{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T05:19:45Z","timestamp":1754111985837,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:00:00Z","timestamp":1642118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:00:00Z","timestamp":1642118400000},"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":["Soft Comput"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s00500-021-06578-4","type":"journal-article","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:03:06Z","timestamp":1642118586000},"page":"10563-10570","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["EmoPercept: EEG-based emotion classification through perceiver"],"prefix":"10.1007","volume":"26","author":[{"family":"Aadam","sequence":"first","affiliation":[]},{"given":"Abdallah","family":"Tubaishat","sequence":"additional","affiliation":[]},{"given":"Feras","family":"Al-Obeidat","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3094-3483","authenticated-orcid":false,"given":"Zahid","family":"Halim","sequence":"additional","affiliation":[]},{"given":"Muhammad","family":"Waqas","sequence":"additional","affiliation":[]},{"given":"Fawad","family":"Qayum","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,14]]},"reference":[{"key":"6578_CR1","doi-asserted-by":"publisher","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 (IJACSA). 8(10). https:\/\/doi.org\/10.14569\/IJACSA.2017.081046","DOI":"10.14569\/IJACSA.2017.081046"},{"issue":"1","key":"6578_CR2","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/TSMCB.2005.854502","volume":"36","author":"K Anderson","year":"2006","unstructured":"Anderson K, McOwan P (2006) A real-time automated system for the recognition of human facial expressions. IEEE Trans Syst Man Cybernet Part B (Cybernet) 36(1):96\u2013105. https:\/\/doi.org\/10.1109\/TSMCB.2005.854502","journal-title":"IEEE Trans Syst Man Cybernet Part B (Cybernet)"},{"key":"6578_CR3","unstructured":"Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler DM, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodei D (2020) Language Models are Few-Shot Learners. arXiv:2005.14165 [cs]"},{"issue":"9","key":"6578_CR4","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. https:\/\/doi.org\/10.3390\/s19092212","journal-title":"Sensors"},{"key":"6578_CR5","doi-asserted-by":"publisher","first-page":"118530","DOI":"10.1109\/ACCESS.2019.2936817","volume":"7","author":"JX Chen","year":"2019","unstructured":"Chen JX, Jiang DM, Zhang YN (2019) A hierarchical bidirectional GRU model with attention for EEG-based emotion classification. IEEE Access 7:118530\u2013118540. https:\/\/doi.org\/10.1109\/ACCESS.2019.2936817","journal-title":"IEEE Access"},{"key":"6578_CR6","doi-asserted-by":"crossref","unstructured":"Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning, ICML \u201908, New York, NY, USA. Association for Computing Machinery, pp 160\u2013167","DOI":"10.1145\/1390156.1390177"},{"key":"6578_CR7","doi-asserted-by":"crossref","unstructured":"Deng X, Zhu J, Yang S (2021) SFE-Net: EEG-based Emotion Recognition with Symmetrical Spatial Feature Extraction. arXiv:2104.06308 [cs, eess]","DOI":"10.1145\/3474085.3475403"},{"key":"6578_CR8","doi-asserted-by":"crossref","unstructured":"Ding Y, Robinson N, Zeng Q, Guan C (2021) April. TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition. arXiv:2104.02935 [cs]","DOI":"10.1109\/TAFFC.2022.3169001"},{"key":"6578_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2017.2751602","author":"Z Halim","year":"2017","unstructured":"Halim Z, Atif M, Rashid A, Edwin CA (2017) Profiling players using real-world datasets: clustering the data and correlating the results with the big-five personality traits. IEEE Trans Affect Comput. https:\/\/doi.org\/10.1109\/TAFFC.2017.2751602","journal-title":"IEEE Trans Affect Comput"},{"key":"6578_CR10","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.inffus.2019.06.006","volume":"53","author":"Z Halim","year":"2020","unstructured":"Halim Z, Rehan M (2020) On identification of driving-induced stress using electroencephalogram signals: a framework based on wearable safety-critical scheme and machine learning. Inf Fusion 53:66\u201379. https:\/\/doi.org\/10.1016\/j.inffus.2019.06.006","journal-title":"Inf Fusion"},{"key":"6578_CR11","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770\u2013778. ISSN: 1063-6919","DOI":"10.1109\/CVPR.2016.90"},{"key":"6578_CR12","unstructured":"Jaegle A, Gimeno F, Brock A, Zisserman A, Vinyals O, Carreira J (2021)Perceiver: general perception with iterative attention. arXiv:2103.03206 [cs, eess]"},{"issue":"1","key":"6578_CR13","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","volume":"3","author":"S Koelstra","year":"2012","unstructured":"Koelstra S, Muhl C, Soleymani M, Lee JS, Yazdani A, Ebrahimi T, Pun T, Nijholt A, Patras I (2012) DEAP: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput 3(1):18\u201331. https:\/\/doi.org\/10.1109\/T-AFFC.2011.15","journal-title":"IEEE Trans Affect Comput"},{"issue":"11","key":"6578_CR14","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc IEEE"},{"key":"6578_CR15","doi-asserted-by":"crossref","unstructured":"Liu H, Guo H, Hu W (2021) EEG-based Emotion Classification Using Joint Adaptation Networks. In 2021 IEEE international symposium on circuits and systems (ISCAS), pp 1\u20135. ISSN: 2158-1525","DOI":"10.1109\/ISCAS51556.2021.9401737"},{"key":"6578_CR16","doi-asserted-by":"crossref","unstructured":"Liu X, He P, Chen W, Gao J (2019) Multi-Task Deep Neural Networks for Natural Language Understanding. arXiv:1901.11504 [cs]","DOI":"10.18653\/v1\/P19-1441"},{"key":"6578_CR17","doi-asserted-by":"publisher","first-page":"103927","DOI":"10.1016\/j.compbiomed.2020.103927","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 123:103927. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103927","journal-title":"Comput Biol Med"},{"key":"6578_CR18","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin Transformer: hierarchical vision transformer using shifted windows. arXiv:2103.14030 [cs]","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"C","key":"6578_CR19","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.asoc.2016.08.039","volume":"49","author":"T Muhammad","year":"2016","unstructured":"Muhammad T, Halim Z (2016) Employing artificial neural networks for constructing metadata-based model to automatically select an appropriate data visualization technique. Appl Soft Comput 49(C):365\u2013384. https:\/\/doi.org\/10.1016\/j.asoc.2016.08.039","journal-title":"Appl Soft Comput"},{"issue":"3","key":"6578_CR20","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1016\/j.bbe.2020.04.005","volume":"40","author":"R Nawaz","year":"2020","unstructured":"Nawaz R, Cheah KH, Nisar H, Yap VV (2020) Comparison of different feature extraction methods for EEG-based emotion recognition. Biocybernet Biomed Eng 40(3):910\u2013926. https:\/\/doi.org\/10.1016\/j.bbe.2020.04.005","journal-title":"Biocybernet Biomed Eng"},{"key":"6578_CR21","unstructured":"Petrushin V (2000) Emotion in speech: recognition and application to call centers. Proceedings of artificial neural networks in engineering"},{"key":"6578_CR22","unstructured":"Ramesh A, Pavlov M, Goh G, Gray S, Voss C, Radford A, Chen M, Sutskever I (2021) Zero-Shot Text-to-Image Generation. arXiv:2102.12092 [cs]"},{"key":"6578_CR23","unstructured":"Sabour S, Frosst N, Hinton GE (2017) Dynamic Routing Between Capsules. arXiv:1710.09829 [cs]"},{"issue":"2","key":"6578_CR24","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1109\/T-AFFC.2011.37","volume":"3","author":"M Soleymani","year":"2012","unstructured":"Soleymani M, Pantic M, Pun T (2012) Multimodal emotion recognition in response to videos. IEEE Trans Affect Comput 3(2):211\u2013223. https:\/\/doi.org\/10.1109\/T-AFFC.2011.37","journal-title":"IEEE Trans Affect Comput"},{"issue":"3","key":"6578_CR25","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","volume":"11","author":"T Song","year":"2020","unstructured":"Song T, Zheng W, Song P, Cui Z (2020) EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans Affect Comput 11(3):532\u2013541. https:\/\/doi.org\/10.1109\/TAFFC.2018.2817622","journal-title":"IEEE Trans Affect Comput"},{"key":"6578_CR26","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2020.3025777","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. https:\/\/doi.org\/10.1109\/TAFFC.2020.3025777","journal-title":"IEEE Trans Affect Comput"},{"key":"6578_CR27","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 twenty-ninth IAAI conference","DOI":"10.1609\/aaai.v31i2.19105"},{"key":"6578_CR28","doi-asserted-by":"publisher","first-page":"107560","DOI":"10.1016\/j.knosys.2021.107560","volume":"234","author":"Uzma","year":"2021","unstructured":"Uzma, Halim Z (2021) An ensemble filter-based heuristic approach for cancerous gene expression classification. Knowl-Based Syst 234:107560. https:\/\/doi.org\/10.1016\/j.knosys.2021.107560","journal-title":"Knowl-Based Syst"},{"key":"6578_CR29","doi-asserted-by":"crossref","unstructured":"Wang Y, Huang Z, McCane B, Neo P (2018) EmotioNet: a 3-D convolutional neural network for EEG-based emotion recognition. In 2018 international joint conference on neural networks (IJCNN), pp 1\u20137. ISSN: 2161-4407","DOI":"10.1109\/IJCNN.2018.8489715"},{"key":"6578_CR30","doi-asserted-by":"crossref","unstructured":"Wu X, Zheng WL, Lu BL (2020) Investigating EEG-based functional connectivity patterns for multimodal emotion recognition. arXiv:2004.01973 [cs]","DOI":"10.1109\/NER.2019.8717035"},{"key":"6578_CR31","doi-asserted-by":"crossref","unstructured":"Xiao G, Ye M, Xu B, Chen Z, Ren Quansheng (2021) 4D attention-based neural network for EEG emotion recognition. arXiv:2101.05484 [cs]","DOI":"10.1007\/s11571-021-09751-5"},{"key":"6578_CR32","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/978-3-030-04239-4_39","volume-title":"Neural information processing. Lecture notes in computer science","author":"Y Yang","year":"2018","unstructured":"Yang Y, Wu Q, Fu Y, Chen X (2018) Continuous convolutional neural network with 3D input for EEG-based emotion recognition. In: Cheng L, Leung ACS, Ozawa S (eds) Neural information processing. Lecture notes in computer science. Springer International Publishing, Cham, pp 433\u2013443"},{"key":"6578_CR33","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.specom.2020.03.005","volume":"120","author":"Z Yao","year":"2020","unstructured":"Yao Z, Wang Z, Liu W, Liu Y, Pan J (2020) Speech emotion recognition using fusion of three multi-task learning-based classifiers: HSF-DNN. MS-CNN and LLD-RNN. Speech Commun 120:11\u201319. https:\/\/doi.org\/10.1016\/j.specom.2020.03.005","journal-title":"Speech Commun"},{"key":"6578_CR34","doi-asserted-by":"publisher","first-page":"106954","DOI":"10.1016\/j.asoc.2020.106954","volume":"100","author":"Y Yin","year":"2021","unstructured":"Yin Y, Zheng X, Hu B, Zhang Y, Cui X (2021) EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM. Appl Soft Comput 100:106954. https:\/\/doi.org\/10.1016\/j.asoc.2020.106954","journal-title":"Appl Soft Comput"},{"key":"6578_CR35","doi-asserted-by":"crossref","unstructured":"Yuan L, Chen Y, Wang T, Yu W, Shi Y, Jiang Z, Tay FE, Feng J, Yan S (2021) Tokens-to-Token ViT: training vision transformers from scratch on imagenet. arXiv:2101.11986 [cs]","DOI":"10.1109\/ICCV48922.2021.00060"},{"issue":"5","key":"6578_CR36","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1109\/LSP.2019.2906824","journal-title":"IEEE Signal Process Lett"},{"key":"6578_CR37","unstructured":"Zhang G, Etemad A (2021) Distilling EEG Representations via Capsules for Affective Computing. arXiv: 2105.00104 [cs]"},{"key":"6578_CR38","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2021.3051332","author":"G Zhang","year":"2021","unstructured":"Zhang G, Yu M, Liu YJ, Zhao G, Zhang D, Zheng W (2021) SparseDGCNN: recognizing emotion from multichannel EEG signals. IEEE Trans Affect Comput. https:\/\/doi.org\/10.1109\/TAFFC.2021.3051332","journal-title":"IEEE Trans Affect Comput"},{"key":"6578_CR39","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), pp 1\u20136. ISSN: 1945-788X","DOI":"10.1109\/ICME.2014.6890166"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-06578-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-021-06578-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-06578-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T17:17:20Z","timestamp":1664990240000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-021-06578-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,14]]},"references-count":39,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["6578"],"URL":"https:\/\/doi.org\/10.1007\/s00500-021-06578-4","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"type":"print","value":"1432-7643"},{"type":"electronic","value":"1433-7479"}],"subject":[],"published":{"date-parts":[[2022,1,14]]},"assertion":[{"value":"11 November 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2022","order":2,"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":"Human and animal rights"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}