{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T07:23:32Z","timestamp":1777879412651,"version":"3.51.4"},"reference-count":63,"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","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.2026.110359","type":"journal-article","created":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T18:45:48Z","timestamp":1776797148000},"page":"110359","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["HLF-CIKNet: A spatial-frequency and cross-interaction feature extraction network for EEG-based emotion recognition"],"prefix":"10.1016","volume":"121","author":[{"given":"Jinghui","family":"Qin","sequence":"first","affiliation":[]},{"given":"Hao","family":"Tan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2788-5756","authenticated-orcid":false,"given":"Kebing","family":"Jin","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110359_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107450","article-title":"Emotion recognition in EEG signals using deep learning methods: A review","author":"Jafari","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"5","key":"10.1016\/j.bspc.2026.110359_b2","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1109\/TCBB.2021.3052811","article-title":"EEG-based brain-computer interfaces (BCIs): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their applications","volume":"18","author":"Gu","year":"2021","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"3","key":"10.1016\/j.bspc.2026.110359_b3","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/abc902","article-title":"A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers","volume":"18","author":"Zhang","year":"2021","journal-title":"J. Neural Eng."},{"key":"10.1016\/j.bspc.2026.110359_b4","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.eswa.2015.10.049","article-title":"Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers","volume":"47","author":"Atkinson","year":"2016","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"10.1016\/j.bspc.2026.110359_b5","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1192\/bjp.bp.110.078139","article-title":"State-dependent alteration in face emotion recognition in depression","volume":"198","author":"Anderson","year":"2011","journal-title":"Br. J. Psychiatry"},{"issue":"15","key":"10.1016\/j.bspc.2026.110359_b6","doi-asserted-by":"crossref","first-page":"5092","DOI":"10.3390\/s21155092","article-title":"EEG-based emotion recognition by convolutional neural network with multi-scale kernels","volume":"21","author":"Phan","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.bspc.2026.110359_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103580","article-title":"A new data augmentation convolutional neural network for human emotion recognition based on ECG signals","volume":"75","author":"Nita","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"issue":"20","key":"10.1016\/j.bspc.2026.110359_b8","doi-asserted-by":"crossref","first-page":"19608","DOI":"10.1109\/JSEN.2022.3202209","article-title":"EEG-based emotion recognition via efficient convolutional neural network and contrastive learning","volume":"22","author":"Li","year":"2022","journal-title":"IEEE Sensors J."},{"key":"10.1016\/j.bspc.2026.110359_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103660","article-title":"DRS-net: A spatial\u2013temporal affective computing model based on multichannel EEG data","volume":"76","author":"Li","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110359_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104799","article-title":"GLFANet: A global to local feature aggregation network for EEG emotion recognition","volume":"85","author":"Liu","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"issue":"3","key":"10.1016\/j.bspc.2026.110359_b11","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/ab0ab5","article-title":"Deep learning for electroencephalogram (EEG) classification tasks: a review","volume":"16","author":"Craik","year":"2019","journal-title":"J. Neural Eng."},{"issue":"3","key":"10.1016\/j.bspc.2026.110359_b12","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2560\/12\/3\/031001","article-title":"EEG artifact removal\u2014state-of-the-art and guidelines","volume":"12","author":"Urig\u00fcen","year":"2015","journal-title":"J. Neural Eng."},{"issue":"1","key":"10.1016\/j.bspc.2026.110359_b13","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inform. Theory"},{"key":"10.1016\/j.bspc.2026.110359_b14","series-title":"Support Vector Machines","author":"Steinwart","year":"2008"},{"key":"10.1016\/j.bspc.2026.110359_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106620","article-title":"CMLP-Net: A convolution-multilayer perceptron network for EEG-based emotion recognition","volume":"96","author":"Lu","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110359_b16","doi-asserted-by":"crossref","DOI":"10.3389\/fnhum.2021.765525","article-title":"Data augmentation for deep neural networks model in EEG classification task: a review","volume":"15","author":"He","year":"2021","journal-title":"Front. Hum. Neurosci."},{"issue":"2","key":"10.1016\/j.bspc.2026.110359_b17","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1007\/s13042-021-01414-5","article-title":"EEG-based emotion recognition with feature fusion networks","volume":"13","author":"Gao","year":"2022","journal-title":"Int. J. Mach. Learn. Cybern."},{"issue":"1","key":"10.1016\/j.bspc.2026.110359_b18","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s11517-022-02686-x","article-title":"Subject-independent EEG emotion recognition with hybrid spatio-temporal GRU-Conv architecture","volume":"61","author":"Xu","year":"2023","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.bspc.2026.110359_b19","series-title":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society","first-page":"5723","article-title":"Introducing attention mechanism for eeg signals: Emotion recognition with vision transformers","author":"Arjun","year":"2021"},{"key":"10.1016\/j.bspc.2026.110359_b20","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Alexey","year":"2020"},{"issue":"5","key":"10.1016\/j.bspc.2026.110359_b21","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/ab260c","article-title":"Deep learning-based electroencephalography analysis: a systematic review","volume":"16","author":"Roy","year":"2019","journal-title":"J. Neural Eng."},{"key":"10.1016\/j.bspc.2026.110359_b22","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.neunet.2020.08.009","article-title":"Emotional EEG classification using connectivity features and convolutional neural networks","volume":"132","author":"Moon","year":"2020","journal-title":"Neural Netw."},{"key":"10.1016\/j.bspc.2026.110359_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104999","article-title":"STILN: A novel spatial-temporal information learning network for EEG-based emotion recognition","volume":"85","author":"Tang","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110359_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104806","article-title":"Deep time-frequency features and semi-supervised dimension reduction for subject-independent emotion recognition from multi-channel EEG signals","volume":"85","author":"Zali-Vargahan","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110359_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105422","article-title":"ICaps-ResLSTM: Improved capsule network and residual LSTM for EEG emotion recognition","volume":"87","author":"Fan","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110359_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112770","article-title":"GCD-JFSE: Graph-based class-domain knowledge joint feature selection and ensemble learning for EEG-based emotion recognition","volume":"309","author":"Luo","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.bspc.2026.110359_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105690","article-title":"MS-FTSCNN: An EEG emotion recognition method from the combination of multi-domain features","volume":"88","author":"Li","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110359_b28","first-page":"1","article-title":"Spatial-temporal feature fusion neural network for EEG-based emotion recognition","volume":"71","author":"Wang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"3","key":"10.1016\/j.bspc.2026.110359_b29","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.3390\/s23031622","article-title":"Feature pyramid networks and long short-term memory for EEG feature map-based emotion recognition","volume":"23","author":"Zhang","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.bspc.2026.110359_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106463","article-title":"TC-Net: A Transformer Capsule Network for EEG-based emotion recognition","volume":"152","author":"Wei","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110359_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106249","article-title":"AC-CfC: An attention-based convolutional closed-form continuous-time neural network for raw multi-channel EEG-based emotion recognition","volume":"94","author":"Wang","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110359_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121612","article-title":"A cross-session motor imagery classification method based on Riemannian geometry and deep domain adaptation","volume":"237","author":"Liu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110359_b33","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"4","key":"10.1016\/j.bspc.2026.110359_b34","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.2026.110359_b35","series-title":"U-KAN makes strong backbone for medical image segmentation and generation","author":"Li","year":"2024"},{"key":"10.1016\/j.bspc.2026.110359_b36","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.procs.2018.04.056","article-title":"Learning emotions EEG-based recognition and brain activity: A survey study on BCI for intelligent tutoring system","volume":"130","author":"Xu","year":"2018","journal-title":"Procedia Comput. Sci."},{"issue":"1","key":"10.1016\/j.bspc.2026.110359_b37","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1146\/annurev.clinpsy.1.102803.144009","article-title":"Categorical and dimensional models of personality disorder","volume":"1","author":"Trull","year":"2005","journal-title":"Annu. Rev. Clin. Psychol."},{"key":"10.1016\/j.bspc.2026.110359_b38","doi-asserted-by":"crossref","unstructured":"Z. Zhang, X. Lu, G. Cao, Y. Yang, L. Jiao, F. Liu, ViT-YOLO: Transformer-based YOLO for object detection, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 2799\u20132808.","DOI":"10.1109\/ICCVW54120.2021.00314"},{"key":"10.1016\/j.bspc.2026.110359_b39","series-title":"2022 International Joint Conference on Neural Networks","first-page":"1","article-title":"A novel hybrid CNN-transformer model for EEG motor imagery classification","author":"Ma","year":"2022"},{"key":"10.1016\/j.bspc.2026.110359_b40","first-page":"1","article-title":"4D attention-based neural network for EEG emotion recognition","author":"Xiao","year":"2022","journal-title":"Cogn. Neurodynamics"},{"issue":"2","key":"10.1016\/j.bspc.2026.110359_b41","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1109\/TII.2022.3197419","article-title":"Physics-informed attention temporal convolutional network for EEG-based motor imagery classification","volume":"19","author":"Altaheri","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.bspc.2026.110359_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104835","article-title":"EEG emotion recognition using attention-based convolutional transformer neural network","volume":"84","author":"Gong","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"issue":"15","key":"10.1016\/j.bspc.2026.110359_b43","doi-asserted-by":"crossref","first-page":"5092","DOI":"10.3390\/s21155092","article-title":"EEG-based emotion recognition by convolutional neural network with multi-scale kernels","volume":"21","author":"Phan","year":"2021","journal-title":"Sensors"},{"issue":"22","key":"10.1016\/j.bspc.2026.110359_b44","doi-asserted-by":"crossref","first-page":"6526","DOI":"10.3390\/s20226526","article-title":"Deep-asymmetry: Asymmetry matrix image for deep learning method in pre-screening depression","volume":"20","author":"Kang","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.bspc.2026.110359_b45","series-title":"Kan: Kolmogorov-arnold networks","author":"Liu","year":"2024"},{"issue":"1","key":"10.1016\/j.bspc.2026.110359_b46","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"Deap: A database for emotion analysis; using physiological signals","volume":"3","author":"Koelstra","year":"2011","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"3","key":"10.1016\/j.bspc.2026.110359_b47","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","article-title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","volume":"7","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Auton. Ment. Dev."},{"issue":"3","key":"10.1016\/j.bspc.2026.110359_b48","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1109\/TCYB.2018.2797176","article-title":"Emotionmeter: A multimodal framework for recognizing human emotions","volume":"49","author":"Zheng","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.bspc.2026.110359_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112599","article-title":"MAS-DGAT-Net: A dynamic graph attention network with multibranch feature extraction and staged fusion for EEG emotion recognition","volume":"305","author":"Liu","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.bspc.2026.110359_b50","unstructured":"I. Loshchilov, F. Hutter, SGDR: Stochastic Gradient Descent with Warm Restarts, in: International Conference on Learning Representations, 2022."},{"key":"10.1016\/j.bspc.2026.110359_b51","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"10.1016\/j.bspc.2026.110359_b52","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1023\/A:1022643204877","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"10.1016\/j.bspc.2026.110359_b53","series-title":"FBCNet: A multi-view convolutional neural network for brain-computer interface","author":"Mane","year":"2021"},{"issue":"5","key":"10.1016\/j.bspc.2026.110359_b54","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."},{"key":"10.1016\/j.bspc.2026.110359_b55","series-title":"Conformer: Convolution-augmented transformer for speech recognition","author":"Gulati","year":"2020"},{"issue":"7","key":"10.1016\/j.bspc.2026.110359_b56","doi-asserted-by":"crossref","first-page":"2533","DOI":"10.1109\/JBHI.2021.3049119","article-title":"FLDNet: Frame-level distilling neural network for EEG emotion recognition","volume":"25","author":"Wang","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"10.1016\/j.bspc.2026.110359_b57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11571-025-10239-9","article-title":"BISNN: bio-information-fused spiking neural networks for enhanced EEG-based emotion recognition","volume":"19","author":"Sun","year":"2025","journal-title":"Cogn. Neurodynamics"},{"key":"10.1016\/j.bspc.2026.110359_b58","doi-asserted-by":"crossref","DOI":"10.3389\/fsysb.2025.1715692","article-title":"Neural networks and foundation models: Two strategies for EEG-to-fMRI prediction","volume":"5","author":"Donoso","year":"2025","journal-title":"Front. Syst. Biol."},{"key":"10.1016\/j.bspc.2026.110359_b59","series-title":"2025 IEEE 8th International Conference on Soft Robotics","first-page":"1","article-title":"Human emotion-mediated soft robotic arts: Exploring the intersection of human emotions, soft robotics and arts","author":"Nadipineni","year":"2025"},{"key":"10.1016\/j.bspc.2026.110359_b60","doi-asserted-by":"crossref","DOI":"10.3389\/fnhum.2025.1623331","article-title":"High gamma EEG responses to emotional stimuli in virtual reality: insights from local activation and distributed characteristics","volume":"19","author":"Xiao","year":"2025","journal-title":"Front. Hum. Neurosci."},{"key":"10.1016\/j.bspc.2026.110359_b61","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.107054","article-title":"Study on multidimensional emotion recognition fusing dynamic brain network features in EEG signals","volume":"100","author":"Wu","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110359_b62","doi-asserted-by":"crossref","first-page":"224","DOI":"10.3389\/fnbeh.2017.00224","article-title":"Frontal EEG asymmetry of mood: a mini-review","volume":"11","author":"Palmiero","year":"2017","journal-title":"Front. Behav. Neurosci."},{"issue":"10","key":"10.1016\/j.bspc.2026.110359_b63","doi-asserted-by":"crossref","first-page":"15439","DOI":"10.1007\/s11042-022-14011-7","article-title":"Valence-arousal classification of emotion evoked by Chinese ancient-style music using 1D-CNN-BiLSTM model on EEG signals for college students","volume":"82","author":"Du","year":"2023","journal-title":"Multimedia Tools Appl."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009134?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009134?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T23:43:09Z","timestamp":1777592589000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426009134"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":63,"alternative-id":["S1746809426009134"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110359","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":"HLF-CIKNet: A spatial-frequency and cross-interaction feature extraction network for EEG-based emotion recognition","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110359","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":"110359"}}