A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition
<p dir="ltr">Electroencephalogram (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 differ...
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2023
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| _version_ | 1864513543627341824 |
|---|---|
| author | Ruilin Li (5627456) |
| author2 | Ruobin Gao (16003195) Ponnuthurai Nagaratnam Suganthan (11274636) |
| author2_role | author author |
| author_facet | Ruilin Li (5627456) Ruobin Gao (16003195) Ponnuthurai Nagaratnam Suganthan (11274636) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ruilin Li (5627456) Ruobin Gao (16003195) Ponnuthurai Nagaratnam Suganthan (11274636) |
| dc.date.none.fl_str_mv | 2023-05-01T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.ins.2022.12.088 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_decomposition-based_hybrid_ensemble_CNN_framework_for_driver_fatigue_recognition/24474625 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Electroencephalogram (EEG) Signal decomposition Ensemble learning Convolutional Neural Network (CNN) Driver fatigue recognition |
| dc.title.none.fl_str_mv | A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Electroencephalogram (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.</p><h2>Other Information</h2><p dir="ltr">Published in: Information Sciences<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.ins.2022.12.088" target="_blank">https://dx.doi.org/10.1016/j.ins.2022.12.088</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_b8a03d8ca391b0b8ffed9810404b6d18 |
| identifier_str_mv | 10.1016/j.ins.2022.12.088 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24474625 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A decomposition-based hybrid ensemble CNN framework for driver fatigue recognitionRuilin Li (5627456)Ruobin Gao (16003195)Ponnuthurai Nagaratnam Suganthan (11274636)EngineeringElectronics, sensors and digital hardwareInformation and computing sciencesArtificial intelligenceComputer vision and multimedia computationMachine learningElectroencephalogram (EEG)Signal decompositionEnsemble learningConvolutional Neural Network (CNN)Driver fatigue recognition<p dir="ltr">Electroencephalogram (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.</p><h2>Other Information</h2><p dir="ltr">Published in: Information Sciences<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.ins.2022.12.088" target="_blank">https://dx.doi.org/10.1016/j.ins.2022.12.088</a></p>2023-05-01T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.ins.2022.12.088https://figshare.com/articles/journal_contribution/A_decomposition-based_hybrid_ensemble_CNN_framework_for_driver_fatigue_recognition/24474625CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/244746252023-05-01T03:00:00Z |
| spellingShingle | A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition Ruilin Li (5627456) Engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Electroencephalogram (EEG) Signal decomposition Ensemble learning Convolutional Neural Network (CNN) Driver fatigue recognition |
| status_str | publishedVersion |
| title | A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition |
| title_full | A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition |
| title_fullStr | A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition |
| title_full_unstemmed | A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition |
| title_short | A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition |
| title_sort | A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition |
| topic | Engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Electroencephalogram (EEG) Signal decomposition Ensemble learning Convolutional Neural Network (CNN) Driver fatigue recognition |