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AuthorLi, Ruilin
AuthorGao, Ruobin
AuthorSuganthan, Ponnuthurai Nagaratnam
Available date2023-02-08T07:32:57Z
Publication Date2023-05-01
Publication NameInformation Sciences
Identifierhttp://dx.doi.org/10.1016/j.ins.2022.12.088
CitationLi, R., Gao, R., & Suganthan, P. N. (2023). A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition. Information Sciences, 624, 833-848.‏
ISSN00200255
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85146278092&origin=inward
URIhttp://hdl.handle.net/10576/39800
AbstractElectroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary and improve the EEG decoding performance in different applications. However, it remains challenging to extract more distinguishable features from different decomposed components for driver fatigue recognition. In this work, we propose a novel decomposition-based hybrid ensemble convolutional neural network (CNN) framework to enhance the capability of decoding EEG signals. Four decomposition methods are employed to disassemble the EEG signals into components of different complexity. Instead of handcraft features, the CNNs in this framework directly learn from the decomposed components. In addition, a component-specific batch normalization layer is employed to reduce subject variability. Moreover, we employ two ensemble modes to integrate the outputs of all CNNs, comprehensively exploiting the diverse information of the decomposed components. Against the challenging cross-subject driver fatigue recognition task, the models under the framework all showed better performance than the strong baselines. Specifically, the performance of different decomposition methods and ensemble modes was further compared. The results indicated that discrete wavelet transform-based ensemble CNN achieved the highest average classification accuracy of 83.48% among the compared methods. The proposed framework can be extended to any CNN architecture and be applied to any EEG-related tasks, opening the possibility of extracting more beneficial features from complex EEG data.
Languageen
PublisherElsevier Inc.
SubjectConvolutional Neural Network (CNN)
Driver fatigue recognition
Electroencephalogram (EEG)
Ensemble learning
Signal decomposition
TitleA decomposition-based hybrid ensemble CNN framework for driver fatigue recognition
TypeArticle
Pagination833-848
Volume Number624
dc.accessType Open Access


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