{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T04:23:45Z","timestamp":1783311825018,"version":"3.54.6"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031054563","type":"print"},{"value":"9783031054570","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-05457-0_17","type":"book-chapter","created":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T06:09:08Z","timestamp":1652767748000},"page":"202-212","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["EEG Daydreaming, A Machine Learning Approach to\u00a0Detect Daydreaming Activities"],"prefix":"10.1007","author":[{"given":"Ruyang","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,16]]},"reference":[{"key":"17_CR1","unstructured":"Bashivan, P., Rish, I., Heisig, S.: Mental state recognition via wearable EEG. arXiv preprint arXiv:1602.00985 (2016)"},{"key":"17_CR2","unstructured":"Bashivan, P., Rish, I., Yeasin, M., Codella, N.: Learning representations from EEG with deep recurrent-convolutional neural networks (2016)"},{"issue":"2","key":"17_CR3","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s11749-016-0481-7","volume":"25","author":"G Biau","year":"2016","unstructured":"Biau, G., Scornet, E.: A random forest guided tour. TEST 25(2), 197\u2013227 (2016). https:\/\/doi.org\/10.1007\/s11749-016-0481-7","journal-title":"TEST"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.3389\/fninf.2015.00016"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Bird, J.J., Manso, L.J., Ribeiro, E.P., Ek\u00e1rt, 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\u2013800. IEEE (2018)","DOI":"10.1109\/IS.2018.8710576"},{"issue":"1","key":"17_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Random forests. Mach. Learn."},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, D.: Well log generation via ensemble long short-term memory (ENLSTM) network. Geophys. Res. Lett. 47(23), e2020GL087685 (2020)","DOI":"10.1029\/2020GL087685"},{"issue":"3","key":"17_CR8","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995)","journal-title":"Support-vector networks. Mach. Learn."},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Craik, A., He, Y., Contreras-Vidal, J.L.: Deep learning for electroencephalogram (EEG) classification tasks: a review. J. Neural Eng. 16(3), 031001 (2019)","DOI":"10.1088\/1741-2552\/ab0ab5"},{"issue":"8","key":"17_CR10","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"9","key":"17_CR11","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1038\/nbt.4240","volume":"36","author":"M Ienca","year":"2018","unstructured":"Ienca, M., Haselager, P., Emanuel, E.J.: Brain leaks and consumer neurotechnology. Nat. Biotechnol. 36(9), 805\u2013810 (2018)","journal-title":"Nat. Biotechnol."},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Jas, M., Engemann, D.A., Bekhti, Y., Raimondo, F., Gramfort, A.: Autoreject: automated artifact rejection for MEG and EEG data. Neuroimage 159, 417\u2013429, 031001 (2017)","DOI":"10.1016\/j.neuroimage.2017.06.030"},{"key":"17_CR13","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1016\/j.renene.2018.10.031","volume":"133","author":"J Lei","year":"2019","unstructured":"Lei, J., Liu, C., Jiang, D.: Fault diagnosis of wind turbine based on long short-term memory networks. Renewable Energy 133, 422\u2013432 (2019)","journal-title":"Renewable Energy"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Leske, S., Dalal, S.S.: Reducing power line noise in EEG and meg data via spectrum interpolation. Neuroimage 189, 763\u2013776, 031001 (2019)","DOI":"10.1016\/j.neuroimage.2019.01.026"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1002\/cpe.5199"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1088\/1741-2552\/aab2f2"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Lotte, F., Congedo, M., L\u00e9cuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4(2), R1, 031005 (2007)","DOI":"10.1088\/1741-2560\/4\/2\/R01"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"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\u2013507 (2018)","DOI":"10.5220\/0006814705010507"},{"key":"17_CR19","doi-asserted-by":"publisher","unstructured":"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\u201333. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60735-7_3","DOI":"10.1007\/978-3-030-60735-7_3"},{"key":"17_CR20","doi-asserted-by":"publisher","unstructured":"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\u201374. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60735-7_7","DOI":"10.1007\/978-3-030-60735-7_7"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Qu, X., Sun, Y., Sekuler, R., Hickey, T.: EEG markers of stem learning. In: 2018 IEEE Frontiers in Education Conference (FIE), pp. 1\u20139. IEEE (2018)","DOI":"10.1109\/FIE.2018.8659031"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1088\/1741-2552\/14\/1\/011001"},{"key":"17_CR23","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1088\/1741-2552\/ab260c"},{"issue":"2","key":"17_CR24","first-page":"1","volume":"20","author":"YB Wang","year":"2020","unstructured":"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\u20139 (2020)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1088\/1741-2552\/abc902"}],"container-title":["Lecture Notes in Computer Science","Augmented Cognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-05457-0_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T00:07:45Z","timestamp":1778976465000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-05457-0_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031054563","9783031054570"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-05457-0_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.hci.international\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}