Abstract
In this paper, we propose a new method to detect noise hindrances in Electroencephalographic (EEG) signals caused by mental distractions, which we named “daydreaming signals.” Our approach is based on sliding windows and aims to detect and locate these daydreaming signals to specific points in time. We expect to get cleaner data and, therefore, higher prediction accuracy in current available EEG datasets by removing these daydreaming signals. Beyond these improvements to existing data, this approach also has the potential to improve the quality of future data collection, as researchers can discover the pattern of daydreaming signals in trial rounds and deal with these signals accordingly.
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References
Bashivan, P., Rish, I., Heisig, S.: Mental state recognition via wearable EEG. arXiv preprint arXiv:1602.00985 (2016)
Bashivan, P., Rish, I., Yeasin, M., Codella, N.: Learning representations from EEG with deep recurrent-convolutional neural networks (2016)
Biau, G., Scornet, E.: A random forest guided tour. TEST 25(2), 197–227 (2016). https://doi.org/10.1007/s11749-016-0481-7
Bigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K.M., Robbins, K.A.: The prep pipeline: standardized preprocessing for large-scale EEG analysis. Front. Neuroinform. 9, 16, e5199 (2015)
Bird, J.J., Manso, L.J., Ribeiro, E.P., Ekárt, A., Faria, D.R.: A study on mental state classification using EEG-based brain-machine interface. In: 2018 International Conference on Intelligent Systems (IS), pp. 795–800. IEEE (2018)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Chen, Y., Zhang, D.: Well log generation via ensemble long short-term memory (ENLSTM) network. Geophys. Res. Lett. 47(23), e2020GL087685 (2020)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Craik, A., He, Y., Contreras-Vidal, J.L.: Deep learning for electroencephalogram (EEG) classification tasks: a review. J. Neural Eng. 16(3), 031001 (2019)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Ienca, M., Haselager, P., Emanuel, E.J.: Brain leaks and consumer neurotechnology. Nat. Biotechnol. 36(9), 805–810 (2018)
Jas, M., Engemann, D.A., Bekhti, Y., Raimondo, F., Gramfort, A.: Autoreject: automated artifact rejection for MEG and EEG data. Neuroimage 159, 417–429, 031001 (2017)
Lei, J., Liu, C., Jiang, D.: Fault diagnosis of wind turbine based on long short-term memory networks. Renewable Energy 133, 422–432 (2019)
Leske, S., Dalal, S.S.: Reducing power line noise in EEG and meg data via spectrum interpolation. Neuroimage 189, 763–776, 031001 (2019)
Li, G., Lee, C.H., Jung, J.J., Youn, Y.C., Camacho, D.: Deep learning for EEG data analytics: a survey. Concurrency Comput. Practice Exp. 32(18), e5199 (2020)
Lotte, F., et al.: A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J. Neural Eng. 15(3), 031005 (2018)
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4(2), R1, 031005 (2007)
Qu, X., Hall, M., Sun, Y., Sekuler, R., Hickey, T.J.: A personalized reading coach using wearable EEG sensors-a pilot study of brainwave learning analytics. In: CSEDU (2), pp. 501–507 (2018)
Qu, X., Liu, P., Li, Z., Hickey, T.: Multi-class time continuity voting for EEG classification. In: Frasson, C., Bamidis, P., Vlamos, P. (eds.) BFAL 2020. LNCS (LNAI), vol. 12462, pp. 24–33. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60735-7_3
Qu, X., Mei, Q., Liu, P., Hickey, T.: Using EEG to distinguish between writing and typing for the same cognitive task. In: Frasson, C., Bamidis, P., Vlamos, P. (eds.) BFAL 2020. LNCS (LNAI), vol. 12462, pp. 66–74. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60735-7_7
Qu, X., Sun, Y., Sekuler, R., Hickey, T.: EEG markers of stem learning. In: 2018 IEEE Frontiers in Education Conference (FIE), pp. 1–9. IEEE (2018)
Quitadamo, L., Cavrini, F., Sbernini, L., Riillo, F., Bianchi, L., Seri, S., Saggio, G.: Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction: a review. J. Neural Eng. 14(1), 011001 (2017)
Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T.H., Faubert, J.: Deep learning-based electroencephalography analysis: a systematic review. J. Neural Eng. 16(5), 051001 (2019)
Wang, Y.B., You, Z.H., Yang, S., Yi, H.C., Chen, Z.H., Zheng, K.: A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network. BMC Med. Inform. Decis. Mak. 20(2), 1–9 (2020)
Zhang, X., Yao, L., Wang, X., Monaghan, J., Mcalpine, D., Zhang, Y.: A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. J. Neural Eng. 18(3), 031002 (2021)
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Wang, R., Qu, X. (2022). EEG Daydreaming, A Machine Learning Approach to Detect Daydreaming Activities. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Augmented Cognition. HCII 2022. Lecture Notes in Computer Science(), vol 13310. Springer, Cham. https://doi.org/10.1007/978-3-031-05457-0_17
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