| id |
caadria2024_339 |
| authors |
Kang, Se Yeon, Cho, Ju Eun and Jun, Han Jong |
| year |
2024 |
| title |
Electroencephalogram (EEG) based Emotional Lighting Design Using Deep-Learning for a User-Centric Approach |
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 3, pp. 391–400 |
| doi |
https://doi.org/10.52842/conf.caadria.2024.3.391
|
| summary |
This study proposes a methodology for using artificial intelligence (AI) and biometrics in spatial design. The research mainly applies a gated recurrent unit (GRU) model, a recurrent neural network (RNN), to analyze electroencephalogram (EEG) data and dynamically adjust lighting according to the user's emotional state. This study suggests an illumination adjustment system that modifies lighting according to the user's emotional state using the proposed method. Integration of EEG data can overcome the limitations of lighting systems. It can effectively target individual emotional responses. The GRU model represents a significant improvement in lighting design by addressing both cognitive and emotional user needs. The model's effectiveness in processing real-time data and adapting through incremental learning was evaluated. The model has shown a significant impact on emotional architecture and spatial design, with a focus on individual experience. |
| keywords |
Gated Recurrent Unit, EEG, EEG Data Analysis, User-Centric Design, Emotional Lighting, Real-Time Data Processing, Affective Computing, BCI, BMI |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (1,504,226 bytes) |
| references |
Content-type: text/plain
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Abumohsen, M., Owda, A. Y., & Owda, M. (2023)
 Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms
, Energies, 16(5), Article 2283. https://doi.org/10.3390/en16052283
|
|
|
|
|
Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M., & Assoc Comp, M. (2019)
Optuna: A Next-generation Hyperparameter Optimization Framework
, [Kdd'19: Proceedings of the 25th acm sigkdd international conferencce on knowledge discovery and data mining]. 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), Anchorage, AK
|
|
|
|
|
Craik, A., He, Y. T., & Contreras-Vidal, J. L. (2019)
Deep learning for electroencephalogram (EEG) classification tasks: a review
, Journal of Neural Engineering, 16(3), Article 031001. https://doi.org/10.1088/1741-2552/ab0ab5
|
|
|
|
|
Emotiv. (2023)
Does EmotivPro automatically remove artifacts from the EEG data collected? Emotiv. Updated from original version of May 31
, EmotivPro V3 Guidebook. Retrieved November 30, 2023, from https://emotiv.gitbook.io/emotivpro-v3/data-streams/performance-metrics
|
|
|
|
|
Eroglu, K., Kayikçioglu, T., & Osman, O. (2020)
Effect of brightness of visual stimuli on EEG signals
, Behavioural Brain Research, 382, Article 112486. https://doi.org/10.1016/j.bbr.2020.112486
|
|
|
|
|
Hsieh, M. C., Zhang, Y. Q., Dong, R., Lee, Y. C., & Ni, C. Y. (2022)
Use of EEG signals, cortisol secretion, and task performance to evaluate the effects of different lighting environments on concentration level in a sustained attention task
, International Journal of Industrial Ergonomics, 92, Article 103371. https://doi.org/10.1016/j.ergon.2022.103371
|
|
|
|
|
Hyseni, L. N., Ibrahimi, A., & Ieee. (2017)
Comparison of the Cloud Computing Platforms Provided by Amazon and Google
, [2017 computing conference]. Computing Conference, United Kingdon, London, ENGLAND
|
|
|
|
|
Kim, J., Hwang, D. U., Son, E. J., Oh, S. H., Kim, W., Kim, Y., & Kwon, G. (2022)
Emotion recognition while applying cosmetic cream using deep learning from EEG data; cross-subject analysis
, Plos One, 17(11), Article e0274203. https://doi.org/10.1371/journal.pone.0274203
|
|
|
|
|
Mai, N. D., Lee, B. G., & Chung, W. Y. (2021)
Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device
, Sensors, 21(15), Article 5135. https://doi.org/10.3390/s21155135
|
|
|
|
|
Murugappan, M., Rizon, M., Nagarajan, R., Yaacob, S., Hazry, D., & Zunaidi, I. (2008)
Time-Frequency Analysis of EEG Signals for Human Emotion Detection
, IFMBE Proceedings [4th kuala lumpur international conference on biomedical engineering 2008, vols 1 and 2]. 4th Kuala Lumpur International Conference on Biomedical Engineering, Kuala Lumpur, MALAYSIA
|
|
|
|
|
Muthuswamy, J., & Thakor, N. V. (1998)
Spectral analysis methods for neurological signals
, Journal of Neuroscience Methods, 83(1), 1-14. https://doi.org/10.1016/s0165-0270(98)00065-x
|
|
|
|
|
Salaken, S. M., Hettiarachchi, I., Crameri, L., Hanoun, S., Nguyen, T., Nahavandi, S., & Ieee. (2020)
Evaluation of Classification Techniques for Identifying Cognitive Load Levels using EEG Signals
, Annual IEEE Systems Conference [2020 14th annual ieee international systems conference (syscon2020)]. 14th Annual IEEE International Systems Conference (SysCon), Electr Network
|
|
|
|
|
Subha, D. P., Joseph, P. K., Acharya, R., & Lim, C. M. (2010)
EEG Signal Analysis: A Survey
, Journal of Medical Systems, 34(2), 195-212. https://doi.org/10.1007/s10916-008-9231-z
|
|
|
|
|
Tanaka, K., Matsunaga, K., & Wang, H. O. (2005)
Electroencephalogram-based control of an electric wheelchair
, Ieee Transactions on Robotics, 21(4), 762-766. https://doi.org/10.1109/tro.2004.842350
|
|
|
|
|
Wahy, N., & Mansor, W. (2010)
EEG based home lighting system
, 2010 International Conference on Computer Applications and Industrial Electronics
|
|
|
|
|
Wilaiprasitporn, T., Ditthapron, A., Matchaparn, K., Tongbuasirilai, T., Banluesombatkul, N., & Chuangsuwanich, E. (2020)
Affective EEG-Based Person Identification Using the Deep Learning Approach
, Ieee Transactions on Cognitive and Developmental Systems, 12(3), 486-496. https://doi.org/10.1109/tcds.2019.2924648
|
|
|
|
|
Wu, Y., Jin, Y. L., Yokoyama, A., Wang, C., Li, Y. F., Liu, Y., Wang, G. X., & Ieee. (2019)
Live Demonstration: An EEG-Based Comparative Study of Light Color Effect on Mental Stress Relief Using an Integrated Mobile Application
, Biomedical Circuits and Systems Conference [2019 ieee biomedical circuits and systems conference (biocas 2019)]. IEEE Biomedical Circuits and Systems Conference (BioCAS), Nara, JAPAN
|
|
|
|
|
Zhang, D., Lindholm, G., & Ratnaweera, H. (2018)
Use long short-term memory to enhance Internet of Things for combined sewer overflow monitoring
, Journal of Hydrology, 556, 409-418. https://doi.org/10.1016/j.jhydrol.2017.11.018
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| last changed |
2024/11/17 22:05 |
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