{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T03:18:22Z","timestamp":1783048702307,"version":"3.54.6"},"reference-count":53,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12205328"],"award-info":[{"award-number":["12205328"]}],"id":[{"id":"10.13039\/501100001809","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":[[2026,8]]},"DOI":"10.1016\/j.bspc.2026.110413","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T16:19:26Z","timestamp":1777047566000},"page":"110413","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Multi-channel EEG-based depression recognition via micro- and macro-scale information fusion"],"prefix":"10.1016","volume":"122","author":[{"given":"Tao","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9376-5230","authenticated-orcid":false,"given":"Xia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenglong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingle","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifang","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.bspc.2026.110413_b1","doi-asserted-by":"crossref","first-page":"630","DOI":"10.3390\/brainsci12050630","article-title":"A depression prediction algorithm based on spatiotemporal feature of EEG signal","volume":"12","author":"Liu","year":"2022","journal-title":"Brain Sci."},{"issue":"4","key":"10.1016\/j.bspc.2026.110413_b2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3524499","article-title":"EEG based emotion recognition: A tutorial and review","volume":"55","author":"Li","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.bspc.2026.110413_b3","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/TNSRE.2021.3059429","article-title":"Enhancing EEG-based classification of depression patients using spatial information","volume":"29","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"3","key":"10.1016\/j.bspc.2026.110413_b4","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0299127","article-title":"A machine learning based depression screening framework using temporal domain features of the electroencephalography signals","volume":"19","author":"Khan","year":"2024","journal-title":"PLoS One"},{"key":"10.1016\/j.bspc.2026.110413_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2019.07.004","article-title":"Depression recognition using machine learning methods with different feature generation strategies","volume":"99","author":"Li","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.bspc.2026.110413_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106782","article-title":"A gated temporal-separable attention network for EEG-based depression recognition","volume":"157","author":"Yang","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"3","key":"10.1016\/j.bspc.2026.110413_b7","doi-asserted-by":"crossref","DOI":"10.1111\/exsy.12773","article-title":"Decision support system for major depression detection using spectrogram and convolution neural network with EEG signals","volume":"39","author":"Loh","year":"2022","journal-title":"Expert Syst."},{"key":"10.1016\/j.bspc.2026.110413_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.110190","article-title":"Automated accurate detection of depression using twin pascal\u2019s triangles lattice pattern with EEG signals","volume":"260","author":"Tasci","year":"2023","journal-title":"Knowledge-Based Syst."},{"key":"10.1016\/j.bspc.2026.110413_b9","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/BF00238742","article-title":"Surrogate data analysis of sleep electroencephalograms reveals evidence for nonlinearity","volume":"75","author":"Fell","year":"1996","journal-title":"Biol. Cybernet."},{"key":"10.1016\/j.bspc.2026.110413_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102520","article-title":"Assessing multi-layered nonlinear characteristics of ECG\/EEG signal via adaptive kernel density estimation-based hierarchical entropies","volume":"67","author":"Zhang","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"issue":"6","key":"10.1016\/j.bspc.2026.110413_b11","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol. Hear. Circ. Physiol."},{"issue":"17","key":"10.1016\/j.bspc.2026.110413_b12","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevLett.88.174102","article-title":"Permutation entropy: a natural complexity measure for time series","volume":"88","author":"Bandt","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"10.1016\/j.bspc.2026.110413_b13","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s11517-014-1216-0","article-title":"Assessing the complexity of short-term heartbeat interval series by distribution entropy","volume":"53","author":"Li","year":"2015","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.bspc.2026.110413_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103612","article-title":"LSDD-EEGNet: An efficient end-to-end framework for EEG-based depression detection","volume":"75","author":"Song","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110413_b15","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.bspc.2026.110413_b16","doi-asserted-by":"crossref","first-page":"41337","DOI":"10.1109\/ACCESS.2023.3270426","article-title":"An end-to-end deep learning model for EEG-based major depressive disorder classification","volume":"11","author":"Xia","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110413_b17","doi-asserted-by":"crossref","unstructured":"G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"issue":"11","key":"10.1016\/j.bspc.2026.110413_b18","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.1002\/hbm.23730","article-title":"Deep learning with convolutional neural networks for EEG decoding and visualization","volume":"38","author":"Schirrmeister","year":"2017","journal-title":"Hum. Brain Mapp."},{"issue":"5","key":"10.1016\/j.bspc.2026.110413_b19","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/aace8c","article-title":"EEGNet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces","volume":"15","author":"Lawhern","year":"2018","journal-title":"J. Neural Eng."},{"issue":"1","key":"10.1016\/j.bspc.2026.110413_b20","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1007\/s11571-025-10305-2","article-title":"Novel EEG-based diagnostic framework for major depressive disorder using microstate and entropy features","volume":"19","author":"Rahmati","year":"2025","journal-title":"Cogn. Neurodyn."},{"key":"10.1016\/j.bspc.2026.110413_b21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3053999","article-title":"DeprNet: A deep convolution neural network framework for detecting depression using EEG","volume":"70","author":"Seal","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"11","key":"10.1016\/j.bspc.2026.110413_b22","doi-asserted-by":"crossref","first-page":"5780","DOI":"10.3390\/ijerph18115780","article-title":"Epileptic seizures detection using deep learning techniques: a review","volume":"18","author":"Shoeibi","year":"2021","journal-title":"Int. J. Env. Res. Public Health"},{"key":"10.1016\/j.bspc.2026.110413_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.109053","article-title":"Quantifying randomness and complexity of a signal via maximum fuzzy membership difference entropy","volume":"174","author":"Zhang","year":"2021","journal-title":"Measurement"},{"key":"10.1016\/j.bspc.2026.110413_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107360","article-title":"Cross-subject classification of depression by using multiparadigm EEG feature fusion","volume":"233","author":"Yang","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.bspc.2026.110413_b25","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TIP.2022.3140606","article-title":"Learning discriminative cross-modality features for RGB-D saliency detection","volume":"31","author":"Wang","year":"2022","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"10.1016\/j.bspc.2026.110413_b26","article-title":"Stacked ensemble machine learning approach for electroencephalography based major depressive disorder classification using temporal statistics","volume":"12","author":"Ahmed","year":"2024","journal-title":"Syst. Sci. Control. Eng."},{"issue":"1","key":"10.1016\/j.bspc.2026.110413_b27","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1186\/s12888-023-05349-9","article-title":"Analysis of EEG features and study of automatic classification in first-episode and drug-na\u00efve patients with major depressive disorder","volume":"23","author":"Huang","year":"2023","journal-title":"BMC Psychiatry"},{"key":"10.1016\/j.bspc.2026.110413_b28","doi-asserted-by":"crossref","unstructured":"H. Lin, T. Ma, C. Zhao, J. Qi, T. Zhang, X. Kong, Depression Detection with EEG Based on Mutual Information Regularization, in: Proceedings of the 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications, 2024, pp. 1029\u20131035.","DOI":"10.1109\/ISPA63168.2024.00136"},{"key":"10.1016\/j.bspc.2026.110413_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104520","article-title":"Automatic feature learning model combining functional connectivity network and graph regularization for depression detection","volume":"82","author":"Yang","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"issue":"7","key":"10.1016\/j.bspc.2026.110413_b30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-020-01573-y","article-title":"Detection of depression and scaling of severity using six channel EEG data","volume":"44","author":"Mahato","year":"2020","journal-title":"J. Med. Syst."},{"issue":"2","key":"10.1016\/j.bspc.2026.110413_b31","doi-asserted-by":"crossref","first-page":"211","DOI":"10.3390\/e24020211","article-title":"Ensemble approach for detection of depression using EEG features","volume":"24","author":"Avots","year":"2022","journal-title":"Entropy"},{"key":"10.1016\/j.bspc.2026.110413_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108094","article-title":"Assessment of dispersion patterns for negative stress detection from electroencephalographic signals","volume":"119","author":"Garc\u00eda-Mart\u00ednez","year":"2021","journal-title":"Pattern Recognit."},{"issue":"2","key":"10.1016\/j.bspc.2026.110413_b33","doi-asserted-by":"crossref","first-page":"142","DOI":"10.3390\/e27020142","article-title":"Research on depression recognition model and its temporal characteristics based on multiscale entropy of EEG signals","volume":"27","author":"Xu","year":"2025","journal-title":"Entropy"},{"key":"10.1016\/j.bspc.2026.110413_b34","doi-asserted-by":"crossref","first-page":"92630","DOI":"10.1109\/ACCESS.2019.2927121","article-title":"Multivariate pattern analysis of EEG-based functional connectivity: A study on the identification of depression","volume":"7","author":"Peng","year":"2019","journal-title":"IEEE Access"},{"issue":"10","key":"10.1016\/j.bspc.2026.110413_b35","doi-asserted-by":"crossref","first-page":"2266","DOI":"10.1016\/j.clinph.2005.06.011","article-title":"Nonlinear dynamical analysis of EEG and MEG: review of an emerging field","volume":"116","author":"Stam","year":"2005","journal-title":"Clin. Neurophysiol."},{"key":"10.1016\/j.bspc.2026.110413_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106930","article-title":"Depression detection based on the temporal-spatial-frequency feature fusion of EEG","volume":"100","author":"Xi","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"issue":"10","key":"10.1016\/j.bspc.2026.110413_b37","doi-asserted-by":"crossref","first-page":"7210","DOI":"10.1109\/JBHI.2025.3578126","article-title":"TDSFE-Net: A temporal dual-stream feature extraction network for depression detection from EEG","volume":"29","author":"Li","year":"2025","journal-title":"IEEE J. Biomed. Health Inf."},{"issue":"1","key":"10.1016\/j.bspc.2026.110413_b38","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1109\/21.87068","article-title":"On ordered weighted averaging aggregation operators in multicriteria decisionmaking","volume":"18","author":"Yager","year":"2002","journal-title":"IEEE Trans. Syst. Man, Cybern."},{"key":"10.1016\/j.bspc.2026.110413_b39","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103592","article-title":"SingleChannelNet: A model for automatic sleep stage classification with raw single-channel EEG","volume":"75","author":"Zhou","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"issue":"1","key":"10.1016\/j.bspc.2026.110413_b40","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1038\/s41597-022-01211-x","article-title":"A multi-modal open dataset for mental-disorder analysis","volume":"9","author":"Cai","year":"2022","journal-title":"Sci. Data"},{"issue":"2","key":"10.1016\/j.bspc.2026.110413_b41","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0171409","article-title":"A wavelet-based technique to predict treatment outcome for major depressive disorder","volume":"12","author":"Mumtaz","year":"2017","journal-title":"PLoS One"},{"key":"10.1016\/j.bspc.2026.110413_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103927","article-title":"Multi-channel EEG-based emotion recognition via a multi-level features guided capsule network","volume":"123","author":"Liu","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110413_b43","doi-asserted-by":"crossref","unstructured":"M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C. Chen, Mobilenetv2: Inverted residuals and linear bottlenecks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510\u20134520.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"10.1016\/j.bspc.2026.110413_b44","doi-asserted-by":"crossref","unstructured":"X. Zhang, X. Zhou, M. Lin, J. Sun, Shufflenet: An extremely efficient convolutional neural network for mobile devices, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 6848\u20136856.","DOI":"10.1109\/CVPR.2018.00716"},{"issue":"3","key":"10.1016\/j.bspc.2026.110413_b45","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.cmpb.2010.12.003","article-title":"Fast computation of sample entropy and approximate entropy in biomedicine","volume":"104","author":"Pan","year":"2011","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.bspc.2026.110413_b46","article-title":"Robust learning from corrupted EEG with dynamic spatial filtering","volume":"251","author":"Hubert","year":"2022","journal-title":"NeuroImage"},{"key":"10.1016\/j.bspc.2026.110413_b47","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.compbiomed.2015.09.019","article-title":"Nonlinear analysis of EEGs of patients with major depression during different emotional states","volume":"67","author":"Akar","year":"2015","journal-title":"Comput. Biol. Med."},{"issue":"3","key":"10.1016\/j.bspc.2026.110413_b48","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1109\/TNSRE.2019.2894423","article-title":"Graph theory analysis of functional connectivity in major depression disorder with high-density resting state EEG data","volume":"27","author":"Sun","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"2","key":"10.1016\/j.bspc.2026.110413_b49","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1109\/JBHI.2022.3200522","article-title":"Cognitive depression detection cyber-medical system based on EEG analysis and deep learning approaches","volume":"27","author":"Chiang","year":"2022","journal-title":"IEEE J. Biomed. Health Inf."},{"issue":"1","key":"10.1016\/j.bspc.2026.110413_b50","doi-asserted-by":"crossref","first-page":"13530","DOI":"10.1038\/s41598-017-13626-3","article-title":"Emotion regulating attentional control abnormalities in major depressive disorder: an event-related potential study","volume":"7","author":"Hu","year":"2017","journal-title":"Sci. Rep."},{"issue":"7","key":"10.1016\/j.bspc.2026.110413_b51","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1001\/jamapsychiatry.2013.1189","article-title":"Behavioral and neurophysiological correlates of autobiographical memory deficits in patients with depression and individuals at high risk for depression","volume":"70","author":"Young","year":"2013","journal-title":"JAMA Psychiatry"},{"issue":"2","key":"10.1016\/j.bspc.2026.110413_b52","first-page":"2481","article-title":"Enhancing heart sound classification with iterative clustering and silhouette analysis: An effective preprocessing selective method to diagnose rare and difficult cardiovascular cases","volume":"144","author":"Alrabie","year":"2025","journal-title":"Comput. Model. Eng. Sci."},{"key":"10.1016\/j.bspc.2026.110413_b53","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102596","article-title":"Quantitative electroencephalographic biomarkers behind major depressive disorder","volume":"68","author":"Knocikov\u00e1","year":"2021","journal-title":"Biomed. Signal Process. Control."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009675?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009675?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T02:54:25Z","timestamp":1783047265000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426009675"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":53,"alternative-id":["S1746809426009675"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110413","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-channel EEG-based depression recognition via micro- and macro-scale information fusion","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110413","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":"110413"}}