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المؤلفLi, Ruilin
المؤلفGao, Ruobin
المؤلفYuan, Liqiang
المؤلفSuganthan, P.N.
المؤلفWang, Lipo
المؤلفSourina, Olga
تاريخ الإتاحة2023-05-16T09:07:33Z
تاريخ النشر2023
اسم المنشورEngineering Applications of Artificial Intelligence
المصدرScopus
الرقم المعياري الدولي للكتاب9521976
معرّف المصادر الموحدhttp://dx.doi.org/10.1016/j.engappai.2023.106237
معرّف المصادر الموحدhttp://hdl.handle.net/10576/42794
الملخصThis work investigated the use of an ensemble deep random vector functional link (edRVFL) network for electroencephalogram (EEG)-based driver fatigue recognition. Against the low feature learning capability of the edRVFL network from raw EEG signals, two strategies were exploited in this work. Specifically, the first one was to exploit the advantages of the feature extractor module in CNNs, i.e., use CNN features as the input of the edRVFL network. The second one was to improve the feature learning capability of the edRVFL network. An enhanced edRFVL network named FGloWD-edRVFL was proposed, in which four enhancements were implemented, including random forest-based Feature selection, Global output layer, Weighting and entropy-based Dynamic ensemble. The proposed FGloWD-edRVFL network was evaluated on the challenging cross-subject driver fatigue recognition tasks. The results indicated that the proposed model could boost the recognition performance, significantly outperforming all strong baselines. The step-wise analysis further demonstrated the effectiveness of the proposed enhancements in the edRVFL network. 2023 The Author(s)
راعي المشروعOpen Access funding provided by the Qatar National Library.
اللغةen
الناشرElsevier
الموضوعCross-subject driver fatigue recognition
Dynamic ensemble
Electroencephalogram (EEG)
Ensemble deep random vector functional link (edRVFL)
Feature selection
العنوانAn enhanced ensemble deep random vector functional link network for driver fatigue recognition
النوعArticle
رقم المجلد123
dc.accessType Open Access


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