{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T02:56:58Z","timestamp":1788922618249,"version":"build-2803163510"},"reference-count":29,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T00:00:00Z","timestamp":1614297600000},"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":["2020R1A2C1004355"],"award-info":[{"award-number":["2020R1A2C1004355"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Technical Development Project","award":["20006105"],"award-info":[{"award-number":["20006105"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>This study was to investigate the changes in brain function due to lack of oxygen (O2) caused by mouth breathing, and to suggest a method to alleviate the side effects of mouth breathing on brain function through an additional O2 supply. For this purpose, we classified the breathing patterns according to EEG signals using a machine learning technique and proposed a method to reduce the side effects of mouth breathing on brain function. Twenty subjects participated in this study, and each subject performed three different breathings: nose and mouth breathing and mouth breathing with O2 supply during a working memory task. The results showed that nose breathing guarantees normal O2 supply to the brain, but mouth breathing interrupts the O2 supply to the brain. Therefore, this comparative study of EEG signals using machine learning showed that one of the most important elements distinguishing the effects of mouth and nose breathing on brain function was the difference in O2 supply. These findings have important implications for the workplace environment, suggesting that special care is required for employees who work long hours in confined spaces such as public transport, and that a sufficient O2 supply is needed in the workplace for working efficiency.<\/jats:p>","DOI":"10.3390\/brainsci11030293","type":"journal-article","created":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T06:47:20Z","timestamp":1614322040000},"page":"293","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Identification of Breathing Patterns through EEG Signal Analysis Using Machine Learning"],"prefix":"10.3390","volume":"11","author":[{"given":"Yong-Gi","family":"Hong","sequence":"first","affiliation":[{"name":"Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences &amp; Technology, Gachon University, Incheon 21936, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang-Keun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences &amp; Technology, Gachon University, Incheon 21936, Korea"},{"name":"Department of Biomedical Engineering, Gachon University, Incheon 21936, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7029-2422","authenticated-orcid":false,"given":"Young-Don","family":"Son","sequence":"additional","affiliation":[{"name":"Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences &amp; Technology, Gachon University, Incheon 21936, Korea"},{"name":"Department of Biomedical Engineering, Gachon University, Incheon 21936, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9573-8432","authenticated-orcid":false,"given":"Chang-Ki","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences &amp; Technology, Gachon University, Incheon 21936, Korea"},{"name":"Department of Radiological Science, Gachon University, Incheon 21936, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1016\/j.ijporl.2014.04.046","article-title":"Comparison of cephalometric patterns in mouth breathing and nose breathing children","volume":"78","author":"Orta","year":"2014","journal-title":"Int. J. Pediatric Otorhinolaryngol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1590\/1516-3180.2013.7630011","article-title":"Deficits in working memory, reading comprehension and arithmetic skills in children with mouth breathing syndrome: Analytical cross-sectional study","volume":"133","author":"Kuroishi","year":"2014","journal-title":"Sao Paulo Med. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"350","DOI":"10.3109\/07853899809029934","article-title":"Sleep-disordered breathing in children","volume":"30","author":"Guilleminault","year":"1998","journal-title":"Ann. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1111\/all.12885","article-title":"Mouth breathing, another risk factor for asthma: The Nagahama Study","volume":"71","author":"Izuhara","year":"2016","journal-title":"Allergy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1542\/peds.73.5.622","article-title":"Facial characteristics of children who breathe through the mouth","volume":"73","author":"Bresolin","year":"1984","journal-title":"Pediatrics"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1590\/S1808-86942010000500003","article-title":"Ocorr\u00eancia da s\u00edndrome da apneia obstrutiva do sono (SAOS) em crian\u00e7as respiradoras orais","volume":"76","author":"Izu","year":"2010","journal-title":"Braz. J. Otorhinolaryngol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"11","DOI":"10.3389\/fnint.2017.00025","article-title":"Hyperbaric Oxygen Environment Can Enhance Brain Activity and Multitasking Performance","volume":"11","author":"Vadas","year":"2017","journal-title":"Front. Integr. Neurosci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1111\/j.1447-0594.2012.00911.x","article-title":"Correlation between cognitive ability measured by response time of 1-back task and changes of SpO2 by supplying three different levels of oxygen in the elderly","volume":"13","author":"Choi","year":"2013","journal-title":"Geriatr. Gerontol. Int."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1080\/00207454.2019.1667787","article-title":"EEG signals during mouth breathing in a working memory task","volume":"130","author":"Lee","year":"2019","journal-title":"Int. J. Neurosci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1080\/00207450701750455","article-title":"A Study on the Effects of 40% Oxygen on Addition Task Performance in Three Levels of Difficulty and Physiological Signals","volume":"118","author":"Chung","year":"2008","journal-title":"Int. J. Neurosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TBME.2010.2048568","article-title":"EEG-Based Emotion Recognition in Music Listening","volume":"57","author":"Lin","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kim, Y., and Choi, A. (2020). EEG-Based Emotion Classification Using Long Short-Term Memory Network with Attention Mechanism. Sensors, 20.","DOI":"10.3390\/s20236727"},{"key":"ref_13","unstructured":"Raposo, F., de Matos, D.M., Ribeiro, R., Tang, S., and Yu, Y. (2017). Towards Deep Modeling of Music Semantics Using EEG Regularizers. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8659","DOI":"10.1016\/j.eswa.2010.06.065","article-title":"EEG signal classification using PCA, ICA, LDA and support vector machines","volume":"37","author":"Subasi","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.compbiomed.2013.04.002","article-title":"Feature extraction and recognition of ictal EEG using EMD and SVM","volume":"43","author":"Li","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Paszkiel, S. (2020). Brain\u2013Computer Interface Technology. Analysis and Classification of EEG Signals for Brain\u2013Computer Interfaces, Springer International Publishing.","DOI":"10.1007\/978-3-030-30581-9"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s42003-020-0846-z","article-title":"Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control","volume":"3","author":"Vahid","year":"2020","journal-title":"Commun. Biol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bentlemsan, M., Zemouri, E.-T., Bouchaffra, D., Yahya-Zoubir, B., and Ferroudji, K. (2014, January 27\u201329). Random Forest and Filter Bank Common Spatial Patterns for EEG-Based Motor Imagery Classification. Proceedings of the 2014 5th International Conference on Intelligent Systems, Modelling and Simulation, Langkawi, Malaysia.","DOI":"10.1109\/ISMS.2014.46"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1080\/00140139.2020.1784467","article-title":"Brain activity during a working memory task in different postures: An EEG study","volume":"63","author":"Jung","year":"2020","journal-title":"Ergonomics"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s13246-015-0333-x","article-title":"Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques","volume":"38","author":"Amin","year":"2015","journal-title":"Australas. Phys. Eng. Sci. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neucom.2013.06.046","article-title":"Emotional state classification from EEG data using machine learning approach","volume":"129","author":"Wang","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"76","DOI":"10.3389\/fnins.2018.00076","article-title":"Using Support Vector Machine on EEG for Advertisement Impact Assessment","volume":"12","author":"Wei","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cho, J., and Hwang, H. (2020). Spatio-Temporal Representation of an Electoencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network. Sensors, 20.","DOI":"10.3390\/s20123491"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Paszkiel, S. (2020). Using Neural Networks for Classification of the Changes in the EEG Signal Based on Facial Expressions. Analysis and Classification of EEG Signals for Brain\u2013Computer Interfaces, Springer.","DOI":"10.1007\/978-3-030-30581-9_7"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1016\/j.eswa.2018.08.031","article-title":"An end-to-end deep learning approach to MI-EEG signal classification for BCIs","volume":"114","author":"Dose","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"223","DOI":"10.4283\/JMAG.2019.24.2.223","article-title":"Development and Application of a Deep Convolutional Neural Network Noise Reduction Algorithm for Diffusion-weighted Magnetic Resonance Imaging","volume":"24","author":"Han","year":"2019","journal-title":"J. Magn."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1016\/j.csda.2009.04.009","article-title":"Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap","volume":"53","author":"Kim","year":"2009","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1016\/j.procs.2018.05.116","article-title":"Classification of EEG data for human mental state analysis using Random Forest Classifier","volume":"132","author":"Edla","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Almuhammadi, W.S., Aboalayon, K.A.I., and Faezipour, M. (2015, January 1). Efficient Obstructive Sleep Apnea Classification Based on EEG Signals. Proceedings of the 2015 Long Island Systems, Applications and Technology, Farmingdale, NY, USA.","DOI":"10.1109\/LISAT.2015.7160186"}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/11\/3\/293\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:29:11Z","timestamp":1760160551000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/11\/3\/293"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,26]]},"references-count":29,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["brainsci11030293"],"URL":"https:\/\/doi.org\/10.3390\/brainsci11030293","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,26]]}}}