Abstract
A person’s state of attentiveness can be affected by various outside factors. Having energy, feeling tired, or even simply being distracted all play a role in someone’s level of attention. The task at hand can potentially affect the person’s attention or concentration level as well. In terms of students who take online courses, constantly watching lectures and conducting these courses solely online can cause lack of concentration or attention. Attention can be considered in two categories: passive or active. Conducting active and passive attention-based trials can reveal different states of attentiveness. This paper compares active and passive attention trial results of the two states, wide awake and tired. This has been done in order to uncover a difference in results between the two states. The data analyzed throughout this paper was collected from DSI 24 EEG equipment, and the generated EEG is processed through a 3D Convolutional Neural Network (CNN) to produce results. Three passive attention trials and three active attention trials were performed on seven subjects, while they were wide awake and when they were tired. The experiments on the preprocessed data results in accuracies as high as 81.78% for passive attention detection accuracy and 63.67% for active attention detection accuracy, which shown a clear ability to separate between the two attention categories.
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References
Hassan, R.: Human attention recognition with machine learning from brain-EEG signals. In: 2nd IEEE Eurasia Conference on Biomedical Engineering, Healthcare, and Sustainability (2020)
Alirezaei, M., Sardouie, S.H.: Detection of human attention using EEG signals. In: 24th National and 2nd International Iranican Conference on Biomedical Engineering, pp. 1–5
Zhang, P.: (2019)
Liu, N.: (2013)
Shi, L., Ko, M.L., Ko, G.Y.P.: Retinoschisin facilitates the function of L-type voltage-gated calcium channels (2017)
Sezer, M.: Avârız Kayıtlarına Göre XVII. ve XVIII. Yüzyıllarda Karinabad Kazâsı (2018)
P: (2019). https://news.harvard.edu/gazette/story/2019/09/study-shows-that-students-learn- more-when-taking-part-in-classrooms-that-employ-active-learning-strategies/Accessed
Elsayed, N., Saad, Z., Bayoumi, M.: Brain computer interface: EEG signal preprocessing issues and solutions. Int. J. Comput. Appl. 169(3), 12–16 (2017). https://doi.org/10.5120/ijca2017914621
Roy, Y.: Deep learning-based electroencephalography analysis: a systematic review. J. Nueral Eng.
Motomura, S., Tanaka, H., Nakamura, S.: Sequential attention-based detection of semantic incongruities from EEG while listening to speech. In: 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, pp. 268–271 (2020)
Subramaniyam, N.: (2018). https://sapienlabs.org/pitfalls-of-filtering-the-eeg-signal/
Arvaneh, M., Tanaka, T.: (2018)
Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single- trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004)
Luazon, F.Q.: An introduction to deep learning. In: 11th International Conference on Informa- tion Sciences, Signal Processing and their Applications: Special Sessions (2012)
Gupgta, S., Singh, H.: Preprocessing EEG signals for direct human-system interface. In: Pro- ceedings IEEE International Joint Symposia on Intelligence and Systems (1996)
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Appendix
Appendix
A GitHub repository has been created for the 3DCNN code and data used through- out this study. Please visit the following link to view: https://github.com/mkwarman/A ctive-Passive-Attention-3DCNN-Classification.
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Chakravarty, S., Xie, Y., Le, L., Johnson, J., Hales, M. (2021). Comparison Between Active and Passive Attention Using EEG Waves and Deep Neural Network. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds) Brain Informatics. BI 2021. Lecture Notes in Computer Science(), vol 12960. Springer, Cham. https://doi.org/10.1007/978-3-030-86993-9_27
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