{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:24:47Z","timestamp":1781108687223,"version":"3.54.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T00:00:00Z","timestamp":1644364800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T00:00:00Z","timestamp":1644364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s00521-022-06942-x","type":"journal-article","created":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T11:04:51Z","timestamp":1644404691000},"page":"13291-13303","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6997-4298","authenticated-orcid":false,"given":"Nandini","family":"Kumari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shamama","family":"Anwar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vandana","family":"Bhattacharjee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,2,9]]},"reference":[{"issue":"5596","key":"6942_CR1","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1126\/science.1076358","volume":"298","author":"RJ Dolan","year":"2002","unstructured":"Dolan RJ (2002) Emotion, cognition, and behavior. Science 298(5596):1191\u20131194","journal-title":"Science"},{"issue":"12","key":"6942_CR2","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.3390\/app7121239","volume":"7","author":"A Al-Nafjan","year":"2017","unstructured":"Al-Nafjan A, Hosny M, Al-Ohali Y, Al-Wabil A (2017) Review and classification of emotion recognition based on EEG brain-computer interface system research: a systematic review. Appl Sci 7(12):1239","journal-title":"Appl Sci"},{"issue":"1","key":"6942_CR3","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/s00779-011-0479-9","volume":"17","author":"EL Van den Broek","year":"2013","unstructured":"Van den Broek EL (2013) Ubiquitous emotion-aware computing. Pers Ubiquitous Comput 17(1):53\u201367","journal-title":"Pers Ubiquitous Comput"},{"key":"6942_CR4","doi-asserted-by":"crossref","unstructured":"Malandrakis N, Potamianos A, Evangelopoulos G, Zlatintsi A (2011) A supervised approach to movie emotion tracking. In: 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2376\u20132379","DOI":"10.1109\/ICASSP.2011.5946961"},{"key":"6942_CR5","doi-asserted-by":"crossref","unstructured":"Aslam AR, Altaf MAB (2019) An 8 channel patient specific neuromorphic processor for the early screening of autistic children through emotion detection. In: 2019 IEEE international symposium on circuits and systems (ISCAS). IEEE, pp 1\u20135","DOI":"10.1109\/ISCAS.2019.8702738"},{"key":"6942_CR6","doi-asserted-by":"crossref","unstructured":"Liu Y, Sourina O, Nguyen MK (2011) Real-time EEG-based emotion recognition and its applications. In: Transactions on computational science XII. Springer, pp 256\u2013277","DOI":"10.1007\/978-3-642-22336-5_13"},{"issue":"7","key":"6942_CR7","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TBME.2010.2048568","volume":"57","author":"YP Lin","year":"2010","unstructured":"Lin YP, Wang CH, Jung TP, Wu TL, Jeng SK, Duann JR, Chen JH (2010) EEG-based emotion recognition in music listening. IEEE Trans Biomed Eng 57(7):1798\u20131806","journal-title":"IEEE Trans Biomed Eng"},{"key":"6942_CR8","doi-asserted-by":"crossref","unstructured":"Yang Y, Wu Q, Fu Y, Chen X (2018) Continuous convolutional neural network with 3d input for EEG-based emotion recognition. In: International conference on neural information processing. Springer, pp 433\u2013443","DOI":"10.1007\/978-3-030-04239-4_39"},{"issue":"11","key":"6942_CR9","doi-asserted-by":"publisher","first-page":"4017","DOI":"10.1109\/TCYB.2018.2859482","volume":"49","author":"W Wu","year":"2018","unstructured":"Wu W, Yin Y, Wang X, Xu D (2018) Face detection with different scales based on faster r-CNN. IEEE Trans Cybern 49(11):4017\u20134028","journal-title":"IEEE Trans Cybern"},{"key":"6942_CR10","doi-asserted-by":"crossref","unstructured":"Zheng WL, Zhu JY, Peng Y, Lu BL (2014) EEG-based emotion classification using deep belief networks. In: 2014 IEEE international conference on multimedia and expo (ICME). IEEE, pp 1\u20136","DOI":"10.1109\/ICME.2014.6890166"},{"key":"6942_CR11","doi-asserted-by":"crossref","unstructured":"Sun B, Wei Q, Li L, Xu Q, He J, Yu L (2016) LSTM for dynamic emotion and group emotion recognition in the wild. In: Proceedings of the 18th ACM international conference on multimodal interaction, pp 451\u2013457","DOI":"10.1145\/2993148.2997640"},{"issue":"9","key":"6942_CR12","doi-asserted-by":"publisher","first-page":"2212","DOI":"10.3390\/s19092212","volume":"19","author":"H Chao","year":"2019","unstructured":"Chao H, Dong L, Liu Y, Lu B (2019) Emotion recognition from multiband EEG signals using CapsNet. Sensors 19(9):2212","journal-title":"Sensors"},{"key":"6942_CR13","doi-asserted-by":"publisher","first-page":"7790","DOI":"10.1109\/TIP.2021.3109518","volume":"30","author":"W Zhou","year":"2021","unstructured":"Zhou W, Liu J, Lei J, Yu L, Hwang JN (2021) Gmnet: graded-feature multilabel-learning network for RGB-thermal urban scene semantic segmentation. IEEE Trans Image Process 30:7790\u20137802","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"6942_CR14","doi-asserted-by":"publisher","first-page":"894","DOI":"10.1109\/TRO.2020.2981822","volume":"36","author":"L Ding","year":"2020","unstructured":"Ding L, Huang L, Li S, Gao H, Deng H, Li Y, Liu G (2020) Definition and application of variable resistance coefficient for wheeled mobile robots on deformable terrain. IEEE Trans Robot 36(3):894\u2013909","journal-title":"IEEE Trans Robot"},{"key":"6942_CR15","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"21","key":"6942_CR16","doi-asserted-by":"publisher","first-page":"4736","DOI":"10.3390\/s19214736","volume":"19","author":"H Yang","year":"2019","unstructured":"Yang H, Han J, Min K (2019) A multi-column CNN model for emotion recognition from EEG signals. Sensors 19(21):4736","journal-title":"Sensors"},{"issue":"4","key":"6942_CR17","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1587\/transinf.2015EDP7251","volume":"99","author":"N Thammasan","year":"2016","unstructured":"Thammasan N, Moriyama K, Ki Fukui, Numao M (2016) Continuous music-emotion recognition based on electroencephalogram. IEICE Trans Inf Syst 99(4):1234\u20131241","journal-title":"IEICE Trans Inf Syst"},{"key":"6942_CR18","doi-asserted-by":"publisher","first-page":"54","DOI":"10.3389\/fnins.2015.00054","volume":"9","author":"JR Estepp","year":"2015","unstructured":"Estepp JR, Christensen JC (2015) Electrode replacement does not affect classification accuracy in dual-session use of a passive brain-computer interface for assessing cognitive workload. Front Neurosci 9:54","journal-title":"Front Neurosci"},{"issue":"3","key":"6942_CR19","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","volume":"7","author":"WL Zheng","year":"2015","unstructured":"Zheng WL, Lu BL (2015) Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans Auton Mental Dev 7(3):162\u2013175","journal-title":"IEEE Trans Auton Mental Dev"},{"issue":"6","key":"6942_CR20","doi-asserted-by":"publisher","first-page":"1365","DOI":"10.1007\/s11760-013-0591-6","volume":"9","author":"M Naji","year":"2015","unstructured":"Naji M, Firoozabadi M, Azadfallah P (2015) Emotion classification during music listening from forehead biosignals. Signal Image Video Process 9(6):1365\u20131375","journal-title":"Signal Image Video Process"},{"key":"6942_CR21","doi-asserted-by":"crossref","unstructured":"Liu F, Zhang G, Lu J (2020) Multi-source heterogeneous unsupervised domain adaptation via fuzzy-relation neural networks. IEEE Trans Fuzzy Syst","DOI":"10.1109\/TFUZZ.2020.3018191"},{"key":"6942_CR22","doi-asserted-by":"crossref","unstructured":"Dong J, Cong Y, Sun G, Fang Z, Ding Z (2021) Where and how to transfer: knowledge aggregation-induced transferability perception for unsupervised domain adaptation. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2021.3128560"},{"key":"6942_CR23","unstructured":"Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Advances in neural information processing systems, pp 3856\u20133866"},{"key":"6942_CR24","doi-asserted-by":"crossref","unstructured":"Wang Y, Sun A, Huang M, Zhu X (2019) Aspect-level sentiment analysis using as-capsules. In: The world wide web conference, pp 2033\u20132044","DOI":"10.1145\/3308558.3313750"},{"key":"6942_CR25","doi-asserted-by":"crossref","unstructured":"Turan MAT, Erzin E (2018) Monitoring infant\u2019s emotional cry in domestic environments using the capsule network architecture. In: Interspeech, pp 132\u2013136","DOI":"10.21437\/Interspeech.2018-2187"},{"issue":"7","key":"6942_CR26","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1109\/LGRS.2019.2891076","volume":"16","author":"J Yin","year":"2019","unstructured":"Yin J, Li S, Zhu H, Luo X (2019) Hyperspectral image classification using CAPSNET with well-initialized shallow layers. IEEE Geosci Remote Sens Lett 16(7):1095\u20131099","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"103","key":"6942_CR27","first-page":"927","volume":"123","author":"Y Liu","year":"2020","unstructured":"Liu Y, Ding Y, Li C, Cheng J, Song R, Wan F, Chen X (2020) Multi-channel EEG-based emotion recognition via a multi-level features guided capsule network. Comput Biol Med 123(103):927","journal-title":"Comput Biol Med"},{"issue":"1","key":"6942_CR28","first-page":"434","volume":"11","author":"U Ali","year":"2020","unstructured":"Ali U, Li H, Yao R, Wang Q, Hussain W, ud Duja SB, Amjad M, Ahmed B (2020) EEG emotion signal of artificial neural network by using capsule network. Int J Adv Comput Sci Appl 11(1):434\u2013443","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"1","key":"6942_CR29","doi-asserted-by":"publisher","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 JS, 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"},{"key":"6942_CR30","unstructured":"Correa JAM, Abadi MK, Sebe N, Patras I (2018) Amigos: a dataset for affect, personality and mood research on individuals and groups. IEEE Trans Affect Comput"},{"issue":"1","key":"6942_CR31","doi-asserted-by":"publisher","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"},{"key":"6942_CR32","doi-asserted-by":"crossref","unstructured":"Rahman MA, Anjum A, Milu MMH, Khanam F, Uddin MS, Mollah MN (2021) Emotion recognition from EEG-based relative power spectral topography using convolutional neural network. Array, p 100072","DOI":"10.1016\/j.array.2021.100072"},{"key":"6942_CR33","doi-asserted-by":"crossref","unstructured":"Feng Y, Zhang B, Liu Y, Niu Z, Dai B, Fan Y, Chen X (2021) A 200\u2013225-GHZ manifold-coupled multiplexer utilizing metal waveguides. IEEE Trans Microw Theory Tech","DOI":"10.1109\/TMTT.2021.3119316"},{"key":"6942_CR34","doi-asserted-by":"crossref","unstructured":"Jiang Y, Li X (2021) Broadband cancellation method in an adaptive co-site interference cancellation system. Int J Electron (just-accepted)","DOI":"10.1109\/ICWCSG53609.2021.00019"},{"issue":"125","key":"6942_CR35","first-page":"589","volume":"306","author":"Y Yan","year":"2020","unstructured":"Yan Y, Feng L, Shi M, Cui C, Liu Y (2020) Effect of plasma-activated water on the structure and in vitro digestibility of waxy and normal maize starches during heat-moisture treatment. Food Chem 306(125):589","journal-title":"Food Chem"},{"issue":"110","key":"6942_CR36","first-page":"677","volume":"138","author":"M Shi","year":"2021","unstructured":"Shi M, Wang F, Lan P, Zhang Y, Zhang M, Yan Y, Liu Y (2021) Effect of ultrasonic intensity on structure and properties of wheat starch-monoglyceride complex and its influence on quality of norther-style Chinese steamed bread. LWT 138(110):677","journal-title":"LWT"},{"issue":"13","key":"6942_CR37","doi-asserted-by":"publisher","first-page":"2854","DOI":"10.3390\/s19132854","volume":"19","author":"KW Ha","year":"2019","unstructured":"Ha KW, Jeong JW (2019) Motor imagery EEG classification using capsule networks. Sensors 19(13):2854","journal-title":"Sensors"},{"issue":"1","key":"6942_CR38","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/TNNLS.2020.2973760","volume":"32","author":"H Che","year":"2020","unstructured":"Che H, Wang J (2020) A two-timescale duplex neurodynamic approach to mixed-integer optimization. IEEE Trans Neural Netw Learn Syst 32(1):36\u201348","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"6942_CR39","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Tejada LA, Yoshimura N, Koike Y (2020) Classifier comparison using EEG features for emotion recognition process. In: 2020 IEEE 18th world symposium on applied machine intelligence and informatics (SAMI). IEEE, pp 225\u2013230","DOI":"10.1109\/SAMI48414.2020.9108746"},{"issue":"13","key":"6942_CR40","doi-asserted-by":"publisher","first-page":"6057","DOI":"10.1016\/j.eswa.2014.03.050","volume":"41","author":"SN Daimi","year":"2014","unstructured":"Daimi SN, Saha G (2014) Classification of emotions induced by music videos and correlation with participants\u2019 rating. Expert Syst Appl 41(13):6057\u20136065","journal-title":"Expert Syst Appl"},{"key":"6942_CR41","unstructured":"Li X, Zhang P, Song D, Yu G, Hou Y, Hu B (2015) EEG based emotion identification using unsupervised deep feature learning"},{"key":"6942_CR42","doi-asserted-by":"crossref","unstructured":"Jirayucharoensak S, Pan-Ngum S, Israsena P (2014) EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation. Sci World J","DOI":"10.1155\/2014\/627892"},{"key":"6942_CR43","doi-asserted-by":"crossref","unstructured":"Tripathi S, Acharya S, Sharma RD, Mittal S, Bhattacharya S (2017) Using deep and convolutional neural networks for accurate emotion classification on DEAP dataset. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, pp 4746\u20134752","DOI":"10.1609\/aaai.v31i2.19105"},{"key":"6942_CR44","doi-asserted-by":"crossref","unstructured":"Yang HC, Lee CC (2019) An attribute-invariant variational learning for emotion recognition using physiology. In: ICASSP 2019\u20132019 IEEE international conference on acoustics. Speech and signal processing (ICASSP). IEEE, pp 1184\u20131188","DOI":"10.1109\/ICASSP.2019.8683290"},{"key":"6942_CR45","unstructured":"Siddharth S, Jung TP, Sejnowski TJ (2019) Utilizing deep learning towards multi-modal bio-sensing and vision-based affective computing. IEEE Trans Affect Comput 1\u20131"},{"key":"6942_CR46","doi-asserted-by":"publisher","first-page":"4566","DOI":"10.1109\/ACCESS.2020.3045078","volume":"9","author":"X Liu","year":"2020","unstructured":"Liu X, Xie L, Wang Y, Zou J, Xiong J, Ying Z, Vasilakos AV (2020) Privacy and security issues in deep learning: a survey. IEEE Access 9:4566\u20134593","journal-title":"IEEE Access"},{"key":"6942_CR47","doi-asserted-by":"crossref","unstructured":"Debie E, Moustafa N, Vasilakos A (2021) Session invariant EEG signatures using elicitation protocol fusion and convolutional neural network. IEEE Trans Dependable Secure Comput","DOI":"10.1109\/TDSC.2021.3060775"},{"issue":"12","key":"6942_CR48","doi-asserted-by":"publisher","first-page":"2089","DOI":"10.1109\/JPROC.2011.2165330","volume":"99","author":"Z Shen","year":"2011","unstructured":"Shen Z, Luo J, Zimmermann R, Vasilakos AV (2011) Peer-to-peer media streaming: insights and new developments. Proc IEEE 99(12):2089\u20132109","journal-title":"Proc IEEE"},{"key":"6942_CR49","doi-asserted-by":"crossref","unstructured":"Afshar P, Mohammadi A, Plataniotis KN (2018) Brain tumor type classification via capsule networks. In: 25th IEEE international conference on image processing (ICIP) (pp 3129-3133). IEEE","DOI":"10.1109\/ICIP.2018.8451379"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-06942-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-06942-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-06942-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,24]],"date-time":"2022-07-24T10:04:37Z","timestamp":1658657077000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-06942-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,9]]},"references-count":49,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["6942"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-06942-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,9]]},"assertion":[{"value":"25 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}