A complex network-based broad learning system for detecting driver fatigue from 
EEG signals

Yang, Y.; Gao, Z.; Li, Y.; Cai, Q.; Marwan, Norbert; Kurths, Jürgen

doi:10.1109/TSMC.2019.2956022

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

A complex network-based broad learning system for detecting driver fatigue from EEG signals

Authors

Yang, Y.
External Organizations;

Gao, Z.
External Organizations;

Li, Y.
External Organizations;

Cai, Q.
External Organizations;

/persons/resource/Marwan

Marwan, Norbert
Potsdam Institute for Climate Impact Research;

/persons/resource/Juergen.Kurths

Kurths, Jürgen
Potsdam Institute for Climate Impact Research;

External Ressource

No external resources are shared

Fulltext (public)

There are no public fulltexts stored in PIKpublic

Supplementary Material (public)

There is no public supplementary material available

Citation

Yang, Y., Gao, Z., Li, Y., Cai, Q., Marwan, N., Kurths, J. (2021): A complex network-based broad learning system for detecting driver fatigue from EEG signals. - IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 9, 5800-5808.
https://doi.org/10.1109/TSMC.2019.2956022

Cite as: https://publications.pik-potsdam.de/pubman/item/item_23614

Abstract

Driver fatigue detection is of great significance for guaranteeing traffic safety and further reducing economic as well as societal loss. In this article, a novel complex network (CN) based broad learning system (CNBLS) is proposed to realize an electroencephalogram (EEG)-based fatigue detection. First, a simulated driving experiment was conducted to obtain EEG recordings in alert and fatigue state. Then, the CN theory is applied to facilitate the broad learning system (BLS) for realizing an EEG-based fatigue detection. The results demonstrate that the proposed CNBLS can accurately differentiate the fatigue state from an alert state with high stability. In addition, the performances of the four existing methods are compared with the results of the proposed method. The results indicate that the proposed method outperforms these existing methods. In comparison to directly using EEG signals as the input of BLS, CNBLS can sharply improve the detection results. These results demonstrate that it is feasible to apply BLS in classifying EEG signals by means of CN theory. Also, the proposed method enriches the EEG analysis methods.