{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T09:55:52Z","timestamp":1763200552672,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T00:00:00Z","timestamp":1612915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2019R1G1A1007097"],"award-info":[{"award-number":["NRF-2019R1G1A1007097"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Drowsiness while driving can lead to accidents that are related to the loss of perception during emergencies that harm the health. Among physiological signals, brain waves have been used as informative signals for the analyses of behavioral observations, steering information, and other biosignals during drowsiness. We inspected the machine learning methods for drowsiness detection based on brain signals with varying quantities of information. The results demonstrated that machine learning could be utilized to compensate for a lack of information and to account for individual differences. Cerebral area selection approaches to decide optimal measurement locations could be utilized to minimize the discomfort of participants. Although other statistics could provide additional information in further study, the optimized machine learning method could prevent the dangers of drowsiness while driving by considering a transitional state with nonlinear features. Because brain signals can be altered not only by mental fatigue but also by health status, the optimization analysis of the system hardware and software will be able to increase the power-efficiency and accessibility in acquiring brain waves for health enhancements in daily life.<\/jats:p>","DOI":"10.3390\/s21041255","type":"journal-article","created":{"date-parts":[[2021,2,12]],"date-time":"2021-02-12T16:12:10Z","timestamp":1613146330000},"page":"1255","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Drowsiness Detection Based on Intelligent Systems with Nonlinear Features for Optimal Placement of Encephalogram Electrodes on the Cerebral Area"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4242-7638","authenticated-orcid":false,"given":"Seunghyeok","family":"Hong","sequence":"first","affiliation":[{"name":"Division of Data Science, The University of Suwon, Hwaseong-si 18323, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyun Jae","family":"Baek","sequence":"additional","affiliation":[{"name":"Department of Medical and Mechatronics Engineering, Soonchunhyang University, Asan 31538, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MSP.2016.2602095","article-title":"Driver Status Monitoring Systems for Smart Vehicles Using Physiological Sensors","volume":"33","author":"Choi","year":"2016","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.aap.2014.04.007","article-title":"Are drivers aware of sleepiness and increasing crash risk while driving?","volume":"70","author":"Williamson","year":"2014","journal-title":"Accid. Anal. Prev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1136\/oem.56.5.289","article-title":"Vehicle accidents related to sleep: A review","volume":"56","author":"Horne","year":"1999","journal-title":"Occup. Environ. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"16937","DOI":"10.3390\/s121216937","article-title":"Detecting driver drowsiness based on sensors: A review","volume":"12","author":"Sahayadhas","year":"2012","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Aboalayon, K.A.I., Faezipour, M., Almuhammadi, W.S., and Moslehpour, S. (2016). Sleep stage classification using EEG signal analysis: A comprehensive survey and new investigation. Entropy, 18.","DOI":"10.3390\/e18090272"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.cmpb.2016.12.004","article-title":"A comparative review on sleep stage classification methods in patients and healthy individuals","volume":"140","author":"Boostani","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1109\/TBCAS.2010.2046415","article-title":"A real-time wireless brain-computer interface system for drowsiness detection","volume":"4","author":"Lin","year":"2010","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1109\/TCSI.2012.2185290","article-title":"Generalized EEG-based drowsiness prediction system by using a self-organizing neural fuzzy system","volume":"59","author":"Lin","year":"2012","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/TBME.2010.2077291","article-title":"Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm","volume":"58","author":"Khushaba","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7344","DOI":"10.1016\/j.eswa.2015.05.028","article-title":"Automatic detection of alertness\/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning","volume":"42","author":"Chen","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mu, Z., Hu, J., and Min, J. (2017). Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features. Appl. Sci., 7.","DOI":"10.3390\/app7020150"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Min, J., Wang, P., and Hu, J. (2017). Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system. PLoS ONE.","DOI":"10.1371\/journal.pone.0188756"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.ins.2018.04.003","article-title":"Intelligent system for drowsiness recognition based on ear canal electroencephalography with photoplethysmography and electrocardiography","volume":"453","author":"Hong","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"22908","DOI":"10.1109\/ACCESS.2018.2811723","article-title":"A Review on EEG-Based Automatic Sleepiness Detection Systems for Driver","volume":"6","author":"Balandong","year":"2018","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100635","DOI":"10.1016\/j.dcn.2019.100635","article-title":"Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges","volume":"36","author":"Lau","year":"2019","journal-title":"Dev. Cogn. Neurosci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rad\u00fcntz, T., and Meffert, B. (2019). User experience of 7 mobile electroencephalography devices: Comparative study. JMIR mHealth uHealth.","DOI":"10.2196\/preprints.14474"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kleinberg, E.M. (1996). An overtraining-resistant stochastic modeling method for pattern recognition. Ann. Stat.","DOI":"10.1214\/aos\/1032181157"},{"key":"ref_18","unstructured":"Ho, T.K. (1998). The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cortes, C., and Vapnik, V. (1995). Support-Vector Networks. Mach. Learn.","DOI":"10.1007\/BF00994018"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yeo, M.V.M., Li, X., Shen, K., and Wilder-Smith, E.P.V. (2009). Can SVM be used for automatic EEG detection of drowsiness during car driving?. Saf. Sci.","DOI":"10.1016\/j.ssci.2008.01.007"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1080\/21646821.2016.1245558","article-title":"American Clinical Neurophysiology Society Guideline 2: Guidelines for Standard Electrode Position Nomenclature","volume":"56","author":"Acharya","year":"2016","journal-title":"Neurodiagn. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1080\/14697680600969727","article-title":"Multi-scaling in finance","volume":"7","year":"2007","journal-title":"Quant. Financ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Higuchi, T. (1988). Approach to an irregular time series on the basis of the fractal theory. Phys. D Nonlinear Phenom.","DOI":"10.1016\/0167-2789(88)90081-4"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Accardo, A., Affinito, M., Carrozzi, M., and Bouquet, F. (1997). Use of fractal dimension for the analysis of electroencephalographic time series. Biol. Cybern.","DOI":"10.1007\/s004220050394"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"\u015een, B., Peker, M., \u00c7avu\u015fo\u01e7lu, A., and \u00c7elebi, F.V. (2014). A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms. J. Med. Syst.","DOI":"10.1007\/s10916-014-0018-0"},{"key":"ref_26","unstructured":"Gu, Q., Li, Z., and Han, J. (2012). Generalized Fisher Score for Feature Selection. arXiv."},{"key":"ref_27","unstructured":"ETSC (2001). The Role of Driver Fatigue in Commercial Road Transport Crashes, European Transport Safety Council."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Oshiro, T.M., Perez, P.S., and Baranauskas, J.A. (2012). How Many Trees in A Random Forest?. International Workshop on Machine Learning and Data Mining in Pattern Recognition, Springer.","DOI":"10.1007\/978-3-642-31537-4_13"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.knosys.2015.01.007","article-title":"An EEG-based perceptual function integration network for application to drowsy driving","volume":"80","author":"Chuang","year":"2015","journal-title":"Knowl. Based Syst."},{"key":"ref_30","first-page":"281","article-title":"Random Search for Hyper-Parameter Optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Knight, J.F., and Baber, C. (2005). A tool to assess the comfort of wearable computers. Hum. Factors.","DOI":"10.1518\/0018720053653875"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Adamchic, I., Langguth, B., Hauptmann, C., and Tass, P.A. (2012). Psychometric evaluation of visual analog scale for the assessment of chronic tinnitus. Am. J. Audiol.","DOI":"10.1044\/1059-0889(2012\/12-0010)"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bourdel, N., Alves, J., Pickering, G., Ramilo, I., Roman, H., and Canis, M. (2015). Systematic review of endometriosis pain assessment: How to choose a scale?. Hum. Reprod. Update.","DOI":"10.1093\/humupd\/dmu046"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Garra, G., Singer, A.J., Taira, B.R., Chohan, J., Cardoz, H., Chisena, E., and Thode, H.C. (2010). Validation of the Wong-Baker FACES pain rating scale in pediatric emergency department patients. Acad. Emerg. Med.","DOI":"10.1111\/j.1553-2712.2009.00620.x"},{"key":"ref_35","unstructured":"Cortes, C., Jackel, L.D., Solla, S.A., Vapnik, V., and Denker, J.S. (1994). Learning Curves: Asymptotic Values and Rate of Convergence. Adv. Neural Inf. Process. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Figueroa, R.L., Zeng-Treitler, Q., Kandula, S., and Ngo, L.H. (2012). Predicting sample size required for classification performance. BMC Med. Inform. Decis. Mak.","DOI":"10.1186\/1472-6947-12-8"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"10351","DOI":"10.1523\/JNEUROSCI.3439-05.2005","article-title":"Lateralization of the vertebrate brain: Taking the side of model systems","volume":"25","author":"Halpern","year":"2005","journal-title":"J. Neurosci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Looney, D., Kidmose, P., Park, C., Ungstrup, M., Rank, M., Rosenkranz, K., and Mandic, D. (2012). The in-the-ear recording concept: User-centered and wearable brain monitoring. IEEE Pulse.","DOI":"10.1109\/MPUL.2012.2216717"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"553352","DOI":"10.3389\/fninf.2020.553352","article-title":"A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection","volume":"14","author":"LaRocco","year":"2020","journal-title":"Front. Neuroinform."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zheng, W.L., and Lu, B.L. (2017). A multimodal approach to estimating vigilance using EEG and forehead EOG. J. Neural Eng.","DOI":"10.1088\/1741-2552\/aa5a98"},{"key":"ref_41","unstructured":"Shen, K.Q., Ong, C.J., Li, X.P., Hui, Z., and Wilder-Smith, E.P.V. (2007). A feature selection method for multilevel mental fatigue EEG classification. IEEE Trans. Biomed. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Budak, U., Bajaj, V., Akbulut, Y., Atila, O., and Sengur, A. (2019). An effective hybrid model for EEG-based drowsiness detection. IEEE Sens. J.","DOI":"10.1109\/JSEN.2019.2917850"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bajaj, V., Taran, S., Khare, S.K., and Sengur, A. (2020). Feature extraction method for classification of alertness and drowsiness states EEG signals. Appl. Acoust.","DOI":"10.1016\/j.apacoust.2020.107224"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Awais, M., Badruddin, N., and Drieberg, M. (2017). A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and Wearability. Sensors, 17.","DOI":"10.3390\/s17091991"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1109\/JBHI.2016.2532354","article-title":"Driver Fatigue Classification with Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System","volume":"21","author":"Chai","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fncom.2017.00072","article-title":"Automated Detection of Driver Fatigue Based on AdaBoost Classifier with EEG Signals","volume":"11","author":"Hu","year":"2017","journal-title":"Front. Comput. Neurosci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.micpro.2018.02.004","article-title":"Single-channel-based automatic drowsiness detection architecture with a reduced number of EEG features","volume":"58","author":"Belakhdar","year":"2018","journal-title":"Microprocess. Microsyst."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ogino, M., and Mitsukura, Y. (2018). Portable drowsiness detection through use of a prefrontal single-channel electroencephalogram. Sensors, 18.","DOI":"10.3390\/s18124477"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e12362","DOI":"10.14814\/phy2.12362","article-title":"Exploring miniaturized EEG electrodes for brain-computer interfaces. An EEG you do not see?","volume":"3","author":"Bleichner","year":"2015","journal-title":"Physiol. Rep."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wu, J., Srinivasan, R., Quinlan, E.B., Solodkin, A., Small, S.L., and Cramer, S.C. (2016). Utility of EEG measures of brain function in patients with acute stroke. J. Neurophysiol.","DOI":"10.1152\/jn.00978.2015"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Shin, J., M\u00fcller, K.R., Schmitz, C.H., Kim, D.W., and Hwang, H.J. (2017). Evaluation of a Compact Hybrid Brain-Computer Interface System. Biomed. Res. Int.","DOI":"10.1155\/2017\/6820482"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1255\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:22:24Z","timestamp":1760160144000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1255"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,10]]},"references-count":51,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21041255"],"URL":"https:\/\/doi.org\/10.3390\/s21041255","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,2,10]]}}}