Abdulkader SN, Atia A, Mostafa M-SM (2015) Brain computer interfacing: applications and challenges. Egypt Inf J 16(2):213–230

Google Scholar 

Altaheri H, Muhammad G, Alsulaiman M (2022) Physics-informed attention temporal convolutional network for eeg-based motor imagery classification. IEEE Trans Industr Inf 19(2):2249–2258

Article  Google Scholar 

Ang KK, Chin ZY, Zhang H, Guan C (2008) 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

Autthasan P, Chaisaen R, Sudhawiyangkul T, Rangpong P, Kiatthaveephong S, Dilokthanakul N, Bhakdisongkhram G, Phan H, Guan C, Wilaiprasitporn T (2021) Min2net: end-to-end multi-task learning for subject-independent motor imagery eeg classification. IEEE Trans Biomed Eng 69(6):2105–2118

Article  Google Scholar 

Autthasan P, Chaisaen R, Phan H, Vos M, Wilaiprasitporn T (2024) Mixnet: joining force of classical and modern approaches toward the comprehensive pipeline in motor imagery eeg classification. IEEE Internet Things J 11(17):28539–28554

Article  Google Scholar 

Baig MZ, Aslam N, Shum HP (2020) Filtering techniques for channel selection in motor imagery eeg applications: a survey. Artif Intell Rev 53(2):1207–1232

Article  Google Scholar 

Brunner C, Leeb 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 Technol 16:1–6

Chaudhary U, Birbaumer N, Ramos-Murguialday A (2016) Brain-computer interfaces for communication and rehabilitation. Nat Rev Neurol 12(9):513–525

Article  PubMed  Google Scholar 

Dai M, Zheng D, Liu S, Zhang P (2018) Transfer kernel common spatial patterns for motor imagery brain-computer interface classification. Comput Math Methods Med 2018(1):9871603

PubMed  PubMed Central  Google Scholar 

Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701. https://doi.org/10.1080/01621459.1937.10503522

Article  Google Scholar 

Gan M-G, Zhang Y (2022) Temporal attention-pyramid pooling for temporal action detection. IEEE Trans Multimed 25:3799–3810

Article  Google Scholar 

Georgiadis K, Laskaris N, Nikolopoulos S, Kompatsiaris I (2019) Connectivity steered graph Fourier transform for motor imagery bci decoding. J Neural Eng 16(5):056021

Article  CAS  PubMed  Google Scholar 

Gupta A, Agrawal RK, Kirar JS, Andreu-Perez J, Ding W-P, Lin C-T, Prasad M (2019) On the utility of power spectral techniques with feature selection techniques for effective mental task classification in noninvasive bci. IEEE Trans Syst Man Cybern Syst 51(5):3080–3092

Article  Google Scholar 

Ha K-W, Jeong J-W (2020) Temporal pyramid pooling for decoding motor-imagery eeg signals. IEEE Access 9:3112–3125

Article  Google Scholar 

He H, Wu D (2019) Transfer learning for brain-computer interfaces: a Euclidean space data alignment approach. IEEE Trans Biomed Eng 67(2):399–410

Article  PubMed  Google Scholar 

He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916

Article  PubMed  Google Scholar 

Huang X, Choi K-S, Zhou N, Zhang Y, Chen B, Pedrycz W (2023) Shallow inception domain adaptation network for eeg-based motor imagery classification. IEEE Trans Cogn Dev Syst 16(2):521–533

Article  Google Scholar 

Ilyas M, Saad P, Ahmad M, Ghani A (2016) Classification of eeg signals for brain-computer interface applications: Performance comparison. In: 2016 international conference on robotics, automation and sciences (ICORAS), pp 1–4 IEEE

Iman RL, Davenport JM (1980) Approximations of the critical region of the fbietkan statistic. Commun Stat Theory Methods 9(6):571–595. https://doi.org/10.1080/03610928008827904

Article  Google Scholar 

Ingolfsson TM, Hersche M, Wang X, Kobayashi N, Cavigelli L, Benini L (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

Jeannerod M (1994) The representing brain: neural correlates of motor intention and imagery. Behav Brain Sci 17(2):187–202

Article  Google Scholar 

Jeunet C, Glize B, McGonigal A, Batail J-M, Micoulaud-Franchi J-A (2019) Using eeg-based brain computer interface and neurofeedback targeting sensorimotor rhythms to improve motor skills: theoretical background, applications and prospects. Neurophysiol Clin 49(2):125–136

Article  PubMed  Google Scholar 

Jin J, Allison BZ, Sellers EW, Brunner C, Horki P, Wang X, Neuper C (2011) Optimized stimulus presentation patterns for an event-related potential eeg-based brain-computer interface. Med Biol Eng Comput 49:181–191

Article  PubMed  Google Scholar 

Kirar JS, Agrawal R (2018) Relevant feature selection from a combination of spectral-temporal and spatial features for classification of motor imagery eeg. J Med Syst 42:1–15

Article  Google Scholar 

Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ (2018) Eegnet: a compact convolutional neural network for eeg-based brain-computer interfaces. J Neural Eng 15(5):056013

Article  PubMed  Google Scholar 

Lea C, Flynn MD, Vidal R, Reiter A, Hager GD (2017) Temporal convolutional networks for action segmentation and detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 156–165

Lee M-H, Kwon O-Y, Kim Y-J, Kim H-K, Lee Y-E, Williamson J, Fazli S, Lee S-W (2019) EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy. GigaSci 8(5):002. https://doi.org/10.1093/gigascience/giz002

Article  Google Scholar 

Leeb R, Brunner C, Müller-Putz G, Schlögl A, Pfurtscheller G (2008) Bci competition 2008-graz data set b. Graz Univ Technol Austria 16:1–6

Google Scholar 

Leuthardt EC, Schalk G, Wolpaw JR, Ojemann JG, Moran DW (2004) A brain-computer interface using electrocorticographic signals in humans. J Neural Eng 1(2):63

Article  PubMed  Google Scholar 

Li Y, Long J, Yu T, Yu Z, Wang C, Zhang H, Guan C (2010) An eeg-based bci system for 2-d cursor control by combining mu/beta rhythm and p300 potential. IEEE Trans Biomed Eng 57(10):2495–2505

Article  PubMed  Google Scholar 

Li A, Wang Z, Zhao X, Xu T, Zhou T, Hu H (2023a) Mdtl: a novel and model-agnostic transfer learning strategy for cross-subject motor imagery bci. IEEE Trans Neural Syst Rehabil Eng 31:1743–1753

Article  PubMed  Google Scholar 

Li H, Zhang D, Xie J (2023b) Mi-daban: a dual-attention-based adversarial network for motor imagery classification. Comput Biol Med 152:106420

Article  PubMed  Google Scholar 

Liu K, Yang M, Yu Z, Wang G, Wu W (2022) Fbmsnet: a filter-bank multi-scale convolutional neural network for eeg-based motor imagery decoding. IEEE Trans Biomed Eng 70(2):436–445

Article  Google Scholar 

Liu X, Xiong S, Wang X, Liang T, Wang H, Liu X (2023) A compact multi-branch 1d convolutional neural network for eeg-based motor imagery classification. Biomed Signal Process Control 81:104456

Article  Google Scholar 

Luo J, Wang Y, Xia S, Lu N, Ren X, Shi Z, Hei X (2023) A shallow mirror transformer for subject-independent motor imagery bci. Comput Biol Med 164:107254

Article  PubMed  Google Scholar 

Mak JN, Wolpaw JR (2009) Clinical applications of brain-computer interfaces: current state and future prospects. IEEE Rev Biomed Eng 2:187–199

Article  PubMed  PubMed Central  Google Scholar 

Mane R, Chew E, Chua K, Ang KK, Robinson N, Vinod AP, Lee S-W, Guan C (2021) Fbcnet: a multi-view convolutional neural network for brain-computer interface. arXiv preprint arXiv:2104.01233

Nicolas-Alonso LF, Gomez-Gil J (2012) Brain computer interfaces, a review. Sensors 12(2):1211–1279

Article  PubMed  PubMed Central  Google Scholar 

Padfield N, Zabalza J, Zhao H, Masero V, Ren J (2019) Eeg-based brain-computer interfaces using motor-imagery: techniques and challenges. Sensors 19(6):1423

Article  PubMed  PubMed Central  Google Scholar 

Park C, Looney D, Rehman N, Ahrabian A, Mandic DP (2012) Classification of motor imagery bci using multivariate empirical mode decomposition. IEEE Trans Neural Syst Rehabil Eng 21(1):10–22

Article  PubMed  Google Scholar 

Quadrianto N, Cuntai G, Dat T, Xue P (2007) Sub-band common spatial pattern (sbcsp) for brain-computer interface. international ieee. In: EMBS Conference on Neural Engineering. In: Proceedings of the 2007 3rd International IEEE/EMBS Conference on Neural Engineering, Kohala Coast, HI, USA pp 2–7

Rahman MA, Khanam F, Hossain MK, Alam MK, Mohiuddin A (2019) Four-class motor imagery eeg signal classification using pca, wavelet and two-stage neural network. Int J Adv Comput Sci Appl 10(5)

Ramadan RA, Vasilakos AV (2017) Brain computer interface: control signals review. Neurocomput 223:26–44

Article  Google Scholar 

Rodríguez-Bermúdez G, García-Laencina PJ (2012) Automatic and adaptive classification of electroencephalographic signals for brain computer interfaces. J Med Syst 36:51–63

Article  Google Scholar 

Roy AM (2022) Adaptive transfer learning-based multiscale feature fused deep convolutional neural network for eeg mi multiclassification in brain-computer interface. Eng Appl Artif Intell 116:105347

Article  Google Scholar 

Santamaria-Vazquez E, Martinez-Cagigal V, Vaquerizo-Villar F, Hornero R (2020) Eeg-inception: a novel deep convolutional neural network for assistive erp-based brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 28(12):2773–2782

Article  PubMed