Mane R, Chouhan T, Guan C. BCI for stroke rehabilitation: motor and beyond. J Neural Eng. 2020;17:041001.

Article  Google Scholar 

Ma X, Qiu S, He H. Multi-channel EEG recording during motor imagery of different joints from the same limb. Sci Data. 2020;7:191.

Article  Google Scholar 

Yang B, Ma J, Qiu W, Zhang J, Wang X. The unilateral upper limb classification from fMRI-weighted EEG signals using convolutional neural network. Biomed Signal Process Control. 2022;78:103855.

Article  Google Scholar 

Ma J, Yang B, Qiu W, Zhang J, Yan L, Wang W. Recognizable rehabilitation movements of multiple unilateral upper limb: an fMRI study of motor execution and motor imagery. J Neurosci Methods. 2023;392:109861.

Article  Google Scholar 

Fadiyah AU, Djamal EC. Classification of motor imagery and synchronization of post-stroke patient EEG signal. In: 2019 6th international conference on electrical engineering, computer science and informatics (EECSI). IEEE; 2019. p. 28–33.

Chapter  Google Scholar 

Schlögl A, Vidaurre C, Müller K-R. Adaptive methods in BCI research-an introductory tutorial. In: Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction. Springer; 2010. p. 331–55.

Google Scholar 

Vidaurre C, Schlogl A, Cabeza R, Scherer R, Pfurtscheller G. Study of on-line adaptive discriminant analysis for EEG-based brain computer interfaces. IEEE Trans Biomed Eng. 2007;54:550–6.

Article  Google Scholar 

Hazrati MK, Erfanian A. An online EEG-based brain–computer interface for controlling hand grasp using an adaptive probabilistic neural network. Med Eng Phys. 2010;32:730–9.

Article  Google Scholar 

Hsu W-Y. EEG-based motor imagery classification using enhanced active segment selection and adaptive classifier. Comput Biol Med. 2011;41:633–9.

Article  Google Scholar 

Ramoser H, Muller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng. 2000;8:441–6.

Article  Google Scholar 

Ang KK, Chin ZY, Zhang H, Guan C. 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). IEEE; 2008. p. 2390–7.

Google Scholar 

Song X, Yoon S-C, Perera V. Adaptive common spatial pattern for single-trial EEG classification in multisubject BCI. In: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE; 2013. p. 411–4.

Chapter  Google Scholar 

Zhao Q, Zhang L, Cichocki A, Li J. Incremental common spatial pattern algorithm for BCI. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). IEEE; 2008. p. 2656–9.

Chapter  Google Scholar 

Woehrle H, Krell MM, Straube S, Kim SK, Kirchner EA, Kirchner F. An adaptive spatial filter for user-independent single trial detection of event-related potentials. IEEE Trans Biomed Eng. 2015;62:1696–705.

Article  Google Scholar 

Phan AH, Cichocki A. Tensor decompositions for feature extraction and classification of high dimensional datasets. Nonlin Theory Appl IEICE. 2010;1:37–68.

Google Scholar 

Llera A, Gómez V, Kappen HJ. Adaptive multiclass classification for brain computer interfaces. Neural Comput. 2014;26:1108–27.

Article  Google Scholar 

Vidaurre C, Kawanabe M, von Bünau P, Blankertz B, Müller K-R. Toward unsupervised adaptation of LDA for brain–computer interfaces. IEEE Trans Biomed Eng. 2010;58:587–97.

Article  Google Scholar 

Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J Neural Eng. 2018;15:056013.

Article  Google Scholar 

Schirrmeister RT, Springenberg JT, Fiederer LDJ, et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp. 2017;38:5391–420.

Article  Google Scholar 

Mane R, Robinson N, Vinod AP, Lee S-W and Guan C. A multi-view CNN with novel variance layer for motor imagery brain computer interface. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2020, pp. 2950-2953.

Altaheri H, Muhammad G, Alsulaiman M. Physics-informed attention temporal convolutional network for EEG-based motor imagery classification. IEEE Trans Ind Inf. 2022;19:2249–58.

Article  Google Scholar 

Song Y, Zheng Q, Liu B, Gao X. EEG conformer: convolutional transformer for EEG decoding and visualization. IEEE Trans Neural Syst Rehabil Eng. 2022;31:710–9.

Article  Google Scholar 

Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are features in deep neural networks? Adv Neural Inf Proces Syst. 2014;27

Sejdinovic D, Gretton A, Sriperumbudur B, Fukumizu K. Equivalence of distance-based and RKHS-based statistics in hypothesis testing. Qual Control Appl Stat. 2014;59:475–8.

Google Scholar 

Long M, Cao Y, Cao Z, Wang J, Jordan MI. Transferable representation learning with deep adaptation networks. IEEE Trans Pattern Anal Mach Intell. 2018;41:3071–85.

Article  Google Scholar 

Long M, Wang J, Ding G, Sun J and Yu PS. Transfer feature learning with joint distribution adaptation. Proceedings of the IEEE international conference on computer vision. 2013, pp. 2200-7.

Yu C, Wang J, Chen Y, Huang M, Transfer learning with dynamic adversarial adaptation network. IEEE international conference on data mining (ICDM). IEEE. 2019;2019:778–86.

Google Scholar 

Ghifary M, Kleijn WB and Zhang M. Domain adaptive neural networks for object recognition. PRICAI 2014: rrends in artificial intelligence: 13th Pacific Rim international conference on artificial intelligence, Gold Coast, QLD, Australia, December 1-5, 2014 Proceedings 13. Springer, 2014, pp. 898-904.

Tzeng E, Hoffman J, Zhang N, Saenko K and Darrell T. Deep domain confusion: maximizing for domain invariance. arXiv preprint arXiv:14123474. 2014.

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 770–8.

Google Scholar 

Sun B, Saenko K. Deep coral: correlation alignment for deep domain adaptation. In: Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14. Springer; 2016. p. 443–50.

Chapter  Google Scholar 

Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst. 2012;25

Zhu Y, Zhuang F, Wang J, et al. Multi-representation adaptation network for cross-domain image classification. Neural Netw. 2019;119:214–21.

Article  Google Scholar 

Ren C-X, Ge P, Dai D-Q, Yan H. Learning kernel for conditional moment-matching discrepancy-based image classification. IEEE Trans Cybern. 2019;51:2006–18.

Article  Google Scholar 

Gretton A, Sejdinovic D, Strathmann H, et al. Optimal kernel choice for large-scale two-sample tests. Adv Neural Inf Proces Syst. 2012;25

Zhu Y, Zhuang F, Wang J, et al. Deep subdomain adaptation network for image classification. IEEE TransNeural Networks Learn Syst. 2020;32:1713–22.

Article  MathSciNet  Google Scholar 

Zhang K, Robinson N, Lee S-W, Guan C. Adaptive transfer learning for EEG motor imagery classification with deep convolutional neural network. Neural Netw. 2021;136:1–10.

Article  Google Scholar 

Chen J, Pi D, Jiang X and Chen Y. Cross-subject domain adaptation for classifying working memory load with multi-frame EEG images. arXiv preprint arXiv:210606769. 2021.

Chen Y, Yang R, Huang M, Wang Z, Liu X. Single-source to single-target cross-subject motor imagery classification based on multisubdomain adaptation network. IEEE Trans Neural Syst Rehabil Eng. 2022;30:1992–2002.

Article  Google Scholar 

Oostenveld R, Praamstra P. The five percent electrode system for high-resolution EEG and ERP measurements. Clin Neurophysiol. 2001;112:713–9.

Article  Google Scholar 

Tangermann M, Müller KR, Aertsen A, et al. Review of the BCI competition IV. Front Neurosci. 2012;6:55.

Article  Google Scholar 

Van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008:9.

Ma J, Yang B, Rong F, Gao S, Wang W. Motor imagery decoding using source optimized transfer learning based on multi-loss fusion CNN. Cogn Neurodyn. 2024:1–14.

Liu H, Wei P, Wang H, et al. An EEG motor imagery dataset for brain computer interface in acute stroke patients. Sci Data. 2024;11:131.

Article  Google Scholar 

Ma J, Ma W, Zhang J, et al. Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients. Sci Rep. 2024;14:28170.

Article  Google Scholar