{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T20:26:35Z","timestamp":1783023995948,"version":"3.54.6"},"reference-count":65,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["2024YFG0012"],"award-info":[{"award-number":["2024YFG0012"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["2023YFG0018"],"award-info":[{"award-number":["2023YFG0018"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.bspc.2026.110786","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T12:37:19Z","timestamp":1781699839000},"page":"110786","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["DGE\u2013TCF: Decoding brain representations of driving fatigue based on dynamic geometry enhancement and temporal connectivity filtering"],"prefix":"10.1016","volume":"125","author":[{"given":"Meng","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1398-207X","authenticated-orcid":false,"given":"Pengrui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongyao","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shihong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haokai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manqing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinmin","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dingming","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongrui","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.bspc.2026.110786_b1","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1109\/TBME.2019.2913914","article-title":"Transfer learning for brain\u2013computer interfaces: A Euclidean space data alignment approach","volume":"67","author":"He","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.bspc.2026.110786_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107457","article-title":"A multi-domain constraint learning system inspired by adaptive cognitive graphs for emotion recognition","volume":"188","author":"Gao","year":"2025","journal-title":"Neural Netw."},{"issue":"3","key":"10.1016\/j.bspc.2026.110786_b3","doi-asserted-by":"crossref","first-page":"230","DOI":"10.20517\/ir.2024.15","article-title":"A novel fatigue driving detection method based on whale optimization and Attention-enhanced GRU","volume":"4","author":"Li","year":"2024","journal-title":"Intell. Robotics"},{"key":"10.1016\/j.bspc.2026.110786_b4","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2021.3099565","article-title":"A drowsiness reduction strategy utilizing visual stimulation with different colors of light: an fNIRS study","volume":"9","author":"Shoaib","year":"2021","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.bspc.2026.110786_b5","doi-asserted-by":"crossref","first-page":"2558","DOI":"10.1109\/JBHI.2023.3240891","article-title":"CSF-GTNet: A novel multi-dimensional feature fusion network based on Convnext-GeLU-BiLSTM for EEG-signals-enabled fatigue driving detection","volume":"28","author":"Gao","year":"2023","journal-title":"IEEE J. Biomed. Health Informatics"},{"key":"10.1016\/j.bspc.2026.110786_b6","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.bmt.2024.10.003","article-title":"HMS-TENet: A hierarchical multi-scale topological enhanced network based on EEG and EOG for driver vigilance estimation","volume":"8","author":"Tang","year":"2024","journal-title":"Biomed. Technol."},{"key":"10.1016\/j.bspc.2026.110786_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107554","article-title":"TSMNet: A comprehensive network based on spatio-temporal representations for SSVEP classification","volume":"105","author":"Deng","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110786_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107550","article-title":"A coupling of common\u2013private topological patterns learning approach for cross-subject emotion recognition","volume":"105","author":"Zhang","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110786_b9","series-title":"2024 IEEE 4th International Conference on Digital Twins and Parallel Intelligence","first-page":"737","article-title":"Fatigue EEG signal detection based on ensemble learning","author":"Chen","year":"2024"},{"issue":"1","key":"10.1016\/j.bspc.2026.110786_b10","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/TFUZZ.2024.3399400","article-title":"A comprehensive adaptive interpretable Takagi\u2013Sugeno\u2013Kang fuzzy classifier for fatigue driving detection","volume":"33","author":"Gao","year":"2025","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.bspc.2026.110786_b11","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1109\/LSP.2022.3179946","article-title":"EEG-GCN: Spatio-temporal and self-adaptive graph convolutional networks for single and multi-view EEG-based emotion recognition","volume":"29","author":"Gao","year":"2022","journal-title":"IEEE Signal Process. Lett."},{"issue":"5","key":"10.1016\/j.bspc.2026.110786_b12","doi-asserted-by":"crossref","first-page":"4359","DOI":"10.1109\/JSEN.2022.3144317","article-title":"Transformers for EEG-based emotion recognition: A hierarchical spatial information learning model","volume":"22","author":"Wang","year":"2022","journal-title":"IEEE Sensors J."},{"key":"10.1016\/j.bspc.2026.110786_b13","doi-asserted-by":"crossref","first-page":"27146","DOI":"10.1109\/ACCESS.2019.2897908","article-title":"A complementary method of PCC for the construction of scalp resting-state EEG connectome: Maximum information coefficient","volume":"7","author":"Tian","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110786_b14","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2019.2931624","article-title":"EEG fingerprints: phase synchronization of EEG signals as biomarker for subject identification","volume":"7","author":"Kong","year":"2019","journal-title":"IEEE Access"},{"issue":"4","key":"10.1016\/j.bspc.2026.110786_b15","doi-asserted-by":"crossref","first-page":"7130","DOI":"10.1109\/TNNLS.2024.3405663","article-title":"An efficient graph learning system for emotion recognition inspired by the cognitive prior graph of eeg brain network","volume":"36","author":"Li","year":"2024","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"10.1016\/j.bspc.2026.110786_b16","series-title":"2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","first-page":"1","article-title":"Functional graph image representation applied to EEG-based mental workload classification","author":"Sarkis","year":"2024"},{"issue":"7","key":"10.1016\/j.bspc.2026.110786_b17","doi-asserted-by":"crossref","first-page":"9773","DOI":"10.1109\/TNNLS.2023.3236635","article-title":"LGGNet: Learning from local-global-graph representations for brain\u2013computer interface","volume":"35","author":"Ding","year":"2023","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"11","key":"10.1016\/j.bspc.2026.110786_b18","doi-asserted-by":"crossref","first-page":"19489","DOI":"10.1109\/TNNLS.2022.3225855","article-title":"Hierarchical dynamic graph convolutional network with interpretability for EEG-based emotion recognition","volume":"36","author":"Ye","year":"2025","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"27","key":"10.1016\/j.bspc.2026.110786_b19","doi-asserted-by":"crossref","first-page":"9673","DOI":"10.1073\/pnas.0504136102","article-title":"The human brain is intrinsically organized into dynamic, anticorrelated functional networks","volume":"102","author":"Fox","year":"2005","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"18","key":"10.1016\/j.bspc.2026.110786_b20","doi-asserted-by":"crossref","first-page":"7641","DOI":"10.1073\/pnas.1018985108","article-title":"Dynamic reconfiguration of human brain networks during learning","volume":"108","author":"Bassett","year":"2011","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.bspc.2026.110786_b21","series-title":"2018 17th IEEE International Conference on Machine Learning and Applications","first-page":"1358","article-title":"Lorenz chaotic system artificial neural network training with single time series input and multiple time series outputs for EEG prediction","author":"Zhang","year":"2018"},{"key":"10.1016\/j.bspc.2026.110786_b22","series-title":"2024 4th International Conference on Data Engineering and Communication Systems","first-page":"1","article-title":"Motion control using brain computer interface utilizing GCNs-Net with BILstms","author":"Gokulnath","year":"2024"},{"issue":"1","key":"10.1016\/j.bspc.2026.110786_b23","doi-asserted-by":"crossref","DOI":"10.1080\/09540091.2023.2289833","article-title":"Emotion recognition based on convolutional gated recurrent units with attention","volume":"35","author":"Ye","year":"2023","journal-title":"Connect. Sci."},{"key":"10.1016\/j.bspc.2026.110786_b24","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1109\/LSP.2024.3421259","article-title":"Temporal-spatial conversion based sequential convolutional LSTM architecture for detecting drug addiction","volume":"31","author":"Ma","year":"2024","journal-title":"IEEE Signal Process. Lett."},{"key":"10.1016\/j.bspc.2026.110786_b25","article-title":"An EEG-based cross-subject interpretable CNN for game player expertise level classification","volume":"237","year":"2024","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"10.1016\/j.bspc.2026.110786_b26","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1109\/TCDS.2024.3370635","article-title":"Brain connectivity analysis for EEG-based face perception task","volume":"16","author":"Chakladar","year":"2024","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"issue":"8","key":"10.1016\/j.bspc.2026.110786_b27","doi-asserted-by":"crossref","first-page":"4444","DOI":"10.1109\/JBHI.2023.3285268","article-title":"SFT-Net: A network for detecting fatigue from EEG signals by combining 4D feature flow and attention mechanism","volume":"28","author":"Gao","year":"2024","journal-title":"IEEE J. Biomed. Health Informatics"},{"issue":"4","key":"10.1016\/j.bspc.2026.110786_b28","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1109\/JBHI.2024.3349583","article-title":"Spatiotemporal network based on GCN and BiGRU for seizure detection","volume":"28","author":"Xu","year":"2024","journal-title":"IEEE J. Biomed. Health Informatics"},{"issue":"7","key":"10.1016\/j.bspc.2026.110786_b29","doi-asserted-by":"crossref","first-page":"7250","DOI":"10.1109\/TITS.2023.3347439","article-title":"Human\u2013machine shared control for path following considering driver fatigue characteristics","volume":"25","author":"Fang","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"6","key":"10.1016\/j.bspc.2026.110786_b30","doi-asserted-by":"crossref","first-page":"5350","DOI":"10.1109\/TITS.2021.3053096","article-title":"Human-centered design for an in-vehicle truck driver fatigue and distraction warning system","volume":"23","author":"Horberry","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"6","key":"10.1016\/j.bspc.2026.110786_b31","doi-asserted-by":"crossref","first-page":"2339","DOI":"10.1109\/TITS.2018.2868499","article-title":"Driver fatigue detection systems: A review","volume":"20","author":"Sikander","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"11","key":"10.1016\/j.bspc.2026.110786_b32","doi-asserted-by":"crossref","first-page":"19999","DOI":"10.1109\/TITS.2022.3189346","article-title":"A systematic survey of driving fatigue monitoring","volume":"23","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"4","key":"10.1016\/j.bspc.2026.110786_b33","doi-asserted-by":"crossref","first-page":"614","DOI":"10.20517\/ir.2023.34","article-title":"Integrate memory mechanism in multi-granularity deep framework for driver drowsiness detection","volume":"3","author":"Zhang","year":"2023","journal-title":"Intell. Robotics"},{"issue":"8","key":"10.1016\/j.bspc.2026.110786_b34","first-page":"10","article-title":"Bio-inspired intelligence with applications to robotics: A survey. intell","volume":"1","author":"Li","year":"2021","journal-title":"Robotics"},{"issue":"6","key":"10.1016\/j.bspc.2026.110786_b35","doi-asserted-by":"crossref","first-page":"5510","DOI":"10.1109\/TITS.2023.3333252","article-title":"Driver fatigue detection using measures of heart rate variability and electrodermal activity","volume":"25","author":"Jiao","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"6","key":"10.1016\/j.bspc.2026.110786_b36","doi-asserted-by":"crossref","first-page":"4009","DOI":"10.1109\/JBHI.2025.3527964","article-title":"A multi-modality attention network for driver fatigue detection based on frontal EEG, EDA and PPG signals","volume":"29","author":"Guo","year":"2025","journal-title":"IEEE J. Biomed. Health Informatics"},{"key":"10.1016\/j.bspc.2026.110786_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.apergo.2023.104150","article-title":"Investigation of eye movement measures of mental workload in healthcare: Can pupil dilations reflect fatigue or overload when it comes to health information system use?","volume":"114","author":"Lisanne","year":"2024","journal-title":"Appl. Ergon."},{"issue":"1","key":"10.1016\/j.bspc.2026.110786_b38","doi-asserted-by":"crossref","first-page":"21561","DOI":"10.1038\/s41598-021-99133-y","article-title":"On-road driving impairment following sleep deprivation differs according to age","volume":"11","author":"Cai","year":"2021","journal-title":"Sci. Rep."},{"issue":"19","key":"10.1016\/j.bspc.2026.110786_b39","doi-asserted-by":"crossref","DOI":"10.3390\/app11199195","article-title":"Driver fatigue detection based on residual channel attention network and head pose estimation","volume":"11","author":"Ye","year":"2021","journal-title":"Appl. Sci."},{"issue":"2","key":"10.1016\/j.bspc.2026.110786_b40","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/aa5a98","article-title":"A multimodal approach to estimating vigilance using EEG and forehead EOG","volume":"14","author":"Zheng","year":"2017","journal-title":"J. Neural Eng."},{"issue":"12","key":"10.1016\/j.bspc.2026.110786_b41","doi-asserted-by":"crossref","first-page":"20120","DOI":"10.1109\/TITS.2024.3446832","article-title":"A driving risk assessment framework considering driver\u2019s fatigue state and distraction behavior","volume":"25","author":"Chen","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"1","key":"10.1016\/j.bspc.2026.110786_b42","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/THMS.2021.3123171","article-title":"Bringing a vehicle to a controlled stop: Effectiveness of a dual-control scheme for identifying driver drowsiness and preventing lane departures under partial driving automation requiring hands-on-wheel","volume":"52","author":"Saito","year":"2022","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"issue":"11","key":"10.1016\/j.bspc.2026.110786_b43","article-title":"Dual-fuzzy regenerative braking control strategy based on braking intention recognition","volume":"15","author":"Qin","year":"2024","journal-title":"World Electr. Veh. J."},{"issue":"4","key":"10.1016\/j.bspc.2026.110786_b44","doi-asserted-by":"crossref","first-page":"614","DOI":"10.20517\/ir.2023.34","article-title":"Integrate memory mechanism in multi-granularity deep framework for driver drowsiness detection","volume":"3","author":"Zhang","year":"2023","journal-title":"Intell. Robotics"},{"issue":"3","key":"10.1016\/j.bspc.2026.110786_b45","doi-asserted-by":"crossref","first-page":"230","DOI":"10.20517\/ir.2024.15","article-title":"A novel fatigue driving detection method based on whale optimization and attention-enhanced gru","volume":"4","author":"Li","year":"2024","journal-title":"Intell. Robotics"},{"key":"10.1016\/j.bspc.2026.110786_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102591","article-title":"EEG-based driving fatigue detection using multilevel feature extraction and iterative hybrid feature selection","volume":"68","author":"Tuncer","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"issue":"10","key":"10.1016\/j.bspc.2026.110786_b47","doi-asserted-by":"crossref","first-page":"6602","DOI":"10.1109\/TII.2022.3167470","article-title":"EEG-based driver fatigue detection using FAWT and multiboosting approaches","volume":"18","author":"Subasi","year":"2022","journal-title":"IEEE Trans. Ind. Informatics"},{"issue":"21","key":"10.1016\/j.bspc.2026.110786_b48","doi-asserted-by":"crossref","first-page":"35245","DOI":"10.1109\/JSEN.2024.3461682","article-title":"A Siam-LSTM model for multichannel EEG on VR motion sickness recognition","volume":"24","author":"Hua","year":"2024","journal-title":"IEEE Sensors J."},{"key":"10.1016\/j.bspc.2026.110786_b49","unstructured":"Q. Ni, H. Zhang, C. Fan, S. Pei, C. Zhou, Z. Lv, Dbpnet: Dual-branch parallel network with temporal-frequency fusion for auditory attention detection, in: Proceedings of the International Joint Conference on Artificial Intelligence, IJCAI 2024, 2024."},{"key":"10.1016\/j.bspc.2026.110786_b50","doi-asserted-by":"crossref","first-page":"2700","DOI":"10.1109\/TASE.2024.3382892","article-title":"Multiscale global prompt transformer for EEG-based driver fatigue recognition","volume":"22","author":"Zhao","year":"2025","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"10.1016\/j.bspc.2026.110786_b51","doi-asserted-by":"crossref","DOI":"10.1109\/TNSRE.2023.3299156","article-title":"Self-attentive channel-connectivity capsule network for EEG-based driving fatigue detection","author":"Chen","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.bspc.2026.110786_b52","series-title":"2017 IEEE Second Ecuador Technical Chapters Meeting","first-page":"1","article-title":"EEG signal clustering for motor and imaginary motor tasks on hands and feet","author":"Asanza","year":"2017"},{"key":"10.1016\/j.bspc.2026.110786_b53","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.1109\/TNSRE.2021.3103210","article-title":"An effective dual self-attention residual network for seizure prediction","volume":"29","author":"Yang","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"4","key":"10.1016\/j.bspc.2026.110786_b54","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.1109\/JBHI.2024.3357995","article-title":"Manifold learning-based common spatial pattern for EEG signal classification","volume":"28","author":"Cai","year":"2024","journal-title":"IEEE J. Biomed. Health Informatics"},{"key":"10.1016\/j.bspc.2026.110786_b55","series-title":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies","first-page":"201","article-title":"Bi-LSTM based model for efficient diagnosis of schizophrenia using time series EEG","author":"Fatima","year":"2023"},{"issue":"10","key":"10.1016\/j.bspc.2026.110786_b56","doi-asserted-by":"crossref","first-page":"7921","DOI":"10.1109\/TNNLS.2022.3147208","article-title":"EEG-based cross-subject driver drowsiness recognition with an interpretable convolutional neural network","volume":"34","author":"Cui","year":"2023","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"3","key":"10.1016\/j.bspc.2026.110786_b57","doi-asserted-by":"crossref","first-page":"2238","DOI":"10.1109\/TAFFC.2022.3169001","article-title":"TSception: Capturing temporal dynamics and spatial asymmetry from EEG for emotion recognition","volume":"14","author":"Ding","year":"2023","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.bspc.2026.110786_b58","series-title":"2024 IEEE International Conference on Bioinformatics and Biomedicine","first-page":"3961","article-title":"TF-HiTNet: A temporal-frequency hierarchical transformer network for EEG motor imagery classification","author":"Yue","year":"2024"},{"key":"10.1016\/j.bspc.2026.110786_b59","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110613","article-title":"An EEG-based brain cognitive dynamic recognition network for representations of brain fatigue","volume":"146","author":"Li","year":"2023","journal-title":"Appl. Soft Comput."},{"issue":"9","key":"10.1016\/j.bspc.2026.110786_b60","doi-asserted-by":"crossref","first-page":"6524","DOI":"10.1109\/JBHI.2025.3577611","article-title":"Dual-TSST: A dual-branch temporal-spectral-spatial transformer model for EEG decoding","volume":"29","author":"Li","year":"2025","journal-title":"IEEE J. Biomed. Health Informatics"},{"issue":"3","key":"10.1016\/j.bspc.2026.110786_b61","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1109\/TCDS.2020.2985539","article-title":"Driving fatigue recognition with functional connectivity based on phase synchronization","volume":"13","author":"Wang","year":"2021","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"issue":"8","key":"10.1016\/j.bspc.2026.110786_b62","doi-asserted-by":"crossref","first-page":"1790","DOI":"10.1109\/TNSRE.2020.2999599","article-title":"Dynamic reorganization of functional connectivity unmasks fatigue related performance declines in simulated driving","volume":"28","author":"Wang","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.bspc.2026.110786_b63","series-title":"Proceedings of the 30th ACM International Conference on Multimedia","first-page":"209","article-title":"Vigilancenet: decouple intra-and inter-modality learning for multimodal vigilance estimation in RSVP-based BCI","author":"Cheng","year":"2022"},{"issue":"1","key":"10.1016\/j.bspc.2026.110786_b64","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1093\/cercor\/bhs010","article-title":"The extrinsic and intrinsic functional architectures of the human brain are not equivalent","volume":"23","author":"Mennes","year":"2013","journal-title":"Cerebral Cortex"},{"key":"10.1016\/j.bspc.2026.110786_b65","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"10039","article-title":"A local-ascending-global learning strategy for brain-computer interface","volume":"Vol. 38","author":"Gao","year":"2024"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426013406?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426013406?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T20:06:26Z","timestamp":1783022786000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426013406"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":65,"alternative-id":["S1746809426013406"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110786","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DGE\u2013TCF: Decoding brain representations of driving fatigue based on dynamic geometry enhancement and temporal connectivity filtering","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110786","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110786"}}