Author : Yong-Gi Hong 1, Hang-Keun Kim 1,2,, Young-Don Son 1,2 and Chang-Ki Kang 1,3,
1 Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences & Technology, Gachon University, Incheon 21936, Korea; hyg4438@bme.gachon.ac.kr (Y.-G.H.); ydson@bme.gachon.ac.kr (Y.-D.S.) 2 Department of Biomedical Engineering, Gachon University, Incheon 21936, Korea 3 Department of Radiological Science, Gachon University, Incheon 21936, Korea
- Correspondence: dsaint31@bme.gachon.ac.kr (H.-K.K.); ckkang@gachon.ac.kr (C.-K.K.)
Abstract: 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.
Keywords: EEG; breathing; machine learning; LDA; random forest; working memory task