{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:26:07Z","timestamp":1770272767041,"version":"3.49.0"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T00:00:00Z","timestamp":1702944000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T00:00:00Z","timestamp":1702944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["(62173008, 61602017)"],"award-info":[{"award-number":["(62173008, 61602017)"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Natural Science Foundation","award":["(No.4222022)"],"award-info":[{"award-number":["(No.4222022)"]}]},{"name":"Education and Teaching Research Project of Beijing University of Technology","award":["(ER2022SJB06)"],"award-info":[{"award-number":["(ER2022SJB06)"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00521-023-09289-z","type":"journal-article","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T07:02:10Z","timestamp":1702969330000},"page":"3903-3919","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["FAformer: parallel Fourier-attention architectures benefits EEG-based affective computing with enhanced spatial information"],"prefix":"10.1007","volume":"36","author":[{"given":"Ziheng","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,19]]},"reference":[{"issue":"10","key":"9289_CR1","first-page":"355","volume":"8","author":"S Alhagry","year":"2017","unstructured":"Alhagry S, Fahmy AA, El-Khoribi RA (2017) Emotion recognition based on EEG using LSTM recurrent neural network. Int J Adv Comput Sci Appl 8(10):355\u2013358","journal-title":"Int J Adv Comput Sci Appl"},{"key":"9289_CR2","doi-asserted-by":"crossref","unstructured":"Li X, Song D, Zhang P, Yu G, Hou Y, Hu B (2016) Emotion recognition from multi-channel EEG data through convolutional recurrent neural network. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 352\u2013359","DOI":"10.1109\/BIBM.2016.7822545"},{"issue":"2","key":"9289_CR3","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1007\/s00521-022-07843-9","volume":"35","author":"O Almanza-Conejo","year":"2023","unstructured":"Almanza-Conejo O, Almanza-Ojeda DL, Contreras-Hernandez JL, Ibarra-Manzano MA (2023) Emotion recognition in EEG signals using the continuous wavelet transform and cnns. Neural Comput Appl 35(2):1409\u20131422","journal-title":"Neural Comput Appl"},{"issue":"5","key":"9289_CR4","first-page":"499","volume":"23","author":"DS Bassett","year":"2017","unstructured":"Bassett DS, Bullmore ET (2017) Small-world brain networks revisited. The Neurosci 23(5):499\u2013516","journal-title":"The Neurosci"},{"issue":"7","key":"9289_CR5","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1038\/nrn3963","volume":"16","author":"G Deco","year":"2015","unstructured":"Deco G, Tononi G, Boly M, Kringelbach ML (2015) Rethinking segregation and integration: contributions of whole-brain modelling. Nat Rev Neurosci 16(7):430\u2013439","journal-title":"Nat Rev Neurosci"},{"issue":"18","key":"9289_CR6","doi-asserted-by":"crossref","first-page":"4039","DOI":"10.1093\/cercor\/bhab464","volume":"32","author":"G Kastrati","year":"2022","unstructured":"Kastrati G, Thompson WH, Schiffler B, Fransson P, Jensen KB (2022) Brain network segregation and integration during painful thermal stimulation. Cereb Cortex 32(18):4039\u20134049","journal-title":"Cereb Cortex"},{"issue":"23","key":"9289_CR7","doi-asserted-by":"crossref","first-page":"2022288118","DOI":"10.1073\/pnas.2022288118","volume":"118","author":"R Wang","year":"2021","unstructured":"Wang R, Liu M, Cheng X, Wu Y, Hildebrandt A, Zhou C (2021) Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities. Proc Nat Acad Sci 118(23):2022288118","journal-title":"Proc Nat Acad Sci"},{"issue":"4","key":"9289_CR8","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1109\/TCDS.2020.2976112","volume":"13","author":"Z Gao","year":"2020","unstructured":"Gao Z, Wang X, Yang Y, Li Y, Ma K, Chen G (2020) A channel-fused dense convolutional network for EEG-based emotion recognition. IEEE Trans Cognit Dev Syst 13(4):945\u2013954","journal-title":"IEEE Trans Cognit Dev Syst"},{"issue":"24","key":"9289_CR9","doi-asserted-by":"crossref","first-page":"22241","DOI":"10.1007\/s00521-022-07643-1","volume":"34","author":"J Bi","year":"2022","unstructured":"Bi J, Wang F, Yan X, Ping J, Wen Y (2022) Multi-domain fusion deep graph convolution neural network for EEG emotion recognition. Neural Comput Appl 34(24):22241\u201322255","journal-title":"Neural Comput Appl"},{"key":"9289_CR10","doi-asserted-by":"crossref","unstructured":"Liu Y, Zhou Y, Zhang D (2022) Tct: Temporal and channel transformer for EEG-based emotion recognition. In: 2022 IEEE International Symposium on Computer-Based Medical Systems (CBMS), pp 366\u2013371","DOI":"10.1109\/CBMS55023.2022.00072"},{"key":"9289_CR11","volume":"152","author":"Y Wei","year":"2023","unstructured":"Wei Y, Liu Y, Li C, Cheng J, Song R, Chen X (2023) Tc-net: a transformer capsule network for EEG-based emotion recognition. Comput Biol Med 152:106463","journal-title":"Comput Biol Med"},{"issue":"16","key":"9289_CR12","doi-asserted-by":"crossref","first-page":"13291","DOI":"10.1007\/s00521-022-06942-x","volume":"34","author":"N Kumari","year":"2022","unstructured":"Kumari N, Anwar S, Bhattacharjee V (2022) Time series-dependent feature of EEG signals for improved visually evoked emotion classification using emotioncapsnet. Neural Comput Appl 34(16):13291\u201313303","journal-title":"Neural Comput Appl"},{"key":"9289_CR13","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An image is worth 16x16 words: transformers for image recognition at scale. In: 2021 International Conference on Learning Representations (ICLR), https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"9289_CR14","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inform Process Syst 30:5998\u20136008","journal-title":"Adv Neural Inform Process Syst"},{"key":"9289_CR15","volume":"603","author":"J-Y Guo","year":"2022","unstructured":"Guo J-Y, Cai Q, An J-P, Chen P-Y, Ma C, Wan J-H, Gao Z-K (2022) A transformer based neural network for emotion recognition and visualizations of crucial EEG channels. Physica A 603:127700","journal-title":"Physica A"},{"issue":"1","key":"9289_CR16","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.neuron.2014.05.014","volume":"83","author":"MW Cole","year":"2014","unstructured":"Cole MW, Bassett DS, Power JD, Braver TS, Petersen SE (2014) Intrinsic and task-evoked network architectures of the human brain. Neuron 83(1):238\u2013251","journal-title":"Neuron"},{"key":"9289_CR17","doi-asserted-by":"crossref","first-page":"525","DOI":"10.3389\/fnins.2018.00525","volume":"12","author":"Y Du","year":"2018","unstructured":"Du Y, Fu Z, Calhoun VD (2018) Classification and prediction of brain disorders using functional connectivity: promising but challenging. Front Neurosci 12:525","journal-title":"Front Neurosci"},{"issue":"11","key":"9289_CR18","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1038\/nn.4135","volume":"18","author":"ES Finn","year":"2015","unstructured":"Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, Papademetris X, Constable RT (2015) Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci 18(11):1664\u20131671","journal-title":"Nat Neurosci"},{"issue":"3","key":"9289_CR19","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.biopsych.2014.08.009","volume":"77","author":"Q Gong","year":"2015","unstructured":"Gong Q, He Y (2015) Depression, neuroimaging and connectomics: a selective overview. Biol Psychiatry 77(3):223\u2013235","journal-title":"Biol Psychiatry"},{"key":"9289_CR20","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1016\/j.neuroimage.2016.10.020","volume":"146","author":"S Noble","year":"2017","unstructured":"Noble S, Scheinost D, Finn ES, Shen X, Papademetris X, McEwen SC, Bearden CE, Addington J, Goodyear B, Cadenhead KS (2017) Multisite reliability of mr-based functional connectivity. Neuroimage 146:959\u2013970","journal-title":"Neuroimage"},{"issue":"4","key":"9289_CR21","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1080\/02699930126048","volume":"15","author":"LA Schmidt","year":"2001","unstructured":"Schmidt LA, Trainor LJ (2001) Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions. Cognit Emot 15(4):487\u2013500","journal-title":"Cognit Emot"},{"key":"9289_CR22","unstructured":"Guibas J, Mardani M, Li Z, Tao A, Anandkumar A, Catanzaro B (2022) Efficient token mixing for transformers via adaptive fourier neural operators. In: 2022 International Conference on Learning Representations (ICLR), https:\/\/openreview.net\/forum?id=EXHG-A3jlM"},{"key":"9289_CR23","doi-asserted-by":"crossref","unstructured":"Nie D, Wang X-W, Shi L-C, Lu B-L (2011) EEG-based emotion recognition during watching movies. In: 2011 International IEEE\/EMBS Conference on Neural Engineering (NER), pp 667\u2013670","DOI":"10.1109\/NER.2011.5910636"},{"issue":"3","key":"9289_CR24","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","volume":"7","author":"W-L Zheng","year":"2015","unstructured":"Zheng W-L, Lu B-L (2015) Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans Auton Ment Dev 7(3):162\u2013175","journal-title":"IEEE Trans Auton Ment Dev"},{"key":"9289_CR25","volume":"122","author":"D Li","year":"2022","unstructured":"Li D, Xie L, Chai B, Wang Z, Yang H (2022) Spatial-frequency convolutional self-attention network for EEG emotion recognition. Appl Soft Comput 122:108740","journal-title":"Appl Soft Comput"},{"issue":"15","key":"9289_CR26","doi-asserted-by":"crossref","first-page":"5092","DOI":"10.3390\/s21155092","volume":"21","author":"T-D-T Phan","year":"2021","unstructured":"Phan T-D-T, Kim S-H, Yang H-J, Lee G-S (2021) EEG-based emotion recognition by convolutional neural network with multi-scale kernels. Sensors 21(15):5092","journal-title":"Sensors"},{"issue":"1","key":"9289_CR27","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/JBHI.2017.2688239","volume":"22","author":"S Katsigiannis","year":"2017","unstructured":"Katsigiannis S, Ramzan N (2017) Dreamer: a database for emotion recognition through EEG and ecg signals from wireless low-cost off-the-shelf devices. IEEE J Biomed Health Inform 22(1):98\u2013107","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"9289_CR28","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","volume":"3","author":"S Koelstra","year":"2011","unstructured":"Koelstra S, Muhl C, Soleymani M, Lee J-S, Yazdani A, Ebrahimi T, Pun T, Nijholt A, Patras I (2011) Deap: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput 3(1):18\u201331","journal-title":"IEEE Trans Affect Comput"},{"issue":"8","key":"9289_CR29","doi-asserted-by":"crossref","first-page":"329","DOI":"10.14569\/IJACSA.2018.090843","volume":"9","author":"ES Salama","year":"2018","unstructured":"Salama ES, El-Khoribi RA, Shoman ME, Shalaby MAW (2018) EEG-based emotion recognition using 3d convolutional neural networks. International Journal of Advanced Computer Science and Applications 9(8):329","journal-title":"International Journal of Advanced Computer Science and Applications"},{"issue":"3","key":"9289_CR30","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.1109\/TAFFC.2020.2994159","volume":"13","author":"P Zhong","year":"2020","unstructured":"Zhong P, Wang D, Miao C (2020) EEG-based emotion recognition using regularized graph neural networks. IEEE Trans Affect Comput 13(3):1290\u20131301","journal-title":"IEEE Trans Affect Comput"},{"key":"9289_CR31","doi-asserted-by":"crossref","unstructured":"Yang Y, Wu Q, Qiu M, Wang Y, Chen X (2018) Emotion recognition from multi-channel EEG through parallel convolutional recurrent neural network. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp 1\u20137","DOI":"10.1109\/IJCNN.2018.8489331"},{"key":"9289_CR32","doi-asserted-by":"crossref","unstructured":"He Y, Lu Z, Wang J, Shi J (2022) A channel attention based mlp-mixer network for motor imagery decoding with EEG. In: 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 1291\u20131295","DOI":"10.1109\/ICASSP43922.2022.9747488"},{"issue":"5","key":"9289_CR33","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.3390\/s18051383","volume":"18","author":"Y-H Kwon","year":"2018","unstructured":"Kwon Y-H, Shin S-B, Kim S-D (2018) Electroencephalography based fusion two-dimensional (2d)-convolution neural networks (cnn) model for emotion recognition system. Sensors 18(5):1383","journal-title":"Sensors"},{"issue":"4","key":"9289_CR34","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1109\/TCDS.2021.3051465","volume":"13","author":"D Li","year":"2021","unstructured":"Li D, Chai B, Wang Z, Yang H, Du W (2021) EEG emotion recognition based on 3-d feature representation and dilated fully convolutional networks. IEEE Trans Cognit Dev Syst 13(4):885\u2013897","journal-title":"IEEE Trans Cognit Dev Syst"},{"key":"9289_CR35","doi-asserted-by":"crossref","unstructured":"Hu D (2020) An introductory survey on attention mechanisms in nlp problems. In: 2019 Intelligent Systems Conference (IntelliSys) Vol 2, pp 432\u2013448","DOI":"10.1007\/978-3-030-29513-4_31"},{"key":"9289_CR36","unstructured":"Daniluk M, Rockt\u00e4schel T, Welbl J, Riedel S (2017) Frustratingly short attention spans in neural language modeling. In: 2017 International Conference on Learning Representations (ICLR), https:\/\/openreview.net\/forum?id=ByIAPUcee"},{"issue":"23","key":"9289_CR37","doi-asserted-by":"crossref","first-page":"6727","DOI":"10.3390\/s20236727","volume":"20","author":"Y Kim","year":"2020","unstructured":"Kim Y, Choi A (2020) EEG-based emotion classification using long short-term memory network with attention mechanism. Sensors 20(23):6727","journal-title":"Sensors"},{"key":"9289_CR38","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1109\/TNSRE.2022.3173724","volume":"30","author":"L Shen","year":"2022","unstructured":"Shen L, Sun M, Li Q, Li B, Pan Z, Lei J (2022) Multiscale temporal self-attention and dynamical graph convolution hybrid network for EEG-based stereogram recognition. IEEE Trans Neural Syst Rehabil Eng 30:1191\u20131202","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"9289_CR39","doi-asserted-by":"crossref","unstructured":"Jia Z, Lin Y, Wang J, Zhou R, Ning X, He Y, Zhao Y (2020) Graphsleepnet: adaptive spatial-temporal graph convolutional networks for sleep stage classification. In: 2020 International Joint Conference on Artificial Intelligence (IJCAI), pp 1324\u20131330","DOI":"10.24963\/ijcai.2020\/184"},{"key":"9289_CR40","unstructured":"Ding C, Liao S, Wang Y, Li Z, Liu N, Zhuo Y, Wang C, Qian X, Bai Y, Yuan G (2017) Circnn: accelerating and compressing deep neural networks using block-circulant weight matrices. In: 2017 Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO), pp 395\u2013408"},{"key":"9289_CR41","doi-asserted-by":"crossref","unstructured":"Lee J-H, Heo M, Kim K-R, Kim C-S (2018) Single-image depth estimation based on fourier domain analysis. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 330\u2013339","DOI":"10.1109\/CVPR.2018.00042"},{"key":"9289_CR42","doi-asserted-by":"crossref","unstructured":"Li S, Xue K, Zhu B, Ding C, Gao X, Wei D, Wan T (2020) Falcon: a fourier transform based approach for fast and secure convolutional neural network predictions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 8705\u20138714","DOI":"10.1109\/CVPR42600.2020.00873"},{"key":"9289_CR43","doi-asserted-by":"crossref","unstructured":"Yang Y, Soatto S (2020) Fda: Fourier domain adaptation for semantic segmentation. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 4085\u20134095","DOI":"10.1109\/CVPR42600.2020.00414"},{"key":"9289_CR44","doi-asserted-by":"publisher","unstructured":"Lee-Thorp J, Ainslie J, Eckstein I, Ontanon S (2022) Fnet: mixing tokens with fourier transforms. In: 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT). https:\/\/doi.org\/10.18653\/v1\/2022.naacl-main.319","DOI":"10.18653\/v1\/2022.naacl-main.319"},{"key":"9289_CR45","first-page":"980","volume":"34","author":"Y Rao","year":"2021","unstructured":"Rao Y, Zhao W, Zhu Z, Lu J, Zhou J (2021) Global filter networks for image classification. Adv Neural Inform Process Syst 34:980\u2013993","journal-title":"Adv Neural Inform Process Syst"},{"key":"9289_CR46","doi-asserted-by":"crossref","unstructured":"Gidaris S, Bursuc A, Komodakis N, P\u00e9rez P, Cord M (2019) Boosting few-shot visual learning with self-supervision. In: 2019 IEEE\/CVF International Conference on Computer Vision (CVPR), pp 8059\u20138068","DOI":"10.1109\/ICCV.2019.00815"},{"key":"9289_CR47","unstructured":"Gidaris S, Singh P, Komodakis N (2018) Unsupervised representation learning by predicting image rotations. In: 2018 International Conference on Learning Representations (ICLR). https:\/\/openreview.net\/forum?id=S1v4N2l0-"},{"key":"9289_CR48","doi-asserted-by":"crossref","unstructured":"Sermanet P, Lynch C, Chebotar Y, Hsu J, Jang E, Schaal S, Levine S, Brain G (2018) Time-contrastive networks: Self-supervised learning from video. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp 1134\u20131141","DOI":"10.1109\/ICRA.2018.8462891"},{"key":"9289_CR49","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/JSTARS.2020.3036602","volume":"14","author":"Y Yuan","year":"2020","unstructured":"Yuan Y, Lin L (2020) Self-supervised pretraining of transformers for satellite image time series classification. IEEE J Select Top Appl Earth Observ Remote Sens 14:474\u2013487","journal-title":"IEEE J Select Top Appl Earth Observ Remote Sens"},{"issue":"9","key":"9289_CR50","doi-asserted-by":"crossref","first-page":"3942","DOI":"10.1109\/TNNLS.2020.3016291","volume":"32","author":"Q Ma","year":"2020","unstructured":"Ma Q, Li S, Zhuang W, Wang J, Zeng D (2020) Self-supervised time series clustering with model-based dynamics. IEEE Trans Neural Netw Learn Syst 32(9):3942\u20133955","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9289_CR51","doi-asserted-by":"crossref","unstructured":"Banville H, Albuquerque I, Hyv\u00e4rinen A, Moffat G, Engemann D-A, Gramfort A (2019) Self-supervised representation learning from electroencephalography signals. In: 2019 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp 1\u20136","DOI":"10.1109\/MLSP.2019.8918693"},{"key":"9289_CR52","volume":"116","author":"L Xi","year":"2022","unstructured":"Xi L, Yun Z, Liu H, Wang R, Huang X, Fan H (2022) Semi-supervised time series classification model with self-supervised learning. Eng Appl Artif Intell 116:105331","journal-title":"Eng Appl Artif Intell"},{"key":"9289_CR53","doi-asserted-by":"crossref","unstructured":"Jin M, Chen H, Li Z, Li J (2021) EEG-based emotion recognition using graph convolutional network with learnable electrode relations. In: 2021 Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp 5953\u20135957","DOI":"10.1109\/EMBC46164.2021.9630195"},{"issue":"3","key":"9289_CR54","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","volume":"11","author":"T Song","year":"2018","unstructured":"Song T, Zheng W, Song P, Cui Z (2018) EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans Affect Comput 11(3):532\u2013541","journal-title":"IEEE Trans Affect Comput"},{"key":"9289_CR55","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: 2015 International Conference on Learning Representations (ICLR),"},{"issue":"10","key":"9289_CR56","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.3390\/app7101060","volume":"7","author":"Y Li","year":"2017","unstructured":"Li Y, Huang J, Zhou H, Zhong N (2017) Human emotion recognition with electroencephalographic multidimensional features by hybrid deep neural networks. Appl Sci 7(10):1060","journal-title":"Appl Sci"},{"key":"9289_CR57","unstructured":"Hendrycks D, Gimpel K (2016) Bridging nonlinearities and stochastic regularizers with gaussian error linear units. arxiv:1606.08415"},{"key":"9289_CR58","first-page":"19448","volume":"34","author":"F-E Yang","year":"2021","unstructured":"Yang F-E, Cheng Y-C, Shiau Z-Y, Wang Y-CF (2021) Adversarial teacher-student representation learning for domain generalization. Adv Neural Inform Process Syst 34:19448\u201319460","journal-title":"Adv Neural Inform Process Syst"},{"issue":"2","key":"9289_CR59","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/JBHI.2020.2995767","volume":"25","author":"J Cheng","year":"2020","unstructured":"Cheng J, Chen M, Li C, Liu Y, Song R, Liu A, Chen X (2020) Emotion recognition from multi-channel EEG via deep forest. IEEE J Biomed Health Inform 25(2):453\u2013464","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"9289_CR60","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1109\/TAFFC.2020.3025777","volume":"14","author":"W Tao","year":"2020","unstructured":"Tao W, Li C, Song R, Cheng J, Liu Y, Wan F, Chen X (2020) EEG-based emotion recognition via channel-wise attention and self-attention. IEEE Trans Affect Comput 14(1):382\u2013393","journal-title":"IEEE Trans Affect Comput"},{"issue":"5","key":"9289_CR61","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1109\/LSP.2019.2906824","volume":"26","author":"D Zhang","year":"2019","unstructured":"Zhang D, Yao L, Chen K, Monaghan J (2019) A convolutional recurrent attention model for subject-independent EEG signal analysis. IEEE Signal Process Lett 26(5):715\u2013719","journal-title":"IEEE Signal Process Lett"},{"key":"9289_CR62","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.neucom.2021.03.105","volume":"86","author":"D Huang","year":"2021","unstructured":"Huang D, Chen S, Liu C, Zheng L, Tian Z, Jiang D (2021) Differences first in asymmetric brain: a bi-hemisphere discrepancy convolutional neural network for EEG emotion recognition. Neurocomputing 86:140\u2013151","journal-title":"Neurocomputing"},{"key":"9289_CR63","volume":"149","author":"Y Wu","year":"2022","unstructured":"Wu Y, Xia M, Nie L, Zhang Y, Fan A (2022) Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition. Comput Biol Med 149:106002","journal-title":"Comput Biol Med"},{"key":"9289_CR64","unstructured":"Peng Y, Dalmia S, Lane I, Watanabe S (2022) Branchformer: Parallel mlp-attention architectures to capture local and global context for speech recognition and understanding. In: 2022 International Conference on Machine Learning (ICML), pp 17627\u201317643"},{"key":"9289_CR65","first-page":"10105","volume":"34","author":"J Bai","year":"2021","unstructured":"Bai J, Wang W, Gomes CP (2021) Contrastively disentangled sequential variational autoencoder. Adv Neural Inform Process Syst 34:10105\u201310118","journal-title":"Adv Neural Inform Process Syst"},{"key":"9289_CR66","doi-asserted-by":"crossref","unstructured":"Zhu Y, Min MR, Kadav A, Graf HP (2020) S3vae: self-supervised sequential vae for representation disentanglement and data generation. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 6538\u20136547","DOI":"10.1109\/CVPR42600.2020.00657"},{"issue":"4","key":"9289_CR67","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s40708-017-0069-3","volume":"4","author":"MS \u00d6zerdem","year":"2017","unstructured":"\u00d6zerdem MS, Polat H (2017) Emotion recognition based on EEG features in movie clips with channel selection. Brain Inform 4(4):241\u2013252","journal-title":"Brain Inform"},{"key":"9289_CR68","doi-asserted-by":"crossref","unstructured":"Tong L, Zhao J, Fu W (2018) Emotion recognition and channel selection based on EEG signal. In: 2018 International Conference on Intelligent Computation Technology and Automation (ICICTA), pp 101\u2013105","DOI":"10.1109\/ICICTA.2018.00031"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09289-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09289-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09289-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T10:19:10Z","timestamp":1707733150000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09289-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,19]]},"references-count":68,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9289"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09289-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,19]]},"assertion":[{"value":"6 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}