for the analysis of emotional perception and fear to nightscapes. Sustainability, 11(1), 233.
  20. Kiper, J.D., Howard, E., & Ames, C. (1997). Criteria for evaluation of visual programming languages. Journal of Visual Languages & Computing, 8(2), 175-192. https://doi.org/10.1006/jvlc.1996.0034
  21. Kosmyna, N., & Maes, P. (2019). An EEG-based closed-loop biofeedback system for real-time monitoring and improvement of engagement for personalized learning. Sensors, 19(23), 5200.
  22. Kotowski, K., Stapor, K., Leski, J., & Kotas, M. (2018). Validation of Emotiv EPOC+ for extracting ERP correlates of emotional face processing. Biocybernetics and Biomedical Engineering, 38(4), 773-781. https://doi.org/10.1016/j.bbe.2018.06.006
  23. KS A 3011. (2018). Recommended Levels of Illumination. Retrieved September 30, 2022, from https://e-ks.kr/streamdocs/view/sd,streamdocsId=72059197289337820
  24. KS C 7612. (2022). Illuminance measurements for lighting installations. Retrieved September 30, 2022, from https://e-ks.kr/streamdocs/view/sd,streamdocsId=72059256637734317
  25. Lee, C., & Ryu, G. (2016). The effect of personalized LED light on reduction for eye fatigue : A EEG analysis. The Korean Journal of Vision Science, 18(3), 403-412. https://doi.org/10.17337/JMBI.2016.18.3.403
  26. Lee, E., & Suk, H. (2012). The emotional response to lighting hue focusing on relaxation and attention. Journal of Korean Society of Design Science, 25(2), 27-39.
  27. Li, X., Yu, L., Zhang, Y., Shao, Z., Huang, L., Zhang, C., & He, X. (2019). Emotional feedback lighting control system based on face recognition. Social Informatics and Telecommunications Engineering, 282, 258-266.
  28. Lim, S., & Oh, S. (2013). Design of RBFNN-based emotional lighting system using RGBW LED. The Transactions of the Korean Institute of Electrical Engineers, 2013, 62(5), 696-704. https://doi.org/10.5370/KIEE.2013.62.5.696
  29. Lim, S., Yeo, M., & Yoon, G. (2019). Comparison between concentration and immersion based on EEG analysis. Sensors (Basel, Switzerland), 19(7), 1669. https://doi.org/10.3390/s19071669
  30. Mohammadi, Z., Frounchi, J., & Amiri, M. (2017). Wavelet-based emotion recognition system using EEG signal. Neural Computing and Applications, 28, 1985-1990. https://doi.org/10.1007/s00521-015-2149-8
  31. Node-Red. (n.d). Retrieved September 30, 2022, from https://nodered.org/
  32. No, S. (2005). Emotional lighting system by Feelux. KSDS Conference Proceeding, 86-87.
  33. Park, J., & Kim, J. (2016). Activation of EEG concentration on selecting seats in cafe space. Journal of Digital Design 2016, 16(4), 221-229.
  34. Park, J., Lee, H., Choi, K., & Lee, C. (2017). A Study on psychological reaction and visibility consequent on illuminance and color temperature. Journal of Korea Society of Color Studies, 31(2), 129-136. https://doi.org/10.17289/jkscs.31.2.201705.129
  35. Park, J., Xie, W., Ro, H., & Kim, W. (2018). The possibilities in craft creation through convergence. Journal of the Korea Convergence Society, 9(1), 51-58. https://doi.org/10.15207/JKCS.2018.9.1.051
  36. Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019). Deep learning-based electroencephalography analysis: a systematic review. Journal of Neural Engineering, 16(5).
  37. Shin, J., Chun, S., & Lee, C. (2013). Analysis of the effect on attention and relaxation level by correlated color temperature and illuminance of LED lighting using EEG signal. Journal of the Korean Institute of illuminating and Electrical Installation Engineers, 27(5), 9-17. https://doi.org/10.5207/JIEIE.2013.27.5.009
  38. Sim, K., Yeom, H., & Lee. I. (2008). EEG signals measurement and analysis method for brain-computer interface. Journal of Korean Institute of Intelligent Systems, 18(5), 605-610. https://doi.org/10.5391/JKIIS.2008.18.5.605
  39. Taylor, G. S., & Schmidt, C. (2012). Empirical evaluation of the Emotiv EPOC BCI headset for the detection of mental action. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 56(1), 193-197. https://doi.org/10.1177/1071181312561017
  40. Timothy, M., Ian, P., & Thomas, P. (2015). Modality specific assessment of video game player's experience using the Emotiv. Entertainment Computing, 7, 1-6. https://doi.org/10.1016/j.entcom.2015.03.001
  41. Williams, N. S., King, W., Mackellar, G., Randeniya, R., McCormick, A., & Badcock, N. A. (2022). Crowdsourced EEG Experiments: A proof of concept for remote EEG acquisition using EmotivPRO Builder and EmotivLABS. bioRxiv, 2022-02.
  42. Yun, S., & Lin, C. (2015). Design and implementation of optimal LED emotional-lighting control system. The Journal of Korean Institute of Communications and Information Sciences, 40(8), 1637-1642. https://doi.org/10.7840/kics.2015.40.8.1637
  43. Zhang, X., Wu, L., Xu, S., Kang, Y., & Zeng, Q. (2020). Sitting or Walking? Analyzing the neural emotional indicators of urban green space behavior with mobile EEG. Journal of urban health : bulletin of the New York Academy of Medicine, 97(2), 191-203.