EEG Information Transfer Changes in Different Daily Fatigue Levels During Drowsy Driving

Kuan-Chih Huang; Chun-Ying Tseng; Chin-Teng Lin

doi:10.1109/OJEMB.2024.3367496

EEG Information Transfer Changes in Different Daily Fatigue Levels During Drowsy Driving

IEEE Open J Eng Med Biol. 2024 Feb 20:5:180-190. doi: 10.1109/OJEMB.2024.3367496. eCollection 2024.

Authors

Kuan-Chih Huang¹, Chun-Ying Tseng², Chin-Teng Lin^{3

1}

Affiliations

¹ Brain Science and Technology Center, Department of Electrical and Computer EngineeringNational Yang Ming Chiao Tung University Hsinchu 300 Taiwan.
² Brain Science and Technology CenterNational Yang Ming Chiao Tung University Hsinchu 300 Taiwan.
³ Australian Artificial Intelligence Institute, Faculty of Engineering and ITUniversity of Technology Sydney Sydney NSW 2007 Australia.

Abstract

A significant issue for traffic safety has been drowsy driving for decades. A number of studies have investigated the effects of acute fatigue on spectral power; and recent research has revealed that drowsy driving is associated with a variety of brain connections in a specific cortico-cortical pathway. In spite of this, it is still unclear how different brain regions are connected in drowsy driving at different levels of daily fatigue. This study identified the brain connectivity-behavior relationship among three different daily fatigue levels (low-, median- and high-fatigue) with the EEG data transfer entropy. According to the results, only low- and medium-fatigue groups demonstrated an inverted U-shaped change in connectivity from high performance to poor behavioral performance. In addition, from low- to high-fatigue groups, connectivity magnitude decreased in the frontal region and increased in the occipital region. These study results suggest that brain connectivity and driving behavior would be affected by different levels of daily fatigue.

Keywords: Fatigue; brain connectivity; electroencephalography (EEG); transfer entropy.

Grants and funding

This work was supported in part by the National Science and Technology Council of the Republic of China, Taiwan, under Grant NSTC 109-2221-E-009-050-MY2, Grant 110-2221-E-A49-130-MY2, Grant 112-2321-B-A49-012, and Grant 112-2221-E-A49-085, and in part by the Australian Research Council (ARC) under Discovery Grant DP210101093 and Grant DP220100803.