Time-Dependent Adaptations of Brain Networks in Driving Fatigue

• Published in: Engineering proceedings Vol. 50; no. 1; p. 6
• Main Authors: Olympia Giannakopoulou, Ioannis Kakkos, Georgios N. Dimitrakopoulos, Yu Sun, George K. Matsopoulos, Dimitrios D. Koutsouris
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.10.2023
• Subjects: driving fatigue; EEG; functional connectivity; PLI networks; small-world metrics
• ISSN: 2673-4591
• DOI: 10.3390/engproc2023050006

Driving with fatigue is a major contributor to traffic accidents and is closely linked to central nervous system functions. To investigate the evolution of brain dynamics during simulated driving under different EEG rhythms, we conducted an experiment in which participants performed a 1 h driving task while their EEG signals were recorded. We used the complex network theory to analyze data derived from the driving stimulation and found that as fatigue deepened, small-world metrics, namely the path lengths, clustering coefficients, and measures of efficiency (global, local, nodal), showed alterations against the driving time. Additionally, a major correlation (corr = 0.98) was observed between the cluster coefficient with local efficiency in all frequency bands (theta, alpha, beta). Our findings suggest that driving fatigue can cause significant trends in brain network characteristics, such as path length (m = −103 to −93), (m = 98) for specific rhythms (beta, alpha, theta band, respectively) and their related brain functions, which could serve as objective indicators when evaluating the fatigue level and in the future, preventing driving fatigue and its consequences.