{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T15:15:29Z","timestamp":1777130129324,"version":"3.51.4"},"reference-count":27,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electrical Engineering"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.compeleceng.2026.110990","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T23:42:25Z","timestamp":1770162145000},"page":"110990","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["A novel EEG motor imagery classification model using feature fusion of temporal convolution and attention"],"prefix":"10.1016","volume":"132","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3175-4708","authenticated-orcid":false,"given":"Mohammad","family":"Bdaqli","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5023-0961","authenticated-orcid":false,"given":"Saeed","family":"Meshgini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reza","family":"Afrouzian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"13","key":"10.1016\/j.compeleceng.2026.110990_bib0001","doi-asserted-by":"crossref","first-page":"6001","DOI":"10.3390\/s23136001","article-title":"State-of-the-art on brain-computer interface technology","volume":"23","author":"Peksa","year":"2023","journal-title":"Sensors"},{"issue":"7398","key":"10.1016\/j.compeleceng.2026.110990_bib0002","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1038\/nature11076","article-title":"Reach and grasp by people with tetraplegia using a neurally controlled robotic arm","volume":"485","author":"Hochberg","year":"2012","journal-title":"Nature"},{"issue":"13","key":"10.1016\/j.compeleceng.2026.110990_bib0003","doi-asserted-by":"crossref","first-page":"3620","DOI":"10.3390\/s20133620","article-title":"Brain-computer interface-based humanoid control: A review","volume":"20","author":"Chamola","year":"2020","journal-title":"Sensors"},{"issue":"22","key":"10.1016\/j.compeleceng.2026.110990_bib0004","doi-asserted-by":"crossref","first-page":"7125","DOI":"10.3390\/s24227125","article-title":"Electroencephalography-based brain-computer interfaces in rehabilitation: A bibliometric analysis (2013\u20132023)","volume":"24","author":"Angulo Medina","year":"2024","journal-title":"Sensors"},{"key":"10.1016\/j.compeleceng.2026.110990_bib0005","doi-asserted-by":"crossref","unstructured":"Tatti, E. and A. Cacciola, The role of brain oscillatory activity in human sensorimotor control and learning: bridging theory and practice. 2023, Frontiers Media SA. p. 1211763.","DOI":"10.3389\/fnsys.2023.1211763"},{"key":"10.1016\/j.compeleceng.2026.110990_bib0006","doi-asserted-by":"crossref","DOI":"10.3389\/fnhum.2024.1421922","article-title":"Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions","volume":"18","author":"Ahuja","year":"2024","journal-title":"Front Hum Neurosci"},{"issue":"5","key":"10.1016\/j.compeleceng.2026.110990_bib0007","doi-asserted-by":"crossref","first-page":"2798","DOI":"10.3390\/s23052798","article-title":"EEG-based BCIs on motor imagery paradigm using wearable technologies: a systematic review","volume":"23","author":"Saibene","year":"2023","journal-title":"Sensors"},{"issue":"7","key":"10.1016\/j.compeleceng.2026.110990_bib0008","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0268880","article-title":"Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users","volume":"17","author":"Tibrewal","year":"2022","journal-title":"Plos One"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.110990_bib0009","doi-asserted-by":"crossref","first-page":"269","DOI":"10.11591\/eei.v8i1.1402","article-title":"Motor imagery classification in brain computer interface (BCI) based on EEG signal by using machine learning technique","volume":"8","author":"Isa","year":"2019","journal-title":"Bull Electr Eng Inform"},{"issue":"9","key":"10.1016\/j.compeleceng.2026.110990_bib0010","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0074433","article-title":"Z-score linear discriminant analysis for EEG based brain-computer interfaces","volume":"8","author":"Zhang","year":"2013","journal-title":"PloS one"},{"key":"10.1016\/j.compeleceng.2026.110990_bib0011","series-title":"International conference on robot intelligence technology and applications","article-title":"The classification of EEG signal using different machine learning techniques for BCI application","author":"Rashid","year":"2018"},{"issue":"4","key":"10.1016\/j.compeleceng.2026.110990_bib0012","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s42452-020-2378-z","article-title":"Convolutional neural network based features for motor imagery EEG signals classification in brain\u2013computer interface system","volume":"2","author":"Taheri","year":"2020","journal-title":"SN Appl Sci"},{"key":"10.1016\/j.compeleceng.2026.110990_bib0013","series-title":"2021 International Wireless Communications and Mobile Computing (IWCMC)","article-title":"A novel ensemble learning approach for classification of EEG motor imagery signals","author":"Echtioui","year":"2021"},{"issue":"7553","key":"10.1016\/j.compeleceng.2026.110990_bib0014","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"5","key":"10.1016\/j.compeleceng.2026.110990_bib0015","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.compeleceng.2026.110990_bib0016","unstructured":"Dong, J., M. Komosar, J. Vorwerk, D. Baumgarten, and J. Haueisen, Scatter-based common spatial patterns\u2013a unified spatial filtering framework. arXiv preprint arXiv:2303.06019, 2023."},{"key":"10.1016\/j.compeleceng.2026.110990_bib0017","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.compbiomed.2018.09.021","article-title":"An adaptive CSP filter to investigate user independence in a 3-class MI-BCI paradigm","volume":"103","author":"Costa","year":"2018","journal-title":"Comput Biol Med"},{"issue":"11","key":"10.1016\/j.compeleceng.2026.110990_bib0018","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"},{"key":"10.1016\/j.compeleceng.2026.110990_bib0019","series-title":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","article-title":"EEG-TCNet: an accurate temporal convolutional network for embedded motor-imagery brain\u2013machine interfaces","author":"Ingolfsson","year":"2020"},{"issue":"4","key":"10.1016\/j.compeleceng.2026.110990_bib0020","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.1109\/TCDS.2023.3245042","article-title":"A novel multiscale dilated convolution neural network with gating mechanism for decoding driving intentions based on EEG","volume":"15","author":"Sun","year":"2023","journal-title":"IEEE Trans Cogn Dev Syst"},{"issue":"4","key":"10.1016\/j.compeleceng.2026.110990_bib0021","doi-asserted-by":"crossref","first-page":"995","DOI":"10.3390\/diagnostics12040995","article-title":"A multi-branch convolutional neural network with squeeze-and-excitation attention blocks for EEG-based motor imagery signals classification","volume":"12","author":"Altuwaijri","year":"2022","journal-title":"Diagnostics"},{"issue":"2","key":"10.1016\/j.compeleceng.2026.110990_bib0022","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.compeleceng.2026.110990_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102826","article-title":"Electroencephalography-based motor imagery classification using temporal convolutional network fusion","volume":"69","author":"Musallam","year":"2021","journal-title":"Biomed Signal Process Control"},{"issue":"18","key":"10.1016\/j.compeleceng.2026.110990_bib0024","doi-asserted-by":"crossref","first-page":"13109","DOI":"10.1007\/s00521-021-05958-z","article-title":"Parallel spatio-temporal attention-based TCN for multivariate time series prediction","volume":"35","author":"Fan","year":"2023","journal-title":"Neural Comput Appl"},{"key":"10.1016\/j.compeleceng.2026.110990_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107751","article-title":"Temporal convolutional autoencoder for unsupervised anomaly detection in time series","volume":"112","author":"Thill","year":"2021","journal-title":"Appl Soft Comput"},{"issue":"3","key":"10.1016\/j.compeleceng.2026.110990_bib0026","first-page":"1210","article-title":"Dilate-invariant temporal convolutional network for real-time edge applications","volume":"69","author":"Ibrahim","year":"2021","journal-title":"IEEE Trans Circuits Syst I: Regul Pap"},{"key":"10.1016\/j.compeleceng.2026.110990_bib0027","doi-asserted-by":"crossref","unstructured":"Hu, J., L. Shen, and G. Sun. Squeeze-and-excitation networks. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.","DOI":"10.1109\/CVPR.2018.00745"}],"container-title":["Computers and Electrical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0045790626000625?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0045790626000625?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T14:11:39Z","timestamp":1771423899000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0045790626000625"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":27,"alternative-id":["S0045790626000625"],"URL":"https:\/\/doi.org\/10.1016\/j.compeleceng.2026.110990","relation":{},"ISSN":["0045-7906"],"issn-type":[{"value":"0045-7906","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A novel EEG motor imagery classification model using feature fusion of temporal convolution and attention","name":"articletitle","label":"Article Title"},{"value":"Computers and Electrical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compeleceng.2026.110990","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"110990"}}