{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T00:00:03Z","timestamp":1780790403084,"version":"3.54.1"},"reference-count":67,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"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":["Neural Networks"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.neunet.2026.109105","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T06:41:42Z","timestamp":1780382502000},"page":"109105","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Efficient FPGA accelerator for low-power high-speed BCI motor imagery classification using novel deep learning"],"prefix":"10.1016","volume":"203","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2505-1317","authenticated-orcid":false,"given":"Saravanakumar","family":"C","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0841-8351","authenticated-orcid":false,"given":"Srinivasan","family":"C","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0123-017X","authenticated-orcid":false,"given":"Immaculate Joy","family":"S","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neunet.2026.109105_bib0001","article-title":"Assessing robustness to adversarial attacks in attention-based networks: Case of EEG-based motor imagery classification","author":"Aissa","year":"2024","journal-title":"SLAS Technology"},{"issue":"1","key":"10.1016\/j.neunet.2026.109105_bib0002","doi-asserted-by":"crossref","first-page":"22","DOI":"10.3390\/bios12010022","article-title":"A multibranch of convolutional neural network models for electroencephalogram-based motor imagery classification","volume":"12","author":"Altuwaijri","year":"2022","journal-title":"Biosensors"},{"issue":"7","key":"10.1016\/j.neunet.2026.109105_bib0003","doi-asserted-by":"crossref","first-page":"323","DOI":"10.3390\/bioengineering9070323","article-title":"Electroencephalogram-based motor imagery signals classification using a multi-branch convolutional neural network model with attention blocks","volume":"9","author":"Altuwaijri","year":"2022","journal-title":"Bioengineering"},{"key":"10.1016\/j.neunet.2026.109105_bib0004","doi-asserted-by":"crossref","first-page":"143116","DOI":"10.1109\/ACCESS.2023.3341419","article-title":"EEG signal processing for medical diagnosis, healthcare, and monitoring: A comprehensive review","volume":"11","author":"Amer","year":"2023","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.neunet.2026.109105_bib0005","first-page":"525","article-title":"BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients","volume":"16","author":"Casey","year":"2021","journal-title":"Disability and Rehabilitation: Assistive Technology"},{"issue":"6","key":"10.1016\/j.neunet.2026.109105_bib0006","doi-asserted-by":"crossref","first-page":"927","DOI":"10.3390\/mi13060927","article-title":"Motor imagery EEG classification based on transfer learning and multi-scale convolution network","volume":"13","author":"Chang","year":"2022","journal-title":"Micromachines"},{"issue":"14","key":"10.1016\/j.neunet.2026.109105_bib0007","doi-asserted-by":"crossref","first-page":"4646","DOI":"10.3390\/s21144646","article-title":"Classification of motor imagery electroencephalography signals based on image processing method","volume":"21","author":"Chen","year":"2021","journal-title":"Sensors"},{"issue":"7","key":"10.1016\/j.neunet.2026.109105_bib0008","doi-asserted-by":"crossref","DOI":"10.1093\/gigascience\/gix034","article-title":"EEG datasets for motor imagery brain\u2013computer interface","volume":"6","author":"Cho","year":"2017","journal-title":"GigaScience"},{"issue":"18","key":"10.1016\/j.neunet.2026.109105_bib0009","doi-asserted-by":"crossref","first-page":"7908","DOI":"10.3390\/s23187908","article-title":"Enhancing cross-subject motor imagery classification in EEG-based brain\u2013computer interfaces by using multi-branch CNN","volume":"23","author":"Chowdhury","year":"2023","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.neunet.2026.109105_bib0010","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-42790-y","article-title":"Motor imagery classification using sparse representations: An exploratory study","volume":"13","author":"de Menezes","year":"2023","journal-title":"Scientific Reports"},{"key":"10.1016\/j.neunet.2026.109105_bib0011","series-title":"International conference on human-computer interaction","first-page":"404","article-title":"Research on brain-computer interfaces in the entertainment field","author":"de Queiroz Cavalcanti","year":"2023"},{"issue":"4","key":"10.1016\/j.neunet.2026.109105_bib0012","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1109\/TNSRE.2018.2797547","article-title":"Cognitive behavior classification from scalp EEG signals","volume":"26","author":"Dvorak","year":"2018","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"key":"10.1016\/j.neunet.2026.109105_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109924","article-title":"Supervised penalty-based aggregation applied to motor-imagery based brain-computer-interface","volume":"145","author":"Fumanal-Idocin","year":"2024","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2026.109105_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.111129","article-title":"A multiscale feature fusion network based on attention mechanism for motor imagery EEG decoding","volume":"151","author":"Gao","year":"2024","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.neunet.2026.109105_bib0015","series-title":"2020 42nd Annual international conference of the IEEE engineering in medicine & biology society (EMBC)","first-page":"316","article-title":"A novel approach for atrial fibrillation signal identification based on temporal attention mechanism","author":"Gao","year":"2020"},{"key":"10.1016\/j.neunet.2026.109105_bib0016","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition workshops","first-page":"1638","article-title":"SqueezeNext: Hardware-aware neural network design","author":"Gholami","year":"2018"},{"issue":"23","key":"10.1016\/j.neunet.2026.109105_bib0017","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"10.1016\/j.neunet.2026.109105_bib0018","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1007\/s11517-016-1454-4","article-title":"Neurofeedback training with a motor imagery-based BCI: Neurocognitive improvements and EEG changes in the elderly","volume":"54","author":"Gomez-Pilar","year":"2016","journal-title":"Medical & Biological Engineering & Computing"},{"issue":"3","key":"10.1016\/j.neunet.2026.109105_bib0019","first-page":"31","article-title":"Exoskeleton control system based on motor-imaginary brain\u2013computer interface","volume":"9","author":"Gordleeva","year":"2017","journal-title":"Opera Medica et Physiologica"},{"issue":"2","key":"10.1016\/j.neunet.2026.109105_bib0020","doi-asserted-by":"crossref","first-page":"124","DOI":"10.3390\/brainsci15020124","article-title":"CLTNet: A hybrid deep learning model for motor imagery classification","volume":"15","author":"Gu","year":"2025","journal-title":"Brain Sciences"},{"issue":"1","key":"10.1016\/j.neunet.2026.109105_bib0021","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1080\/1750984X.2021.2007539","article-title":"From simulation to motor execution: A review of the impact of dynamic motor imagery on performance","volume":"17","author":"Guillot","year":"2024","journal-title":"International Review of Sport and Exercise Psychology"},{"key":"10.1016\/j.neunet.2026.109105_bib0022","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.neunet.2026.109105_bib0023","unstructured":"Howard A.G., Mobilenets: Efficient convolutional neural networks for mobile vision applications, (2017) arXiv: 1704.04861."},{"key":"10.1016\/j.neunet.2026.109105_bib0024","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1109\/TNSRE.2023.3249831","article-title":"A cross-space CNN with customized characteristics for motor imagery EEG classification","volume":"31","author":"Hu","year":"2023","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"key":"10.1016\/j.neunet.2026.109105_bib0025","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"4700","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.neunet.2026.109105_bib0026","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2021.774857","article-title":"Electroencephalogram-based motor imagery classification using deep residual convolutional networks","volume":"15","author":"Huang","year":"2021","journal-title":"Frontiers in Neuroscience"},{"key":"10.1016\/j.neunet.2026.109105_bib0027","unstructured":"Iandola F.N., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5\u202fMB model size, (2016) arXiv: 1602.07360."},{"issue":"4","key":"10.1016\/j.neunet.2026.109105_bib0028","doi-asserted-by":"crossref","first-page":"4233","DOI":"10.3390\/agriengineering6040238","article-title":"Tomato fungal diagnosis using few-shot learning based on deep feature extraction and cosine similarity","volume":"6","author":"Javidan","year":"2024","journal-title":"AgriEngineering"},{"key":"10.1016\/j.neunet.2026.109105_bib0029","article-title":"Two-phase multitask autoencoder-based deep learning framework for subject-independent EEG motor imagery classification","author":"Jin","year":"2024","journal-title":"IEEE Access"},{"issue":"12","key":"10.1016\/j.neunet.2026.109105_bib0030","doi-asserted-by":"crossref","first-page":"5807","DOI":"10.3390\/app12125807","article-title":"Multi-classification of motor imagery EEG signals using Bayesian optimization-based average ensemble approach","volume":"12","author":"Kamhi","year":"2022","journal-title":"Applied Sciences"},{"key":"10.1016\/j.neunet.2026.109105_bib0031","series-title":"Proceedings of the 2020 ACM\/SIGDA international symposium on field-programmable gate arrays","first-page":"173","article-title":"Xilinx vitis unified software platform","author":"Kathail","year":"2020"},{"key":"10.1016\/j.neunet.2026.109105_bib0032","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/978-1-4842-6168-2_7","article-title":"SqueezeNet","author":"Koonce","year":"2021","journal-title":"Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization"},{"key":"10.1016\/j.neunet.2026.109105_bib0033","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"2012","author":"Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109105_bib0034","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1109\/TNSRE.2023.3259730","article-title":"MDTL: An novel and model-agnostic transfer learning strategy for cross-subject motor imagery BCI","volume":"31","author":"Li","year":"2023","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"issue":"9","key":"10.1016\/j.neunet.2026.109105_bib0035","doi-asserted-by":"crossref","first-page":"1588","DOI":"10.3390\/math10091588","article-title":"A domain adaptation-based method for classification of motor imagery EEG","volume":"10","author":"Li","year":"2022","journal-title":"Mathematics"},{"key":"10.1016\/j.neunet.2026.109105_bib0036","unstructured":"Lin M., Network in network, (2013) arXiv: 1312.4400."},{"issue":"11","key":"10.1016\/j.neunet.2026.109105_bib0037","doi-asserted-by":"crossref","first-page":"2576","DOI":"10.3390\/s17112576","article-title":"Feature selection for motor imagery EEG classification based on firefly algorithm and learning automata","volume":"17","author":"Liu","year":"2017","journal-title":"Sensors"},{"key":"10.1016\/j.neunet.2026.109105_bib0038","series-title":"2020 International conference on high performance big data and intelligent systems (HPBD & IS)","first-page":"1","article-title":"Improving the accuracy of SqueezeNet with negligible extra computational cost","author":"Liu","year":"2020"},{"issue":"11","key":"10.1016\/j.neunet.2026.109105_bib0039","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.3390\/electronics11111752","article-title":"Few-shot image classification: Current status and research trends","volume":"11","author":"Liu","year":"2022","journal-title":"Electronics"},{"issue":"15","key":"10.1016\/j.neunet.2026.109105_bib0040","doi-asserted-by":"crossref","first-page":"6627","DOI":"10.3390\/su16156627","article-title":"Feature extraction and classification of motor imagery EEG signals in motor imagery for sustainable brain\u2013computer interfaces","volume":"16","author":"Lu","year":"2024","journal-title":"Sustainability"},{"issue":"21","key":"10.1016\/j.neunet.2026.109105_bib0041","doi-asserted-by":"crossref","first-page":"7080","DOI":"10.3390\/s24217080","article-title":"A cross-attention-based class alignment network for cross-subject EEG classification in a heterogeneous space","volume":"24","author":"Ma","year":"2024","journal-title":"Sensors"},{"key":"10.1016\/j.neunet.2026.109105_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102582","article-title":"Efficient analysis of deep neural networks for vision via biologically-inspired receptive field angles: An in-depth survey","volume":"112","author":"Ma","year":"2024","journal-title":"Information Fusion"},{"issue":"2","key":"10.1016\/j.neunet.2026.109105_bib0043","doi-asserted-by":"crossref","first-page":"443","DOI":"10.3390\/s25020443","article-title":"A synergy of convolutional neural networks for sensor-based EEG brain\u2013computer interfaces to enhance motor imagery classification","volume":"25","author":"Mallat","year":"2025","journal-title":"Sensors"},{"key":"10.1016\/j.neunet.2026.109105_bib0044","series-title":"teleXbe (2)","article-title":"Neurofeedback video games in the rehabilitative treatment of hyperactivity and attention deficit disorder for attention enhancement and hyperactivity reduction","author":"Nardacchione","year":"2021"},{"key":"10.1016\/j.neunet.2026.109105_bib0045","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition workshops","first-page":"53","article-title":"Exploiting local features from deep networks for image retrieval","author":"Ng","year":"2015"},{"issue":"5","key":"10.1016\/j.neunet.2026.109105_bib0046","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1515\/revneuro-2013-0032","article-title":"Brain-computer interface technologies: From signal to action","volume":"24","author":"Ortiz-Rosario","year":"2013","journal-title":"Reviews in the Neurosciences"},{"key":"10.1016\/j.neunet.2026.109105_bib0047","doi-asserted-by":"crossref","first-page":"127678","DOI":"10.1109\/ACCESS.2019.2939623","article-title":"Motor imagery EEG signals classification based on mode amplitude and frequency components using empirical wavelet transform","volume":"7","author":"Sadiq","year":"2019","journal-title":"IEEE Access"},{"issue":"11","key":"10.1016\/j.neunet.2026.109105_bib0048","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":"Human Brain Mapping"},{"issue":"1","key":"10.1016\/j.neunet.2026.109105_bib0049","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-41653-w","article-title":"Deep temporal networks for EEG-based motor imagery recognition","volume":"13","author":"Sharma","year":"2023","journal-title":"Scientific Reports"},{"key":"10.1016\/j.neunet.2026.109105_bib0050","doi-asserted-by":"crossref","first-page":"7823","DOI":"10.1109\/ACCESS.2018.2890150","article-title":"FPGA-based accelerators of deep learning networks for learning and classification: A review","volume":"7","author":"Shawahna","year":"2018","journal-title":"IEEE Access"},{"issue":"21","key":"10.1016\/j.neunet.2026.109105_bib0051","doi-asserted-by":"crossref","first-page":"7453","DOI":"10.3390\/app10217453","article-title":"Electroencephalography (EEG) technology applications and available devices","volume":"10","author":"Soufineyestani","year":"2020","journal-title":"Applied Sciences"},{"issue":"17","key":"10.1016\/j.neunet.2026.109105_bib0052","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e37343","article-title":"Improving inter-session performance via relevant session-transfer for multi-session motor imagery classification","volume":"10","author":"Sung","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.neunet.2026.109105_bib0053","series-title":"Proceedings of the 36th International Conference on Machine Learning, Proceedings of Machine Learning Research","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume":"97","author":"Tan","year":"2019"},{"key":"10.1016\/j.neunet.2026.109105_bib0054","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12938-018-0545-x","article-title":"Towards BCI-actuated smart wheelchair system","volume":"17","author":"Tang","year":"2018","journal-title":"Biomedical Engineering Online"},{"key":"10.1016\/j.neunet.2026.109105_bib0055","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3389\/fnins.2012.00055","article-title":"Review of the BCI competition IV","volume":"6","author":"Tangermann","year":"2012","journal-title":"Frontiers in Neuroscience"},{"key":"10.1016\/j.neunet.2026.109105_bib0056","doi-asserted-by":"crossref","DOI":"10.1016\/j.prime.2024.100451","article-title":"Decoding brain signals: A convolutional neural network approach for motor imagery classification","volume":"7","author":"Tarahi","year":"2024","journal-title":"e-Prime-Advances in Electrical Engineering, Electronics and Energy"},{"key":"10.1016\/j.neunet.2026.109105_bib0057","doi-asserted-by":"crossref","DOI":"10.1016\/j.mlwa.2022.100399","article-title":"An improved SqueezeNet model for the diagnosis of lung cancer in CT scans","volume":"10","author":"Tsivgoulis","year":"2022","journal-title":"Machine Learning with Applications"},{"issue":"9","key":"10.1016\/j.neunet.2026.109105_bib0058","doi-asserted-by":"crossref","first-page":"3331","DOI":"10.3390\/s22093331","article-title":"Past, present, and future of EEG-based BCI applications","volume":"22","author":"V\u00e4rbu","year":"2022","journal-title":"Sensors"},{"issue":"3","key":"10.1016\/j.neunet.2026.109105_bib0059","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1504\/IJBET.2020.111471","article-title":"A review of non-invasive BCI devices","volume":"34","author":"Veena","year":"2020","journal-title":"International Journal of Biomedical Engineering and Technology"},{"key":"10.1016\/j.neunet.2026.109105_bib0060","article-title":"MSFNet: A multi-scale space-time frequency fusion network for motor imagery EEG classification","author":"Wang","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.neunet.2026.109105_bib0061","unstructured":"XDC, DPUCZDX8G for Zynq UltraScale+ MPSoCs product guide (PG338), Available online: https:\/\/www.xilinx.com\/support\/documentation\/ip_documentation\/dpu\/v33\/pg338-dpu.pdf (accessed on 14 October 2024), (2024)."},{"issue":"4","key":"10.1016\/j.neunet.2026.109105_bib0062","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.3390\/s23041932","article-title":"Classification of motor imagery EEG signals based on data augmentation and convolutional neural networks","volume":"23","author":"Xie","year":"2023","journal-title":"Sensors"},{"issue":"9","key":"10.1016\/j.neunet.2026.109105_bib0063","doi-asserted-by":"crossref","first-page":"5745","DOI":"10.1097\/JS9.0000000000002022","article-title":"Brain\u2013computer interfaces: The innovative key to unlocking neurological conditions","volume":"110","author":"Zhang","year":"2024","journal-title":"International Journal of Surgery"},{"key":"10.1016\/j.neunet.2026.109105_bib0064","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"6848","article-title":"ShuffleNet: An extremely efficient convolutional neural network for mobile devices","author":"Zhang","year":"2018"},{"key":"10.1016\/j.neunet.2026.109105_bib0065","doi-asserted-by":"crossref","DOI":"10.1109\/TNSRE.2023.3323325","article-title":"A multi-domain convolutional neural network for EEG-based motor imagery decoding","author":"Zhi","year":"2023","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"key":"10.1016\/j.neunet.2026.109105_bib0066","doi-asserted-by":"crossref","first-page":"79731","DOI":"10.1109\/ACCESS.2024.3410036","article-title":"Multi-scale convolutional attention and Riemannian geometry network for EEG-based motor imagery classification","volume":"12","author":"Zhou","year":"2024","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.neunet.2026.109105_bib0067","doi-asserted-by":"crossref","first-page":"50","DOI":"10.3390\/brainsci15010050","article-title":"Motor imagery EEG classification based on multi-domain feature rotation and stacking ensemble","volume":"15","author":"Zhu","year":"2025","journal-title":"Brain Sciences"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026005654?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026005654?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T23:11:41Z","timestamp":1780787501000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026005654"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":67,"alternative-id":["S0893608026005654"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109105","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Efficient FPGA accelerator for low-power high-speed BCI motor imagery classification using novel deep learning","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109105","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":"109105"}}