EEG signals during mouth breathing in a working memory task

Abstract

Background: Continuous mouth breathing results not only morphological deformations but also poor learning outcomes. However, there were few studies that observed correlations between mouth breathing and cognition. This study aimed at investigating the changes in brain activity during mouth breathing while the participant simultaneously performed a cognitive task using electroencephalography (EEG).

Methods: Twenty subjects participated in this study, and EEG electrodes (32 channels, 250-Hz sampling rate) were placed on their scalp. Brain waves during a resting state and n-back tasks (0-back and 2-back) and physiological parameters such as SpO2, ETCO2, and the airway respiratory rate were measured. The pre-processed EEG signals were analyzed based on their frequencies as delta waves (0.5 ∼ 4 Hz), theta waves (4 ∼ 8 Hz), alpha waves (8 ∼ 13 Hz), beta waves (13 ∼ 30 Hz) and gamma waves (30 ∼ 50 Hz) using fast Fourier transform (FFT).

Results: When compared with nose breathing, theta and alpha powers were lower during mouth breathing at rest and alpha wave presented low power at 0-back and 2-back tasks. Furthermore, beta and gamma waves exhibited low powers at 2-back task. However, the behavioral results (accuracy and response time) have no significant difference between two breathing methods (mouth and nose). Mouth breathing showed different brain activity patterns, compared to nose breathing, and these changes are related to cognitive regions.

Conclusion: The reason for this change seems to relate to the decreased oxygen saturation during mouth breathing, suggesting that when cognitive abilities are required, mouth breathing can act as one of the variables that cause different outcomes in brain activities.

Keywords:

• Mouth breathing
• cognitive function
• brain waves
• respiration methods
• brain function

Disclosure statement

The authors declare no conflict of interest.

Funding

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2015R1C1A1A02036462) and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number : HI17C0557).