Cargando…
Investigating Driver Fatigue versus Alertness Using the Granger Causality Network
Driving fatigue has been identified as one of the main factors affecting drivers’ safety. The aim of this study was to analyze drivers’ different mental states, such as alertness and drowsiness, and find out a neurometric indicator able to detect drivers’ fatigue level in terms of brain networks. Tw...
Autores principales: | Kong, Wanzeng, Lin, Weicheng, Babiloni, Fabio, Hu, Sanqing, Borghini, Gianluca |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4570365/ https://www.ncbi.nlm.nih.gov/pubmed/26251909 http://dx.doi.org/10.3390/s150819181 |
Ejemplares similares
-
InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection
por: Zeng, Hong, et al.
Publicado: (2020) -
Automatic and Direct Identification of Blink Components from Scalp EEG
por: Kong, Wanzeng, et al.
Publicado: (2013) -
An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction
por: Zeng, Hong, et al.
Publicado: (2021) -
Granger causality revisited
por: Friston, Karl J., et al.
Publicado: (2014) -
Granger Causality–Based Analysis for Classification of Fibrillation Mechanisms and Localization of Rotational Drivers
por: Handa, Balvinder S., et al.
Publicado: (2020)