ArticleFrontiers in human neuroscience2018
Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data.
Article in Frontiers in human neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- EEG spectral attractors identify a geometric core of brain dynamics.Patterns (New York, N.Y.) · 2024Article
- Complex networks and deep learning for EEG signal analysis.Cognitive neurodynamics · 2021Review
- Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain-Computer Interface.Frontiers in human neuroscience · 2021Article
- Using Muse: Rapid Mobile Assessment of Brain Performance.Frontiers in neuroscience · 2021Article
- Observing changes in human functioning during induced sleep deficiency and recovery periods.PloS one · 2021Article
- Fatigue-Related Effects in the Process of Task Interruption on Working Memory.Frontiers in human neuroscience · 2021Article
- Beyond the target area: an integrative view of tDCS-induced motor cortex modulation in patients and athletes.Journal of neuroengineering and rehabilitation · 2019Review
- Brain Complex Network Characteristic Analysis of Fatigue during Simulated Driving Based on Electroencephalogram Signals.Entropy (Basel, Switzerland) · 2019Article
- Editorial: Psychophysiological Contributions to Traffic Safety.Frontiers in human neuroscience · 2019Article
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5 authors.
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Abstract
The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices.
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