Altaheri H, Muhammad G, Alsulaiman M (2023) Physics-informed attention temporal convolutional network for EEG-based motor imagery classification. IEEE Trans Ind Inform 19(2):2249–2258. https://doi.org/10.1109/TII.2022.3197419

Article  Google Scholar 

Altaheri H, Muhammad G, Alsulaiman M et al (2023) Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review. Neural Comput Appl 35(20):14681–14722. https://doi.org/10.1007/s00521-021-06352-5

Article  Google Scholar 

Ang KK, Chin ZY, Wang C, Guan C, Zhang H (2012) Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci 6:39. https://doi.org/10.3389/fnins.2012.00039

Article  PubMed  PubMed Central  Google Scholar 

Ang, K. K., Chin, Z. Y., Zhang, H., & Guan, C. (2008, June). Filter bank common spatial pattern (FBCSP) in brain-computer interface. In 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence) (pp. 2390–2397). IEEE. https://doi.org/10.1109/IJCNN.2008.4634130.

Brunner C, Lee R, Müller-Putz G, Schlögl A, Pfurtscheller G (2008) BCI Competition 2008–Graz data set A. Institute for Knowledge Discovery Laboratory of Brain-Computer Interfaces Graz University of Technology 16(1–6):34

Google Scholar 

Faller J, Müller-Putz G, Schmalstieg D, Pfurtscheller G (2010) An application framework for controlling an avatar in a desktop-based virtual environment via a software SSVEP brain–computer interface. Presence Teleoperators Virtual Environ 19(1):25–34. https://doi.org/10.1162/pres.19.1.25

Article  Google Scholar 

Guo MH, Xu TX, Liu JJ et al (2022) Attention mechanisms in computer vision: a survey. Comput vis Media 8(3):331–368. https://doi.org/10.1007/s41095-022-0271-y

Article  Google Scholar 

Hu J, Shen L and Sun G (2018) Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp 7132–7141)

Ingolfsson TM, Hersche M, Wang X, et al (2020) EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain–Machine Interfaces. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 2958–2965). IEEE.

Khademi Z, Ebrahimi F, Kordy HM (2023) A review of critical challenges in MI-BCI: from conventional to deep learning methods. J Neurosci Methods 383:109736. https://doi.org/10.1016/j.jneumeth.2022.109736

Article  PubMed  Google Scholar 

Lawhern VJ, Solon AJ, Waytowich NR et al (2018) EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J Neural Eng 15(5):056013. https://doi.org/10.1088/1741-2552/aace8c

Article  PubMed  Google Scholar 

Lee M;Kwon O;Kim Y;Kim H;Lee Y;Williamson J;Fazli S;Lee S (2019): Supporting data for "EEG Dataset and OpenBMI Toolbox for Three BCI Paradigms: An Investigation into BCI Illiteracy"GigaScience Database. https://doi.org/10.5524/100542

Leeb R, Brunner C, Muller-Putz G, Schlögl A, Pfurtscheller G (2008) BCI Competition 2008–Graz data set B. Graz University of Technology, Austria 16:1–6

Google Scholar 

Li Y, Guo L, Liu Y, Liu J, Meng F (2021) A temporal-spectral-based squeeze-and-excitation feature fusion network for motor imagery EEG decoding. IEEE Trans Neural Syst Rehabil Eng 29:1534–1545. https://doi.org/10.1109/TNSRE.2021.3099908

Article  PubMed  Google Scholar 

Li H, Ding M, Zhang R, Xiu C (2022) Motor imagery EEG classification algorithm based on CNN-LSTM feature fusion network. Biomed Signal Process Control 72:103342. https://doi.org/10.1016/j.bspc.2021.103342

Article  Google Scholar 

Liu K, Yang T, Yu Z et al (2024) MSVTNet: multi-scale vision transformer neural network for EEG-based motor imagery decoding. IEEE J Biomed Health Inform 28(12):7126–7137. https://doi.org/10.1109/JBHI.2024.3450753

Article  PubMed  Google Scholar 

Ma X, Chen W, Pei Z, Liu J, Huang B, Chen J (2023) A temporal dependency learning CNN with attention mechanism for MI-EEG decoding. IEEE Trans Neural Syst Rehabil Eng 31:3188–3200. https://doi.org/10.1109/TNSRE.2023.3299355

Article  PubMed  Google Scholar 

Mattia D, Cincotti F, Astolfi L et al (2015) Brain–computer interface boosts motor imagery practice during stroke recovery. Ann Neurol 77(5):851–865. https://doi.org/10.1002/ana.24390

Article  PubMed  Google Scholar 

Pfurtscheller G, Neuper C (1997) Motor imagery activates primary sensorimotor area in humans. Neurosci Lett 239(2–3):65–68. https://doi.org/10.1016/S0304-394(97)00889-6

Article  CAS  PubMed  Google Scholar 

Qin Z, Zhang P, Wu F and Li X (2021) FCANet: frequency channel attention networks. I In Proceedings of the IEEE/CVF international conference on computer vision (pp. 783–792). https://doi.org/10.1109/ICCV48922.2021.00082

Ramoser H, Muller-Gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 8(4):441–446. https://doi.org/10.1109/86.895946

Article  CAS  PubMed  Google Scholar 

Roy S, Chowdhury A, McCreadie K, Prasad G (2020) Deep learning based inter-subject continuous decoding of motor imagery for practical brain-computer interfaces. Front Neurosci 14:918. https://doi.org/10.3389/fnins.2020.00918

Article  PubMed  PubMed Central  Google Scholar 

Schirrmeister RT, Springenberg JT, Fiederer LDJ et al (2017) Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp 38(11):5391–5420. https://doi.org/10.1002/hbm.23730

Article  PubMed  PubMed Central  Google Scholar 

Sharma N, Upadhyay A, Sharma M, Singhal A (2023) Deep temporal networks for EEG-based motor imagery recognition. Sci Rep 13(1):18813. https://doi.org/10.1038/s41598-023-41653-w

Article  CAS  PubMed  PubMed Central  Google Scholar 

Song Y, Zheng Q, Liu B, Gao X (2023) EEG conformer: convolutional transformer for EEG decoding and visualization. IEEE Trans Neural Syst Rehabil Eng 31:710–719. https://doi.org/10.1109/TNSRE.2022.3230250

Article  PubMed  Google Scholar 

Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Advances in neural information processing systems, 30. http://arxiv.org/abs/1706.03762

Wang P, Jiang A, Liu X, Shang J, Zhang L (2018) LSTM-based EEG classification in motor imagery tasks. IEEE Trans Neural Syst Rehabil Eng 26(11):2086–2095. https://doi.org/10.1109/TNSRE.2018.2876129

Article  PubMed  Google Scholar 

Wang J, Ye W, He J et al (2026) Integrating biological and machine intelligence: attention mechanisms in brain–computer interfaces. Inf Fusion 125:103417. https://doi.org/10.1016/j.inffus.2025.103417

Article  Google Scholar 

Wang Q, Wu B, Zhu P, et al (2020) ECA-Net: Efficient channel attention for deep convolutional neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11534–11542). https://doi.org/10.1109/CVPR42600.2020.01155

Wimpff M, Gizzi L, Zerfowski J, Yang B (2024) EEG motor imagery decoding: a framework for comparative analysis with channel attention mechanisms. J Neural Eng 21(3):036020. https://doi.org/10.1088/1741-2552/ad48b9

Article  Google Scholar 

Woo S, Park J, Lee JY and Kweon IS (2018) Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV) (pp. 3–19). https://doi.org/10.1007/978-3-030-01234-2_1

Wu Z, Wang M, Sun W et al (2023) CAT: learning to collaborate channel and spatial attention from multi-information fusion. IET Comput vis 17:309–318. https://doi.org/10.1049/cvi2.12166

Article  Google Scholar 

Xie J, Zhang J, Sun J et al (2022) A transformer-based approach combining deep learning network and spatial-temporal information for raw EEG classification. IEEE Trans Neural Syst Rehabil Eng 30:2126–2136. https://doi.org/10.1109/TNSRE.2022.3194600

Article  PubMed  Google Scholar 

Yu Y, Liu Y, Jiang J, Yin E, Zhou Z, Hu D (2018) An asynchronous control paradigm based on sequential motor imagery and its application in wheelchair navigation. IEEE Trans Neural Syst Rehabil Eng 26(12):2367–2375. https://doi.org/10.1109/TNSRE.2018.2881215

Article  PubMed  Google Scholar 

Yu Y, Liu Y, Yin E, Jiang J, Zhou Z, Hu D (2019) An asynchronous hybrid spelling approach based on EEG–EOG signals for Chinese character input. IEEE Trans Neural Syst Rehabil Eng 26(12):2367–2375. https://doi.org/10.1109/TNSRE.2018.2881215

Article  Google Scholar 

Zhang Y, Zhou G, Jin J, Wang X, Cichocki A (2015) Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface. J Neurosci Methods 255:85–91. https://doi.org/10.1016/j.jneumeth.2015.08.004

Article  CAS  PubMed  Google Scholar 

Zhang H, Dana K, Shi J, et al (2018) Context encoding for semantic segmentation. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (pp. 7151–7160). https://doi.org/10.1109/CVPR.2018.00747

Zhao W, Zhang B, Zhou H, Wei D, Huang C, Lan Q (2025) Multi-scale convolutional transformer network for motor imagery brain-computer interface. Sci Rep 15(1):12935. https://doi.org/10.1038/s41598-025-96611-5

Article  PubMed  PubMed Central  Google Scholar 

Zhu J, Chen X, He K, LeCun Y, and Liu Z (2025) Transformers without normalization. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 14901–14911). https://arxiv.org/abs/2503.10622